Search results for: word segmentation and recognition

Authors: Pongsatorn Tantrabundit, Lersak Phothong, Ong-art Chanprasitchai

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Due to the emergence of the Internet, it has extended the traditional Word-of-Mouth (WOM) to a new form called “Electronic Word-of-Mouth (eWOM).” Unlike traditional WOM, eWOM is able to present information in various ways by applying different components. Each eWOM component generates different effects on online consumer behavior. This research investigates the effects of Webcare (responding message) from product/ service providers on negative eWOM by applying two types of products (search and experience). The proposed conceptual model was developed based on the combination of the stages in consumer decision-making process, theory of reasoned action (TRA), theory of planned behavior (TPB), the technology acceptance model (TAM), the information integration theory and the elaboration likelihood model. The methodology techniques used in this study included multivariate analysis of variance (MANOVA) and multiple regression analysis. The results suggest that Webcare does slightly increase Thai online consumer’s perceptions on perceived eWOM trustworthiness, information diagnosticity and quality. For negative eWOM, we also found that perceived eWOM Trustworthiness, perceived eWOM diagnosticity and quality have a positive relationship with eWOM influence whereas perceived valence has a negative relationship with eWOM influence in Thai online consumers.

Keywords: consumer behavior, electronic word-of-mouth, online review, online word-of-mouth, Thai online consumer, webcare

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Authors: Aishik Deb, Rituparna Sinha

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We have developed an algorithm to detect the abnormal segment/"structural variation in the genome across a number of samples. We have worked on simulated as well as real data from the BAM Files and have designed a segmentation algorithm where abnormal segments are detected. This algorithm aims to improve the accuracy and performance of the existing CNV-CH algorithm. The next-generation sequencing (NGS) approach is very fast and can generate large sequences in a reasonable time. So the huge volume of sequence information gives rise to the need for Big Data and parallel approaches of segmentation. Therefore, we have designed a map-reduce approach for the existing CNV-CH algorithm where a large amount of sequence data can be segmented and structural variations in the human genome can be detected. We have compared the efficiency of the traditional and map-reduce algorithms with respect to precision, sensitivity, and F-Score. The advantages of using our algorithm are that it is fast and has better accuracy. This algorithm can be applied to detect structural variations within a genome, which in turn can be used to detect various genetic disorders such as cancer, etc. The defects may be caused by new mutations or changes to the DNA and generally result in abnormally high or low base coverage and quantification values.

Keywords: cancer detection, convex hull segmentation, map reduce, next generation sequencing

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Authors: Yousif Mohamed Y. Abdallah, Mommen A. Alkhir, Amel S. Algaddal

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This was experimental study conducted to study segmentation of brain in MRI images using edge detection and morphology filters. For brain MRI images each film scanned using digitizer scanner then treated by using image processing program (MatLab), where the segmentation was studied. The scanned image was saved in a TIFF file format to preserve the quality of the image. Brain tissue can be easily detected in MRI image if the object has sufficient contrast from the background. We use edge detection and basic morphology tools to detect a brain. The segmentation of MRI images steps using detection and morphology filters were image reading, detection entire brain, dilation of the image, filling interior gaps inside the image, removal connected objects on borders and smoothen the object (brain). The results of this study were that it showed an alternate method for displaying the segmented object would be to place an outline around the segmented brain. Those filters approaches can help in removal of unwanted background information and increase diagnostic information of Brain MRI.

Keywords: improvement, brain, matlab, markers, boundaries

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Authors: Frederik Calsius, Mirela Popa, Alexia Briassouli

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In the field of machine learning, it is common practice to use benchmark datasets to prove the working of a method. The domain of action recognition in videos often uses datasets like Kinet-ics, Something-Something, UCF-101 and HMDB-51 to report results. Considering the properties of the datasets, there are no datasets that focus solely on very short clips (2 to 3 seconds), and on highly-similar fine-grained actions within one specific domain. This paper researches how current state-of-the-art action recognition methods perform on a dataset that consists of highly similar, fine-grained actions. To do so, a dataset of skateboarding tricks was created. The performed analysis highlights both benefits and limitations of state-of-the-art methods, while proposing future research directions in the activity recognition domain. The conducted research shows that the best results are obtained by fusing RGB data with OpenPose data for the Temporal Shift Module.

