Search results for: activity recognition

Authors: Shiyin He, Zheng Huang

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In this paper, deep learning methods are applied in bio-medical field to detect and count different types of cells in an automatic way instead of manual work in medical practice, specifically in bone marrow examination. The process is mainly composed of two steps, detection and recognition. Mask-Region-Convolutional Neural Networks (Mask-RCNN) was used for detection and image segmentation to extract cells and then Convolutional Neural Networks (CNN), as well as Deep Residual Network (ResNet) was used to classify. Result of cell detection network shows high efficiency to meet application requirements. For the cell recognition network, two networks are compared and the final system is fully applicable.

Keywords: cell detection, cell recognition, deep learning, Mask-RCNN, ResNet

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Authors: C. V. Aravinda, H. N. Prakash

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In this paper, we tried to convey fusion and state of art pertaining to SIL character recognition systems. In the first step, the text is preprocessed and normalized to perform the text identification correctly. The second step involves extracting relevant and informative features. The third step implements the classification decision. The three stages which involved are Data acquisition and preprocessing, Feature extraction, and Classification. Here we concentrated on two techniques to obtain features, Feature Extraction & Feature Selection. Edge-hinge distribution is a feature that characterizes the changes in direction of a script stroke in handwritten text. The edge-hinge distribution is extracted by means of a windowpane that is slid over an edge-detected binary handwriting image. Whenever the mid pixel of the window is on, the two edge fragments (i.e. connected sequences of pixels) emerging from this mid pixel are measured. Their directions are measured and stored as pairs. A joint probability distribution is obtained from a large sample of such pairs. Despite continuous effort, handwriting identification remains a challenging issue, due to different approaches use different varieties of features, having different. Therefore, our study will focus on handwriting recognition based on feature selection to simplify features extracting task, optimize classification system complexity, reduce running time and improve the classification accuracy.

Keywords: word segmentation and recognition, character recognition, optical character recognition, hand written character recognition, South Indian languages

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Authors: John Lorenzo Bautista, Yoon-Joong Kim

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This paper presents the development of a Filipino speech recognition tool using the HTK System. The system was trained from a subset of the Filipino Speech Corpus developed by the DSP Laboratory of the University of the Philippines-Diliman. The speech corpus was both used in training and testing the system by estimating the parameters for phonetic HMM-based (Hidden-Markov Model) acoustic models. Experiments on different mixture-weights were incorporated in the study. The phoneme-level word-based recognition of a 5-state HMM resulted in an average accuracy rate of 80.13 for a single-Gaussian mixture model, 81.13 after implementing a phoneme-alignment, and 87.19 for the increased Gaussian-mixture weight model. The highest accuracy rate of 88.70% was obtained from a 5-state model with 6 Gaussian mixtures.

Keywords: Filipino language, Hidden Markov Model, HTK system, speech recognition

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Authors: Eman Ahmed Alsaleh

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Background: Many studies published in other countries identified certain perceived benefits and barriers to physical activity among patients with coronary heart disease. Nevertheless, there is no data about the issue relating to Jordanian patients with coronary heart disease. Objective: This study aimed to describe the prevalence of level of physical activity, benefits of and barriers to physical activity as perceived by Jordanian patients with coronary heart disease, and the relationship between physical activity and perceived benefits of and barriers to physical activity. In addition, it focused on examining the influence of selected sociodemographic and health characteristics on physical activity and the perceived benefits of and barriers to physical activity. Methods: A cross-sectional design was performed on a sample of 400 patients with coronary heart disease. They were given a list of perceived benefits and barriers to physical activity and asked to what extent they disagreed or agreed with each. Results: Jordanian patients with coronary heart disease perceived various benefits and barriers to physical activity. Most of these benefits were physiologically related (average mean = 5.7, SD = .7). The most substantial barriers to physical activity as perceived by the patients were: feeling anxiety, not having enough time, lack of interest, bad weather, and feeling of being uncomfortable. Sociodemographic and health characteristics that significantly influenced perceived barriers to physical activity were age, gender, health perception, chest pain frequency, education, job, caring responsibilities, ability to travel alone, smoking, and previous and current physical activity behaviour. Conclusion: This research demonstrates that patients with coronary heart disease have perceived physiological benefits of physical activity, and they have perceived motivational, physical health, and environmental barriers to physical activity, which is significant in developing intervention strategies that aim to maximize patients' participation in physical activity and overcome barriers to physical activity.

Keywords: prevalence, coronary heart disease, physical activity, perceived barriers

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Authors: Svitov David, Alyamkin Sergey

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The usage of convolutional neural networks (CNNs) in conjunction with the margin-based softmax approach demonstrates the state-of-the-art performance for the face recognition problem. Recently, lightweight neural network models trained with the margin-based softmax have been introduced for the face identification task for edge devices. In this paper, we propose a distillation method for lightweight neural network architectures that outperforms other known methods for the face recognition task on LFW, AgeDB-30 and Megaface datasets. The idea of the proposed method is to use class centers from the teacher network for the student network. Then the student network is trained to get the same angles between the class centers and face embeddings predicted by the teacher network.

Keywords: ArcFace, distillation, face recognition, margin-based softmax

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Authors: Yang-Keun Ahn, Kwang-Soon Choi, Young-Choong Park, Kwang-Mo Jung

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Vision based user interfaces to control TVs and PCs have the advantage of being able to perform natural control without being limited to a specific device. Accordingly, various studies on hand gesture recognition using RGB cameras or depth cameras have been conducted. However, such cameras have the disadvantage of lacking in accuracy or the construction cost being large. The proposed method uses a low cost IR camera to accurately differentiate between the hand and the background. Also, complicated learning and template matching methodologies are not used, and the correlation between the fingertips extracted through curvatures is utilized to recognize Click and Move gestures.

Keywords: recognition, hand gestures, infrared camera, RGB cameras

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Authors: Vilma Zydziunaite, Gitana Balezentiene, Vilma Zydziunaite

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Teaching is one of most responsible profession, and it is not only a job of an artisan. This profes-sion needs a developed ability to identify oneself with the chosen teaching profession. Research questions: How teachers characterize their authentic individual professional identity? What factors teachers exclude, which support and limit the professional identity? Aim was to develop the grounded theory (GT) about teacher’s professional identity (TPI). Research methodology is based on Charmaz GT version. Data were collected via semi-structured interviews with the he sample of 12 teachers. Findings. 15 extracted categories revealed that the core of TPI is teacher’s professional calling. Premises of TPI are family support, motives for choos-ing teacher’s profession, teacher’s didactic competence. Context of TPI consists of teacher compli-ance with the profession, purposeful preparation for pedagogical studies, professional growth. The strategy of TPI is based on teacher relationship with school community strengthening. The profes-sional frustration limits the TPI. TPI outcome includes teacher recognition, authority; professional mastership, professionalism, professional satisfaction. Dimensions of TPI GT the past (reaching teacher’s profession), present (teacher’s commitment to professional activity) and future (teacher’s profession reconsideration). Conclusions. The substantive GT describes professional identity as complex, changing and life-long process, which develops together with teacher’s personal identity and is connected to professional activity. The professional decision "to be a teacher" is determined by the interaction of internal (professional vocation, personal characteristics, values, self-image, talents, abilities) and external (family, friends, school community, labor market, working condi-tions) factors. The dimensions of the TPI development includes: the past (the pursuit of the teaching profession), the present (the teacher's commitment to professional activity) and the future (the revi-sion of the teaching profession). A significant connection emerged - as the teacher's professional commitment strengthens (creating a self-image, growing the teacher's professional experience, recognition, professionalism, mastery, satisfaction with pedagogical activity), the dimension of re-thinking the teacher's profession weakens. This proves that professional identity occupies an im-portant place in a teacher's life and it affects his professional success and job satisfaction. Teachers singled out the main factors supporting a teacher's professional identity: their own self-image per-ception, professional vocation, positive personal qualities, internal motivation, teacher recognition, confidence in choosing a teaching profession, job satisfaction, professional knowledge, professional growth, good relations with the school community, pleasant experiences, quality education process, excellent student achievements.