Keywords: activity recognition, fused deep representations, fine-grained dataset, temporal modeling

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Authors: Sayor Ajfar Aaron, Mushfiqur Rahman, Sajjat Hossain Abir, Ashif Newaz

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This study delves into the development of an artificial intelligence application designed for real-time emotion recognition from human vocal patterns. Utilizing advanced machine learning algorithms, including deep learning and neural networks, the paper highlights both the technical challenges and potential opportunities in accurately interpreting emotional cues from speech. Key findings demonstrate the critical role of diverse training datasets and the impact of ambient noise on recognition accuracy, offering insights into future directions for improving robustness and applicability in real-world scenarios.

Keywords: artificial intelligence, convolutional neural network, emotion recognition, vocal patterns

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Authors: Maryam Maimota Shehu

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Words are the basic building blocks of a language. In everyday usage of a language, words are used, and new words are formed and reformed to contain and accommodate all entities, phenomena, qualities and every aspect of the entire life. Despite the fact that many studies have been conducted on morphological processes in The Hausa language. Most of the works concentrated on borrowing, affixation, reduplication and derivation, but blending has been neglected to the extent that some of the Hausa linguists claim that, blending does not exist in the language. Therefore, the current study investigates and examines blending as one of the word formation processes' in the language. The study focuses its main attention on blending as a word-formation process and how this process is used adequately in the formation of words in The Hausa language. To achieve the aims, the research answered these questions: 1) is blending used as a process of word formation in Hausa? 2) What are the words formed using this process? This study utilizes the Natural Morphology Theory proposed by Dressler, (1985) which was adopted by Belly (2007). The data of this study have been collected from newspaper articles, novels, and written literature of Hausa language. Based on the findings, this study found out that, there exist new kind of words formed in The Hausa language under blending, which previous findings did not either reveal or explain in detail. Another part of the finding shows that some of the words change their grammatical classes and meaning while blended.

Keywords: morphology, word formation, blending in hausa language, language

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Authors: Si Chen, Quanhong Jiang

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In the increasingly mature posture recognition technology, human movement information is widely used in sports rehabilitation, human-computer interaction, medical health, human posture assessment, and other fields today; this project uses the most original ideas; it is proposed to use the collection equipment for the collection of myoelectric data, reflect the muscle posture change on a degree of freedom through data processing, carry out data-muscle three-dimensional model joint adjustment, and realize basic pose recognition. Based on this, bionic aids or medical rehabilitation equipment can be further developed with the help of robotic arms and cutting-edge technology, which has a bright future and unlimited development space.

Keywords: pose recognition, 3D animation, electromyography, machine learning, bionics

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Authors: Abder-Rahman Ali, Antoine Vacavant, Manuel Grand-Brochier, Adélaïde Albouy-Kissi, Jean-Yves Boire

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In this paper, we present a new segmentation approach for liver lesions in regions of interest within MRI (Magnetic Resonance Imaging). This approach, based on a two-cluster Fuzzy C-Means methodology, considers the parameter variable compactness to handle uncertainty. Fine boundaries are detected by a local recursive merging of ambiguous pixels with a sequential forward floating selection with Zernike moments. The method has been tested on both synthetic and real images. When applied on synthetic images, the proposed approach provides good performance, segmentations obtained are accurate, their shape is consistent with the ground truth, and the extracted information is reliable. The results obtained on MR images confirm such observations. Our approach allows, even for difficult cases of MR images, to extract a segmentation with good performance in terms of accuracy and shape, which implies that the geometry of the tumor is preserved for further clinical activities (such as automatic extraction of pharmaco-kinetics properties, lesion characterization, etc).