Keywords: grounded theory, teacher professional identity, semi-structured interview, school, students, school community, family

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Authors: Bin Liu

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Protein remote homology detection and fold recognition are two most important tasks in protein sequence analysis, which is critical for protein structure and function studies. In this study, we combined the profile-based features with various string kernels, and constructed several computational predictors for protein remote homology detection and fold recognition. Experimental results on two widely used benchmark datasets showed that these methods outperformed the competing methods, indicating that these predictors are useful computational tools for protein sequence analysis. By analyzing the discriminative features of the training models, some interesting patterns were discovered, reflecting the characteristics of protein superfamilies and folds, which are important for the researchers who are interested in finding the patterns of protein folds.

Keywords: protein remote homology detection, protein fold recognition, profile-based features, Support Vector Machines (SVMs)

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Authors: Nijole Savickiene, Antonella Di Sotto, Gabriela Mazzanti, Rasa Starselskyte, Silvia Di Giacomo, Annabella Vitalone

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Plant lectins are non-enzymic and non-immune origin proteins that specifically recognize and bind to various sugar structures and possess the activity to agglutinate cells and/or precipitate polysaccharides and glycoconjugates. The emerging evidences showed that plant lectins contribute not only to tumour cell recognition but also to cell adhesion and localization, to signal transduction, to mitogenic cytotoxicity and apoptosis. Among chitin-binding lectins, the Urtica dioica agglutinin (UDA), which is a complex of different isoforms, has been poorly studied for its biological activity. In this context and according to the increasing interest for lectins as novel antitumor drugs, present paper aimed at evaluating the potential antimutagenic activity of a lectin-like glycoprotein-enriched fraction from aerial part of Urtica dioica L. Aim: to evaluate the potential chemopreventive properties of a protein - lectin fraction from the aerial part of Urtica dioica. Materials and methods: Protein – lectin fraction has been tested for the antimutagenic activity in bacteria (50–800 mg/plate; Ames test by the preincubation method) and for the cytotoxicity on human hepatoma HepG2 cells (0.06–2 mg/mL; 24 and 48 h incubation). Results: Protein – lectin fraction from stinging nettle was not cytotoxic on HepG2 cells up to 2 mg/mL; conversely, it exhibited a strong antimutagenic activity against the mutagen 2-aminoanthracene (2AA) in all strains tested (maximum inhibition of 56.78 and 61% in TA98, TA100, and WP2uvrA strains, respectively, at 800 mg/plate). Discussion and conclusions: Protein – lectin fraction from Urtica dioica L. possesses antimutagenic and radical scavenging properties. Being 2AA a pro-carcinogenic agent, we hypothesize that the antimutagenicity of it can be due to the inhibition of CYP450-isoenzymes, involved in the mutagen bioactivation.

Keywords: lectins, antimutagenicity, chemoprevention, Urtica dioica

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Authors: Kahandawala Arachchige Thani Chathurika Kahandawala

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This research is attempted to analyse, Sri Lanka’s current situation regarding the recognition and enforcement of international commercial arbitration awards. Sri Lanka has been involved with commercial arbitration for a long time period. But there are good and bad legal practices in place in proceedings in Sri Lanka legal system. The common perception and reality of Sri Lanka’s arbitration law and practices regarding recognition and enforcement of international arbitral awards is far behind the international standards. Therefore arbitration as a dispute resolution method has become a time-consuming and costly method in Sri Lanka. This research is employed with the qualitative method based on both primary and secondary resources. This carried out the comparative analysis of recognition and enforcement in international arbitration laws established jurisdiction in Singapore and the United Kingdom, which are known as best counties as a seat of arbitration in Asia and Europe. International conventions, act and all the legal proceedings regarding recognition and enforcement of an international arbitral award in Sri Lanka are going to be discussed in the research. In the Jurisdiction of Sri Lanka, critically need to value an international arbitral award in the domestic legal system. Therefore an award has to be recognised in Sri Lanka. Otherwise, it doesn’t have any value. After recognizing it, court can enforce it. This research intends to provide a comparative analysis to overcome the drawbacks.

Keywords: arbitration, alternative dispute method, recognition and enforcement, foreign arbitral awards, Sri Lankan legal system, arbitral award in Singapore

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Authors: M. Dabirinezhad, M. Bayat Pour, A. Dabirinejad

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Recognition of the color spectrum of the teeth plays a significant role in the dental laboratories to produce dentures. Since there are various types and colours of teeth for each patient, there is a need to specify the exact and the most suitable colour to produce a denture. Usually, dentists utilize pallets to identify the color that suits a patient based on the color of the adjacent teeth. Consistent with this, there can be human errors by dentists to recognize the optimum colour for the patient, and it can be annoying for the patient. According to the statistics, there are some claims from the patients that they are not satisfied by the colour of their dentures after the installation of the denture in their mouths. This problem emanates from the lack of sufficient accuracy during the colour recognition process of denture production. The colour recognition pen (CRP) is a technology to distinguish the colour spectrum of the intended teeth with the highest accuracy. CRP is equipped with a sensor that is capable to read and analyse a wide range of spectrums. It is also connected to a database that contains all the spectrum ranges, which exist in the market. The database is editable and updatable based on market requirements. Another advantage of this invention can be mentioned as saving time for the patients since there is no need to redo the denture production in case of failure on the first try.

Keywords: colour recognition pen, colour spectrum, dental laboratory, denture

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Authors: Abujnah Dukali

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Natural honey was assessed in cell culture system for its anticancer activity. Human leukemic cell line HL 60 was treated with honey and cultured for 5 days and cytotoxicity was calculated by MTT assay. Honey showed cytotoxicity with CC50 value of 174.20 µg/ml. Radical modulation activities was assessed by lipid peroxidation assay using egg lecithin. Honey showed antioxidant activity with EC50 value of 159.73 µg/ml. In addition, treatment with HL60 cells also resulted in nuclear DNA fragmentation, as seen in agarose gel electrophoresis. This is a hallmark of cells undergoing apoptosis. Confirmation of apoptosis was performed by staining the cells with Annexin V and FACS analysis. Apoptosis is an active, genetically regulated disassembly of the cell form within. Disassembly creates changes in the phospholipid content of the cytoplasmic membrane outer leaflet. Phosphatidylserine (PS) is translocated from the inner to the outer surface of the cell for phagocytic cell recognition. The human anticoagulant, annexin V, is a Ca2+-dependent phospholipid protein with a high affinity for PS. Annexin V labeled with fluorescein can identify apoptotic cells in the population It is a confirmatory test for apoptosis. Annexin V-positive cells were defined as apoptotic cells. Since honey shows both antioxidant activity and cytotoxicity at almost the same concentration, it can prevent the free radical induced cancer as prophylactic agent and kill the cancer cells by apoptotic process as a chemotherapeutic agent. Everyday intake of honey can prevent the cancer induction.

Keywords: anticancer, cells, DNA, honey

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Authors: Hui Zhang, Ye Tian, Fang Ye, Ziming Guo

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Communication signal modulation recognition technology is one of the key technologies in the field of modern information warfare. At present, communication signal automatic modulation recognition methods are mainly divided into two major categories. One is the maximum likelihood hypothesis testing method based on decision theory, the other is a statistical pattern recognition method based on feature extraction. Now, the most commonly used is a statistical pattern recognition method, which includes feature extraction and classifier design. With the increasingly complex electromagnetic environment of communications, how to effectively extract the features of various signals at low signal-to-noise ratio (SNR) is a hot topic for scholars in various countries. To solve this problem, this paper proposes a feature extraction algorithm for the communication signal based on the improved Holder cloud feature. And the extreme learning machine (ELM) is used which aims at the problem of the real-time in the modern warfare to classify the extracted features. The algorithm extracts the digital features of the improved cloud model without deterministic information in a low SNR environment, and uses the improved cloud model to obtain more stable Holder cloud features and the performance of the algorithm is improved. This algorithm addresses the problem that a simple feature extraction algorithm based on Holder coefficient feature is difficult to recognize at low SNR, and it also has a better recognition accuracy. The results of simulations show that the approach in this paper still has a good classification result at low SNR, even when the SNR is -15dB, the recognition accuracy still reaches 76%.