Keywords: defuzzification, floating search, fuzzy clustering, Zernike moments

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Authors: Yanting Cao, Kazumitsu Nawata

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As smartphones upgrading, their software and hardware are getting smarter, so the smartphone-based human activity recognition will be described as more refined, complex, and detailed. In this context, we analyzed a set of experimental data obtained by observing and measuring 30 volunteers with six activities of daily living (ADL). Due to the large sample size, especially a 561-feature vector with time and frequency domain variables, cleaning these intractable features and training a proper model becomes extremely challenging. After a series of feature selection and parameters adjustment, a well-performed SVM classifier has been trained.

Keywords: smart sensors, human activity recognition, artificial intelligence, SVM

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Authors: Atanu K Samanta, Asim Ali Khan

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Due to the acquisition of huge amounts of brain tumor magnetic resonance images (MRI) in clinics, it is very difficult for radiologists to manually interpret and segment these images within a reasonable span of time. Computer-aided diagnosis (CAD) systems can enhance the diagnostic capabilities of radiologists and reduce the time required for accurate diagnosis. An intelligent computer-aided technique for automatic detection of a brain tumor through MRI is presented in this paper. The technique uses the following computational methods; the Level Set for segmentation of a brain tumor from other brain parts, extraction of features from this segmented tumor portion using gray level co-occurrence Matrix (GLCM), and the Artificial Neural Network (ANN) to classify brain tumor images according to their respective types. The entire work is carried out on 50 images having five types of brain tumor. The overall classification accuracy using this method is found to be 98% which is significantly good.

Keywords: brain tumor, computer-aided diagnostic (CAD) system, gray-level co-occurrence matrix (GLCM), tumor segmentation, level set method

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Authors: Luis N. Pereira, Marco P. Carrasco

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Dynamic pricing is a revenue management strategy in which hotel suppliers define, over time, flexible and different prices for their services for different potential customers, considering the profile of e-consumers and the demand and market supply. This means that the fundamentals of dynamic pricing are based on economic theory (price elasticity of demand) and market segmentation. This study aims to define a dynamic pricing strategy and a contextualized offer to the e-consumers profile in order to improve the number of reservations of an online distribution channel. Segmentation methods (hierarchical and non-hierarchical) were used to identify and validate an optimal number of market segments. A profile of the market segments was studied, considering the characteristics of the e-consumers and the probability of reservation a room. In addition, the price elasticity of demand was estimated for each segment using econometric models. Finally, predictive models were used to define rules for classifying new e-consumers into pre-defined segments. The empirical study illustrates how it is possible to improve the intelligence of an online distribution channel system through an optimal dynamic pricing strategy and a contextualized offer to the profile of each new e-consumer. A database of 11 million e-consumers of an online distribution channel was used in this study. The results suggest that an appropriate policy of market segmentation in using of online reservation systems is benefit for the service suppliers because it brings high probability of reservation and generates more profit than fixed pricing.

Keywords: dynamic pricing, e-consumers segmentation, online reservation systems, predictive analytics

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Authors: Khaled Mohammed

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This paper presents novel face recognition and identification approaches for the real-time attendance management problem in large companies/factories and government institutions. The proposed uses the Minimum Ratio (MR) approach for employee identification. Capturing the authentic face variability from a sequence of video frames has been considered for the recognition of faces and resulted in system robustness against the variability of facial features. Experimental results indicated an improvement in the performance of the proposed system compared to the Previous approaches at a rate between 2% to 5%. In addition, it decreased the time two times if compared with the Previous techniques, such as Extreme Learning Machine (ELM) & Multi-Scale Structural Similarity index (MS-SSIM). Finally, it achieved an accuracy of 99%.