Keywords: communication signal, feature extraction, Holder coefficient, improved cloud model

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Authors: Jadisha Cornejo, Helio Pedrini

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Recognition of emotions based on facial expressions has received increasing attention from the scientific community over the last years. Several fields of applications can benefit from facial emotion recognition, such as behavior prediction, interpersonal relations, human-computer interactions, recommendation systems. In this work, we develop and analyze an emotion recognition framework based on facial expressions robust to occlusions through the Weber Local Descriptor (WLD). Initially, the occluded facial expressions are reconstructed following an extension approach of Robust Principal Component Analysis (RPCA). Then, WLD features are extracted from the facial expression representation, as well as Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG). The feature vector space is reduced using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Finally, K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) classifiers are used to recognize the expressions. Experimental results on three public datasets demonstrated that the WLD representation achieved competitive accuracy rates for occluded and non-occluded facial expressions compared to other approaches available in the literature.

Keywords: emotion recognition, facial expression, occlusion, fiducial landmarks

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Authors: Diego Adrian Cardenas Jorge, Gerardo Mirando Guisado, Alfredo Barrientos Padilla

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A system based on Affective Computing can detect and interpret human information like voice, facial expressions and body movement to detect emotions and execute a corresponding response. This data is important due to the fact that a person can communicate more effectively with emotions than can be possible with words. This information can be processed through technological components like Facial Recognition, Gait Recognition or Gesture Recognition. As of now, solutions proposed using this technology only consider one component at a given moment. This research investigation proposes two solutions based on Affective Computing taking into account more than one component for emotion detection. The proposals reflect the levels of dependency between hardware devices and software, as well as the interaction process between the system and the user which implies the development of scenarios where both proposals will be put to the test in a live environment. Both solutions are to be developed in code by software engineers to prove the feasibility. To validate the impact on society and business interest, interviews with stakeholders are conducted with an investment mind set where each solution is labeled on a scale of 1 through 5, being one a minimum possible investment and 5 the maximum.

Keywords: affective computing, emotions, emotion detection, face recognition, gait recognition

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Authors: Muhammad Imran Ahmad, Ruzelita Ngadiran, Mohd Nazrin Md Isa, Nor Ashidi Mat Isa, Mohd ZaizuIlyas, Raja Abdullah Raja Ahmad, Said Amirul Anwar Ab Hamid, Muzammil Jusoh

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This paper presents two technique, local feature extraction using image spectrum and low frequency spectrum modelling using GMM to capture the underlying statistical information to improve the performance of face recognition system. Local spectrum features are extracted using overlap sub block window that are mapping on the face image. For each of this block, spatial domain is transformed to frequency domain using DFT. A low frequency coefficient is preserved by discarding high frequency coefficients by applying rectangular mask on the spectrum of the facial image. Low frequency information is non Gaussian in the feature space and by using combination of several Gaussian function that has different statistical properties, the best feature representation can be model using probability density function. The recognition process is performed using maximum likelihood value computed using pre-calculate GMM components. The method is tested using FERET data sets and is able to achieved 92% recognition rates.

Keywords: local features modelling, face recognition system, Gaussian mixture models, Feret

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Authors: Ahmad Alwosheel, Ahmed Alqaraawi

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This work proposes an unsupervised text-independent probabilistic approach to recognize Quran reciter voice. It is an accurate approach that works on real time applications. This approach does not require a prior information about reciter models. It has two phases, where in the training phase the reciters' acoustical features are modeled using Gaussian Mixture Models, while in the testing phase, unlabeled reciter's acoustical features are examined among GMM models. Using this approach, a high accuracy results are achieved with efficient computation time process.

Keywords: Quran, speaker recognition, reciter recognition, Gaussian Mixture Model

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Authors: Fawaz S. Al-Anzi, Dia AbuZeina

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Speech recognition is of an important contribution in promoting new technologies in human computer interaction. Today, there is a growing need to employ speech technology in daily life and business activities. However, speech recognition is a challenging task that requires different stages before obtaining the desired output. Among automatic speech recognition (ASR) components is the feature extraction process, which parameterizes the speech signal to produce the corresponding feature vectors. Feature extraction process aims at approximating the linguistic content that is conveyed by the input speech signal. In speech processing field, there are several methods to extract speech features, however, Mel Frequency Cepstral Coefficients (MFCC) is the popular technique. It has been long observed that the MFCC is dominantly used in the well-known recognizers such as the Carnegie Mellon University (CMU) Sphinx and the Markov Model Toolkit (HTK). Hence, this paper focuses on the MFCC method as the standard choice to identify the different speech segments in order to obtain the language phonemes for further training and decoding steps. Due to MFCC good performance, the previous studies show that the MFCC dominates the Arabic ASR research. In this paper, we demonstrate MFCC as well as the intermediate steps that are performed to get these coefficients using the HTK toolkit.

Keywords: speech recognition, acoustic features, mel frequency, cepstral coefficients

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Authors: Sameh Abaza, Mohamed Ibrahim, Tarek Mahmoud

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Due to the development in the digital image processing, its wide use in many applications such as medical, security, and others, the need for more accurate techniques that are reliable, fast and robust is vehemently demanded. In the field of security, in particular, speed is of the essence. In this paper, a pattern recognition technique that is based on the use of Hidden Markov Model (HMM), K-means and the Sobel operator method is developed. The proposed technique is proved to be fast with respect to some other techniques that are investigated for comparison. Moreover, it shows its capability of recognizing the normal face (center part) as well as face boundary.

Keywords: HMM, K-Means, Sobel, accuracy, face recognition

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Authors: Vishwa Joshi

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The study of mood recognition in the field of music has gained a lot of momentum in the recent years with machine learning and data mining techniques and many audio features contributing considerably to analyze and identify the relation of mood plus music. In this paper we consider the same idea forward and come up with making an effort to build a system for automatic recognition of mood underlying the audio song’s clips by mining their audio features and have evaluated several data classification algorithms in order to learn, train and test the model describing the moods of these audio songs and developed an open source framework. Before classification, Preprocessing and Feature Extraction phase is necessary for removing noise and gathering features respectively.

Keywords: music, mood, features, classification

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Authors: Bamidele Samson Alobalorun, Ifedotun Roseline Idowu

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Biometrics technologies apply the human body parts for their unique and reliable identification based on physiological traits. The iris recognition system is a biometric–based method for identification. The human iris has some discriminating characteristics which provide efficiency to the method. In order to achieve this efficiency, there is a need for feature extraction of the distinct features from the human iris in order to generate accurate authentication of persons. In this study, an approach for an iris recognition system using 2D Gabor for feature extraction is applied to iris templates. The 2D Gabor filter formulated the patterns that were used for training and equally sent to the hamming distance matching technique for recognition. A comparison of results is presented using two iris image subjects of different matching indices of 1,2,3,4,5 filter based on the CASIA iris image database. By comparing the two subject results, the actual computational time of the developed models, which is measured in terms of training and average testing time in processing the hamming distance classifier, is found with best recognition accuracy of 96.11% after capturing the iris localization or segmentation using the Daughman’s Integro-differential, the normalization is confined to the Daugman’s rubber sheet model.

Keywords: Daugman rubber sheet, feature extraction, Hamming distance, iris recognition system, 2D Gabor wavelet transform

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Authors: Ferruh Taspinar, Gulce K. Seyyar, Gamze Kurt, Eda O. Okur, Emrah Afsar, Ismail Saracoglu, Betul Taspinar

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Alterations of physical activity can effect body composition (especially body fat ratio); however body mass index may not sufficient to indicate these minimal differences. The aim of this study was to evaluate the relationship between body composition and physical activity in university students. In this study, 132 university students (mean age; 21.21±1.51) were included. Tanita BC-418 and International Physical Activity Questionnaire (IPAQ) were used to evaluate participants. The correlation between the parameters was analysed via Spearman correlation analysis. Significance level in statistical analyses was accepted is 0.05. The results showed that there was no correlation between body mass index and physical activity (p>0.05). There was a positive correlation between body muscle ratio and physical activity, whereas a negative correlation between body fat ratio and physical activity (p<0.05). This study showed that body fat and muscle ratio affects the level of physical activity in healthy university students. Therefore, we thought that physical activity might reduce effects of the diseases caused by disturbed body composition. Further studies are required to support this idea.

Keywords: body composition, body mass index, physical activity, university student

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Authors: Wanthani Paengsri, Thanyarat Chuesaard, Napapha Promsawan

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Spilanthes acmella Murr. was extracted with methanol, yielding methanol crude extract 5.86 %w/w. This study aimed to examine the chemical composition and antioxidant activity of methanolic crude extract. The chemical composition of methanolic crude extract was analyzed by gas chromatography-mass spectrometry (GC-MS). The predominant components were found to be palmitic acid (40.08%), 2-hexadecanoyl glycerol (6.96%) and octadecanoic acid (4.06%). Antioxidant activity was determined using 2,2-diphenyl-1-picryl hydrazyl (DPPH) free radical, for evaluating free radicle scavenging activity. The methanolic extract at 150 µg/mL showed an antioxidant activity with high of radical scavenging activity (75.23%).