Keywords: attendance management system, face detection and recognition, live face recognition, minimum ratio

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Authors: Jyoti Bharti, Navneet Manjhi, M. K.Gupta, Bimi Jain

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A reliable gait features are required to extract the gait sequences from an images. In this paper suggested a simple method for gait identification which is based on moments. Moment values are extracted on different number of frames of gray scale and silhouette images of CASIA database. These moment values are considered as feature values. Fuzzy logic and nearest neighbour classifier are used for classification. Both achieved higher recognition.

Keywords: gait, fuzzy logic, nearest neighbour, recognition rate, moments

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Authors: Smita Pallavi, Raj Ratn Pranesh, Sumit Kumar

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Information representation as tables is compact and concise method that eases searching, indexing, and storage requirements. Extracting and cloning tables from parsable documents is easier and widely used; however, industry still faces challenges in detecting and extracting tables from OCR (Optical Character Recognition) documents or images. This paper proposes an algorithm that detects and extracts multiple tables from OCR document. The algorithm uses a combination of image processing techniques, text recognition, and procedural coding to identify distinct tables in the same image and map the text to appropriate the corresponding cell in dataframe, which can be stored as comma-separated values, database, excel, and multiple other usable formats.

Keywords: table extraction, optical character recognition, image processing, text extraction, morphological transformation

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Authors: Gunnar Oskarsson, Irena Georgsdottir

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Tourism has been growing incredibly fast during the past years, including the cruise industry, which is gaining increasing popularity among various groups of travelers. It is a challenging task for companies serving cruise ship passengers with local products and services at the point of destination to reach them in due time with information about their offerings, as well learning how to adapt their offerings and messages to the type of customers arriving on each particular occasion. Although some research has been conducted in this sphere, there is still limited knowledge about many specifics within this sector of the tourist industry. The objective of this research is to examine one of these, with the main goal of studying the segmentation of cruise passengers and to learn about marketing practices directed towards them. A qualitative research method, based on in-depth interviews, was used, as this provides an opportunity to gain insight into the participants’ perspectives. Interviews were conducted with 10 respondents from different companies in the tourist industry in Iceland, who interact with cruise passengers on a regular basis in their work environment. The main objective was to gain an understanding of what distinguishes different customer groups, or segments, in this industry, and of the marketing approaches directed towards them. The main findings reveal that participants note the strongest difference between cruise passengers of different nationalities, passengers coming on different ships (size and type), and passengers arriving at different times of the year. A drastic difference was noticed between nationalities in four main segments, American, British, Other European, and Asian customers, although some of these segments could be divided into even further sub-segments. Other important differencing factors were size and type of ships, quality or number of stars on the ship, and travelling time of the year. Companies serving cruise ship passengers, as well as the customers themselves, could benefit if the offerings of services were designed specifically for particular segments within the industry. Concerning marketing towards cruise passengers, the results indicate that it is carried out almost exclusively through the Internet using; a reliable website and, search engine optimization. Marketing is also by word-of-mouth. This research can assist practitioners by offering a deeper understanding of the approaches that may be effective in marketing local products and services to cruise ship passengers, based on their segmentation and by identifying effective ways to reach them. The research, furthermore, provides a valuable contribution to marketing knowledge for the benefit of an increasingly important market segment in a fast growing tourist industry.

Keywords: capabilities, global integration, internationalisation, SMEs

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Authors: Ledeza Jordan Babiano