Keywords: antioxidant activity, GC-MS analysis, Spilanthes, Phak-Kratt Hauwaen

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Authors: Maria Pavlova

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In our days, it is possible to put the camera on different vehicles like quadcopter, train, airplane and etc. The camera also can be the input sensor in many different systems. That means the object recognition like non separate part of monitoring control can be key part of the most intelligent systems. The aim of this paper is to focus of the object recognition process during vehicles movement. During the vehicle’s movement the camera takes pictures from the environment without storage in Data Base. In case the camera detects a special object (for example human or animal), the system saves the picture and sends it to the work station in real time. This functionality will be very useful in emergency or security situations where is necessary to find a specific object. In another application, the camera can be mounted on crossroad where do not have many people and if one or more persons come on the road, the traffic lights became the green and they can cross the road. In this papers is presented the system has solved the aforementioned problems. It is presented architecture of the object recognition system includes the camera, Raspberry platform, GPS system, neural network, software and Data Base. The camera in the system takes the pictures. The object recognition is done in real time using the OpenCV library and Raspberry microcontroller. An additional feature of this library is the ability to display the GPS coordinates of the captured objects position. The results from this processes will be sent to remote station. So, in this case, we can know the location of the specific object. By neural network, we can learn the module to solve the problems using incoming data and to be part in bigger intelligent system. The present paper focuses on the design and integration of the image recognition like a part of smart systems.

Keywords: camera, object recognition, OpenCV, Raspberry

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Authors: Shih-Yu Lo

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Stereotypes exist in almost every society, affecting how people interact with each other. However, to our knowledge, the influence of stereotypes was rarely explored in the context of basic perceptual processes. This study aims to explore how the gender stereotype affects object recognition. Participants were presented with a series of scene pictures, followed by a target display with a man or a woman, holding a weapon or a non-weapon object. The task was to identify whether the object in the target display was a weapon or not. Although the gender of the object holder could not predict whether he or she held a weapon, and was irrelevant to the task goal, the participant nevertheless tended to identify the object as a weapon when the object holder was a man than a woman. The analysis based on the signal detection theory showed that the stereotype effect on object recognition mainly resulted from the participant’s bias to make a 'weapon' response when a man was in the scene instead of a woman in the scene. In addition, there was a trend that the participant’s sensitivity to differentiate a weapon from a non-threating object was higher when a woman was in the scene than a man was in the scene. The results of this study suggest that the irrelevant social cues implied in the visual scene can be very powerful that they can modulate the basic object recognition process.

Keywords: gender stereotype, object recognition, signal detection theory, weapon

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Authors: Mina Youssef Makram Ibrahim

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Engineering identity contributes to the professional and academic sustainability of female engineers. Recognizability is an important factor that shapes an engineer's identity. People who are deprived of real recognition often fail to create a positive identity. This study draws on Hornet’s recognition theory to identify factors that influence female civil engineers' sense of recognition. Over the past decade, a survey was created and distributed to 330 graduate students in the Department of Civil, Civil and Environmental Engineering at Iowa State University. Survey items include demographics, perceptions of a civil engineer's identity, and factors that influence recognition of a civil engineer's identity, such as B. Opinions about society and family. Descriptive analysis of survey responses revealed that perceptions of civil engineering varied significantly. The definitions of civil engineering provided by participants included the terms structure, design and infrastructure. Almost half of the participants said the main reason for studying Civil Engineering was their interest in the subject, and the majority said they were proud to be a civil engineer. Many study participants reported that their parents viewed them as civil engineers. Institutional and operational treatment was also found to have a significant impact on the recognition of women civil engineers. Almost half of the participants reported feeling isolated or ignored at work because of their gender. This research highlights the importance of recognition in developing the identity of women engineers.