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Translating statements into mathematical notations is one of the processes in word problem-solving. However, based on the literature, students still have difficulties with this skill. The purpose of this study was to investigate the translation errors of the students when they translate algebraic word problems into mathematical structures and locate the errors via the lens of the Translation-Verification Model. Moreover, this qualitative research study employed content analysis. During the data-gathering process, the students were asked to answer a six-item algebra word problem questionnaire, and their answers were analyzed by experts through blind coding using the Translation-Verification Model to determine their translation errors. After this, a focus group discussion was conducted, and the data gathered was analyzed through thematic analysis to determine the causes of the students’ translation errors. It was found out that students’ prevalent error in translation was the interpretation error, which was situated in the Attribute construct. The emerging themes during the FGD were: (1) The procedure of translation is strategically incorrect; (2) Lack of comprehension; (3) Algebra concepts related to difficulty; (4) Lack of spatial skills; (5) Unprepared for independent learning; and (6) The content of the problem is developmentally inappropriate. These themes boiled down to the major concept of independent learning preparedness in solving mathematical problems. This concept has subcomponents, which include contextual and conceptual factors in translation. Consequently, the results provided implications for instructors and professors in Mathematics to innovate their teaching pedagogies and strategies to address translation gaps among students.

Keywords: mathematical structure, algebra word problems, translation, errors

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Authors: Norshakirah Ab.Aziz, Nurul Atiqah Jamaluddin

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Consequently, electronic word-of-mouth (e-WoM) becomes one of the resources in the decision making process and considered a valuable marketing channel for consumers and organizations. Admittedly, there is increasing concern on the accuracy and genuine of e-WoM content because consumers prefer to look out product or service information available online. Thus, the focus of this study is to propose a model and guidelines how to select trusted online review content according to domain chosen –undergrad students smartphone online review. Undeniable, mobile devices like smartphone has now become a necessity in today are daily life to complete our daily chores. The model and guideline focused on product competency review and the message integrity. In other words, this study aims to enable consumers to identify trusted online review content, which helps them in buying decisions.

Keywords: electronic word of mouth, e-WoM, WoM, online review

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Authors: Ahmed Elmarhomy, K. Terada

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An automated method for counting passerby has been proposed using virtual-vertical measurement lines. Space-time image is representing the human regions which are treated using the segmentation process. Different color space has been used to perform the template matching. A proper template matching has been achieved to determine direction and speed of passing people. Distinguish one or two passersby has been investigated using a correlation between passerby speed and the human-pixel area. Finally, the effectiveness of the presented method has been experimentally verified.

Keywords: counting people, measurement line, space-time image, segmentation, template matching

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Authors: Zixuan Xu, Yichun Lou, Yang Song, Zihuai Lin

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MediaPipe is an open-source machine learning computer vision framework that can be ported into a multi-platform environment, which makes it easier to use it to recognize the human activity. Based on this framework, many human recognition systems have been created, but the fundamental issue is the recognition of human behavior and posture. In this paper, two methods are proposed to recognize human gestures based on MediaPipe, the first one uses the Adaptive Boosting algorithm to recognize a series of fitness gestures, and the second one uses the Fast Dynamic Time Warping algorithm to recognize 413 continuous fitness actions. These two methods are also applicable to any human posture movement recognition.

Keywords: computer vision, MediaPipe, adaptive boosting, fast dynamic time warping

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Authors: S. Sulochana Singha

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Dhiyan or Dehan is the name of the community and language spoken by the Koch-Rajbangshi people of Barak Valley of Assam. Ethnically, they are Mongoloids, and their language belongs to the Indo-Aryan language family. However, Dhiyan is absent in any classification of Indo-Aryan languages. So the classification of Dhiyan language under the Indo-Aryan language family is completely based on the shared typological features of the other Indo-Aryan languages. Typologically, Dhiyan is an agglutinating language, and it shares many features of Indo-Aryan languages like presence of aspirated voiced stops, non-tonal, verb-person agreement, adjectives as different word class, prominent tense and subject object verb word order. Reduplication is a productive word-formation process in Dhiyan. Besides it also expresses plurality, intensification, and distributive. Generally, reduplication in Dhiyan can be at the morphological or lexical level. Morphological reduplication in Dhiyan involves expressives which includes onomatopoeias, sound symbolism, idiophones, and imitatives. Lexical reduplication in the language can be formed by echo formations and word reduplication. Echo formation in Dhiyan is formed by partial repetition from the base word which can be either consonant alternation or vowel alternation. The consonant alternation is basically found in onset position while the alternation of vowel is basically found in open syllable particularly in final syllable. Word reduplication involves reduplication of nouns, interrogatives, adjectives, and numerals which further can be class changing or class maintaining reduplication. The process of reduplication can be partial or complete whether it is lexical or morphological. The present paper is an attempt to describe some aspects of the formation, function, and usage of reduplications in Dhiyan which is mainly spoken in ten villages in the Eastern part of Barak River in the Cachar District of Assam.