Keywords: civil service, hiring, merit, policing civil engineering, construction, surveying, mapping, pile civil service, Kazakhstan, modernization, a national model of civil service, civil service reforms, bureaucracy civil engineering, gender, identity, recognition

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Authors: Rachid Kacem, Sara Talbi, Yasmina Hemissi, Sofia Bouguattoucha

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The aim of the present work is the evaluation of the antioxidant activity of the Peganum harmala (P. harmala) seeds extracts. The antioxidant activity was evaluated by applying two methods, the method of ß-carotene bleaching and DPPH (2, 2-Diphenyl-1-Picryl-Hydrazyl). Using Folin-Ciocalteu assay, these results revealed that the concentration of polyphenols in EthOH E. (122.28 ± 2.24 µg GAE/mg extract) is the highest. The antiradical activity of the P. harmala seeds extracts on DPPH was found to be dose dependent with polyphenols concentration. The E. EthOH extract showed the highest antioxidant activity (IC = 252.10 ± 11.18 μg /ml). The test of β-carotene bleaching indicates that the E. EthOH of P. harmala showed the highest percentage of the antioxidant activity (49.88 %).

Keywords: antioxidant activity, Peganum harmala, polyphenols, flavonoids

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Authors: Amin Derakhshan, Mohammad Mikaili, Mohammad Ali Khalilzadeh, Amin Mohammadian

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This paper proposes a stress recognition system from multi-modal bio-potential signals. For stress recognition, Support Vector Machines (SVM) and LDA are applied to design the stress classifiers and its characteristics are investigated. Using gathered data under psychological polygraph experiments, the classifiers are trained and tested. The pattern recognition method classifies stressful from non-stressful subjects based on labels which come from polygraph data. The successful classification rate is 96% for 12 subjects. It means that facial thermal imaging due to its non-contact advantage could be a remarkable alternative for psycho-physiological methods.

Keywords: stress, thermal imaging, face, SVM, polygraph

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Authors: A: Annis Fathima, V. Vaidehi, S. Ajitha

Abstract:

Face recognition system finds many applications in surveillance and human computer interaction systems. As the applications using face recognition systems are of much importance and demand more accuracy, more robustness in the face recognition system is expected with less computation time. In this paper, a hybrid approach for face recognition combining Gabor Wavelet and Linear Discriminant Analysis (HGWLDA) is proposed. The normalized input grayscale image is approximated and reduced in dimension to lower the processing overhead for Gabor filters. This image is convolved with bank of Gabor filters with varying scales and orientations. LDA, a subspace analysis techniques are used to reduce the intra-class space and maximize the inter-class space. The techniques used are 2-dimensional Linear Discriminant Analysis (2D-LDA), 2-dimensional bidirectional LDA ((2D)2LDA), Weighted 2-dimensional bidirectional Linear Discriminant Analysis (Wt (2D)2 LDA). LDA reduces the feature dimension by extracting the features with greater variance. k-Nearest Neighbour (k-NN) classifier is used to classify and recognize the test image by comparing its feature with each of the training set features. The HGWLDA approach is robust against illumination conditions as the Gabor features are illumination invariant. This approach also aims at a better recognition rate using less number of features for varying expressions. The performance of the proposed HGWLDA approaches is evaluated using AT&T database, MIT-India face database and faces94 database. It is found that the proposed HGWLDA approach provides better results than the existing Gabor approach.

Keywords: face recognition, Gabor wavelet, LDA, k-NN classifier

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Authors: Kamel Zemour, Kada Mohamed Amine Chouhim, Mohamed Mairif, Tadj Eddine Adda Ardjan

Abstract:

Many recent studies show the positive effect of phenolic compounds in olive oil on health. They are known for their biological properties, where they have shown potential activity as an antioxidant, anti-inflammatory, and antimicrobial agents. However, this characteristic is rarely studied in olive oil from different regions of Algeria. Different samples collected from the western region of Algeria were evaluated for their polyphenol content, antioxidant activity, and antimicrobial effect. The obtained results demonstrated that this oil is rich in polyphenols and revealed high antimicrobial activity against Staphylococcus aureus and Escherichia coli. Finally, this study has highlighted the nutritional and pharmaceutical importance of olive oil grown in Algeria.

Keywords: olive oil, polyphenols, antioxidant activity, antimicrobial activity

Procedia PDF Downloads 108