Keywords: Barak-Valley, Dhiyan, Indo-Aryan, reduplication

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Authors: El Mahdi Barrah, Said Safi, Abdessamad Malaoui

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In this paper, we present an algorithm for reconstruction from incomplete 2D scans for tifinagh characters. This algorithm is based on using correlation between the lost block and its neighbors. This system proposed contains three main parts: pre-processing, features extraction and recognition. In the first step, we construct a database of tifinagh characters. In the second step, we will apply “shape analysis algorithm”. In classification part, we will use Neural Network. The simulation results demonstrate that the proposed method give good results.

Keywords: Tifinagh character recognition, neural networks, local cost computation, ANN

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Authors: Deawan Rakin Ahamed Remal, Sinthia Chowdhury, Sharun Akter Khushbu, Sheak Rashed Haider Noori

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Native Speech to text synthesis has its own leverage for the purpose of mankind. The extensive nature of art to speaking different accents is common but the purpose of communication between two different accent types of people is quite difficult. This problem will be motivated by the extraction of the wrong perception of language meaning. Thus, many existing automatic speech recognition has been placed to detect text. Overall study of this paper mentions a review of NSTTR (Native Speech Text to Text Recognition) synthesis compared with Text to Text recognition. Review has exposed many text to text recognition systems that are at a very early stage to comply with the system by native speech recognition. Many discussions started about the progression of chatbots, linguistic theory another is rule based approach. In the Recent years Deep learning is an overwhelming chapter for text to text learning to detect language nature. To the best of our knowledge, In the sub continent a huge number of people speak in Bangla language but they have different accents in different regions therefore study has been elaborate contradictory discussion achievement of existing works and findings of future needs in Bangla language acoustic accent.

Keywords: TTR, NSTTR, text to text recognition, deep learning, natural language processing

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Authors: Sonia Perez-Gamboa, Qingquan Sun, Yan Zhang

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Non-intrusive sensor-based human activity recognition (HAR) is utilized in a spectrum of applications, including fitness tracking devices, gaming, health care monitoring, and smartphone applications. Deep learning models such as convolutional neural networks (CNNs) and long short term memory (LSTM) recurrent neural networks (RNNs) provide a way to achieve HAR accurately and effectively. In this paper, we design a multi-layer hybrid architecture with CNN and LSTM and explore a variety of multi-layer combinations. Based on the exploration, we present a lightweight, hybrid, and multi-layer model, which can improve the recognition performance by integrating local features and scale-invariant with dependencies of activities. The experimental results demonstrate the efficacy of the proposed model, which can achieve a 94.7% activity recognition rate on a benchmark human activity dataset. This model outperforms traditional machine learning and other deep learning methods. Additionally, our implementation achieves a balance between recognition rate and training time consumption.

Keywords: deep learning, LSTM, CNN, human activity recognition, inertial sensor

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Authors: Isabelle Lorge, Napoleon Katsos

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BACKGROUND: Children growing up in a multilingual environment face challenges related to the need to monitor the speaker’s linguistic abilities, more frequent communication failures, and having to acquire a large number of words in a limited amount of time compared to monolinguals. As a result, bilingual learners may develop different word learning strategies, rely more on some strategies than others, and engage cognitive resources such as theory of mind and attention skills in different ways. HYPOTHESIS: The goal of our study is to investigate whether multilingual exposure leads to improvements in the ability to use pragmatic inference for word learning, i.e., to use speaker cues to derive their referring intentions, often by overcoming lower level salience effects. The speaker cues we identified as relevant are (a) use of a modifier with or without stress (‘the WET dax’ prompting the choice of the referent which has a dry counterpart), (b) referent extension (‘this is a kitten with a fep’ prompting the choice of the unique rather than shared object), (c) referent novelty (choosing novel action rather than novel object which has been manipulated already), (d) teacher versus random sampling (assuming the choice of specific examples for a novel word to be relevant to the extension of that new category), and finally (e) emotional affect (‘look at the figoo’ uttered in a sad or happy voice) . METHOD: To this end, we implemented on a touchscreen computer a task corresponding to each of the cues above, where the child had to pick the referent of a novel word. These word learning tasks (a), (b), (c), (d) and (e) were adapted from previous word learning studies. 113 children have been tested (54 reception and 59 year 1, ranging from 4 to 6 years old) in a London primary school. Bilingual or monolingual status and other relevant information (age of onset, proficiency, literacy for bilinguals) is ascertained through language questionnaires from parents (34 out of 113 received to date). While we do not yet have the data that will allow us to test for effect of bilingualism, we can already see that performances are far from approaching ceiling in any of the tasks. In some cases the children’s performances radically differ from adults’ in a qualitative way, which means that there is scope for quantitative and qualitative effects to arise between language groups. The findings should contribute to explain the puzzling speed and efficiency that bilinguals demonstrate in acquiring competence in two languages.

Keywords: bilingualism, pragmatics, word learning, attention

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Authors: Kamyar Fakhr, Roozbeh Salmani

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Alphonse Bertillon developed the first biometric security system in the 1800s. Today, many governments and giant companies are considering or have procured biometrically enabled security schemes. Iris is a kaleidoscope of patterns and colors. Each individual holds a set of irises more unique than their thumbprint. Every single day, giant companies like Google and Apple are experimenting with reliable biometric systems. Now, after almost 200 years of improvements, face ID does not work with masks, it gives access to fake 3D images, and there is no global usage of biometric recognition systems as national identity (ID) card. The goal of this paper is to demonstrate the advantages of iris recognition overall biometric recognition systems. It make two extensions: first, we illustrate how a very large amount of internet fraud and cyber abuse is happening due to bugs in face recognition systems and in a very large dataset of 3.4M people; second, we discuss how establishing a secure global network of iris recognition devices connected to authoritative convolutional neural networks could be the safest solution to this dilemma. Another aim of this study is to provide a system that will prevent system infiltration caused by cyber-attacks and will block all wireframes to the data until the main user ceases the procedure.

Keywords: biometric system, convolutional neural network, cyber-attack, secure

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Authors: Endrick Barnacin, Jean-Luc Henry, Jimmy Nagau, Jack Molinie

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Being able to identify biological particles such as spores, viruses, or pollens is important for health care professionals, as it allows for appropriate therapeutic management of patients. Optical microscopy is a technology widely used for the analysis of these types of microorganisms, because, compared to other types of microscopy, it is not expensive. The analysis of an optical microscope slide is a tedious and time-consuming task when done manually. However, using machine learning and computer vision, this process can be automated. The first step of an automated microscope slide image analysis process is segmentation. During this step, the biological particles are localized and extracted. Very often, the use of an automatic thresholding method is sufficient to locate and extract the particles. However, in some cases, the particles are not extracted individually because they are stuck to other biological elements. In this paper, we propose a stuck particles separation method based on the use of the Sobel operator and thresholding. We illustrate it by applying it to the separation of 813 images of adjacent pollen grains. The method correctly separated 95.4% of these images.

Keywords: image segmentation, stuck particles separation, Sobel operator, thresholding

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Authors: Tomas Trainys, Algimantas Venckauskas

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Application of biometric features to the cryptography for human identification and authentication is widely studied and promising area of the development of high-reliability cryptosystems. Biometric cryptosystems typically are designed for patterns recognition, which allows biometric data acquisition from an individual, extracts feature sets, compares the feature set against the set stored in the vault and gives a result of the comparison. Preprocessing and fusion of biometric data are the most important phases in generating a feature vector for key generation or authentication. Fusion of biometric features is critical for achieving a higher level of security and prevents from possible spoofing attacks. The paper focuses on the tasks of initial processing and fusion of multiple representations of finger vein modality patterns. These tasks are solved by applying conventional image preprocessing methods and machine learning techniques, Convolutional Neural Network (SVM) method for image segmentation and feature extraction. An article presents a method for generating sets of biometric features from a finger vein network using several instances of the same modality. Extracted features sets were fused at the feature level. The proposed method was tested and compared with the performance and accuracy results of other authors.

Keywords: bio-cryptography, biometrics, cryptographic key generation, data fusion, information security, SVM, pattern recognition, finger vein method.

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Authors: Pepe Marais, Krishna Govender

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This study aimed to investigate the impact of “purpose” as a principal leadership skill on the performance of two township schools using a quantitative research design and collecting data from the school principals, teachers and matric learners, using the 28-scale Servant Leadership Test as well as Gallup’s Q12 Employee Engagement survey. The questionnaires addressed the key objectives, namely, the extent to which the principals of the participating schools exhibited servant leadership and their understanding of “purpose” as one word in leadership and how teachers and learners perceived the impact of a “one-word” purpose-driven leader on the performance of the selected schools. Although no relationship could be demonstrated between ‘’purpose’’ and the performance of the two township schools, it became evident that a significant increase in Servant Leadership leads to a significant increase in engagement and performance, as measured by the matric pass rate. It is recommended that workshops be facilitated with principals and teachers in order to entrench ‘’purpose’’ deeper throughout the schools. In addition, Servant Leadership training has to be conduced to increase the leadership ability of the school principals. Future research in the area of ‘’purpose as one word’’, as well as Servant Leadership as a principal skillset within South Africa’s public school leadership, is recommended.

Keywords: school leadership, servant leadership, one-word purpose, engagement, leadership

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Authors: Giovanna Borges Bottino, Luis Felipe Freitas do Nascimento Alves Teixeira

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This article aims to present a case study of the National Civil Aviation Agency (ANAC) in Brazil, ANAC-id. ANAC-id is the artificial intelligence algorithm developed for image analysis that recognizes standard images of unobstructed and uprighted face without sunglasses, allowing to identify potential inconsistencies. It combines YOLO architecture and 3 libraries in python - face recognition, face comparison, and deep face, providing robust analysis with high level of accuracy.

Keywords: artificial intelligence, deepface, face compare, face recognition, YOLO, computer vision

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Authors: Kiyoko Yoshimura, Yasunobu Kino

Abstract:

Focusing on professional call centers where workers with expertise perform services, this study aims to clarify the relationships between emotional labor and job satisfaction and the effects of recognition of customer feedback. Since the professional call center operators consist of professional license holders (qualification holders) and those who do not (non-holders), the following three points are analyzed in the two groups by using covariance structure analysis and simultaneous multi-population analysis: 1) The relationship between emotional labor and job satisfaction, 2) customer feedback and job satisfaction, and 3) The intermediation effect between the emotional labor of customer feedback and job satisfaction. The following results are obtained: i) no direct effect is found between job satisfaction and emotional labor for qualification holders and non-holders, ii) for qualification holders and non-holders, recognition of positive feedback and recognition of negative feedback had positive and negative effects on job satisfaction, respectively, iii) for qualification and non-holders, "consideration for colleagues" influences job satisfaction by recognizing positive feedback, and iv) only for qualification holders, the factors "customer-oriented emotional expression" and "emotional disharmony" have a positive and negative effect on job satisfaction, respectively, through recognition of positive feedback and recognition of negative feedback.

Keywords: call center, emotional labor, professional service, job satisfaction, customer feedback

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