Search results for: activity recognition

Authors: Nermeen El Kashef, Yasser Fouad, Khaled Mahar

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No AI-complete system can model the human brain or behavior, without looking at the totality of the whole situation and incorporating a combination of senses. This paper proposes a Pattern Recognition model based on Simulation of Chemical Senses (SCS) for separation and classification of sign language. The model based on human taste controlling strategy. The main idea of the introduced model is motivated by the facts that the tongue cluster input substance into its basic tastes first, and then the brain recognizes its flavor. To implement this strategy, two level architecture is proposed (this is inspired from taste system). The separation-level of the architecture focuses on hand posture cluster, while the classification-level of the architecture to recognizes the sign language. The efficiency of proposed model is demonstrated experimentally by recognizing American Sign Language (ASL) data set. The recognition accuracy obtained for numbers of ASL is 92.9 percent.

Keywords: artificial intelligence, biocybernetics, gustatory system, sign language recognition, taste sense

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Authors: Bouchenak Fatima, Lmegharbi Abdelbaki, Houssem Degaichia, Benrebiha Fatima

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The essential oil composition of the leaf of Artemisia absinthium from region of Cherchell (The south of Algeria) was investigated by GC, GC-MS. 27 constituents were identified correspond to 84, 63% of the total oil. The major components are Thujone (60, 82%), Chamazulènel (16, 62%), ρ-cymène (4, 29%) and 2-carène (4.25%). The antimicrobial activity of oil was tested in vitro by two methods (agar diffusion and microdilution) on three plant pathogenic fungi. This oil has been tested for antimicrobial activity against three pathogenic fungi (Botrytis cinerea, Fusarium culmorum and Helminthosporium Sp.).The study of activity was evaluated by two methods: Method of diffusion in gelose and the minimum inhibitory concentration MIC. This oil exhibited an interesting antimicrobial activity. A preliminary study showed that this oil presented high toxicity against this fungus. These results, although preliminary show a good antifungal activity, to limit and inhibit stop the development of those pathogen agent.

Keywords: artemisia absinthian, extraction process, chemical study, antifungal activity

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Authors: Mahta Eskandarnejad

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The aim of this paper was to compare physical activity and mental toughness in the staff and students of the University of Tabriz. 615 people participated in this study and filled demographic questionnaire, mental thoughness48 (MTQ48) questionnaire and habitual physical activity questionnaire (Baecke physical activity questionnaire). The research sample included 355 students and 260 staff (615 questionnaires). For analyzing hypotheses MANOVA, correlation and independent t-test were used. Based on the result; some subscales of mental toughness and physical activity were significantly related. The result showed the significant correlation between mental toughness and physical activity in student and no significant correlation in staff. Students were significantly physically more active than staff, and mental toughness was higher in staff. There was no difference in mental toughness variable between active participants (active staff and student). The results of this study showed that mental toughness could influence the way a person cope with living conditions. It is expected that mental toughness changes can lead to changing in levels of physical activity. It should be noted that the other variables should not be ignored.

Keywords: Baecke physical activity questionnaire, mental toughness, physical activity, university staff, university student

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Authors: P. S. BEDI* , Neavty Thakur, Balvınder Sıngh

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In the present study an attempt has been made to prepare the crude extracts of leaves and stem of ‘Girardinia heterophylla’ by using various solvents like petroleum ether, ethanol and double distilled water. The samples were given the code NGLS 1, NGLS 2, NGLS 3, NGSS 1, NGSS 2 and NGSS 3 respectively. All the extracts were used to study their antimicrobial activity against gram positive bacteria eg. Bacillus subtilis, Gram negative bacteria eg. E. coli, K. pneumonia and antifungal activity against Aspergillus niger. The results of the antimicrobial activity showed that all the crude extracts of the plant posseses antibacterial activity. Maximum antibacterial activity was shown by NGLS 2, NGLS 3 and NGSS 3 against K. pneumonia. The growth of fungus A. niger was also inhibited by all the crude extracts. Maximum inhibition was shown by NGSS 2 followed by NGSS 1.

Keywords: Girardinia heterophylla, leaves and stem extracts, Antibacterial activity, antifungal activity.

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Authors: Qiang Wang, Hongyang Yu, MingRong Lai, Miao Luo

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This paper presents a method for accurately localizing and interacting with known surface defects by overlaying patterns onto real-world surfaces using a projection system. Given the world coordinates of the defects, we project corresponding patterns onto the surfaces, providing an intuitive visualization of the specific defect locations. To enable users to interact with and retrieve more information about individual defects, we implement a gesture recognition system based on a pruned and optimized version of YOLOv6. This lightweight model achieves an accuracy of 82.8% and is suitable for deployment on low-performance devices. Our approach demonstrates the potential for enhancing defect identification, inspection processes, and user interaction in various applications.

Keywords: defect localization, projection mapping, gesture recognition, YOLOv6

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Authors: Fei Gao, Xinyang Dong, Yisu Ge, Shufang Lu, Libo Weng

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Vehicle color recognition plays an important role in traffic accident investigation. However, due to the influence of illumination, weather, and noise, vehicle color recognition still faces challenges. In this paper, a vehicle color classification network based on spatial cluster loss and channel attention mechanism (SCNet) is proposed for vehicle color recognition. A channel attention module is applied to extract the features of vehicle color representative regions and reduce the weight of nonrepresentative color regions in the channel. The proposed loss function, called spatial clustering loss (SC-loss), consists of two channel-specific components, such as a concentration component and a diversity component. The concentration component forces all feature channels belonging to the same class to be concentrated through the channel cluster. The diversity components impose additional constraints on the channels through the mean distance coefficient, making them mutually exclusive in spatial dimensions. In the comparison experiments, the proposed method can achieve state-of-the-art performance on the public datasets, VCD, and VeRi, which are 96.1% and 96.2%, respectively. In addition, the ablation experiment further proves that SC-loss can effectively improve the accuracy of vehicle color recognition.

Keywords: feature extraction, convolutional neural networks, intelligent transportation, vehicle color recognition

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Authors: Rashmi Tamhankar, Madhav Bapat

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In many projects, we have to use human judgment for determining the duration of the activities which may vary from person to person. Hence, there is vagueness about the time duration for activities in network planning. Fuzzy sets can handle such vague or imprecise concepts and has an application to such network. The vague activity times can be represented by triangular fuzzy numbers. In this paper, a general network with fuzzy activity times is considered and conditions for the critical path are obtained also we compute total float time of each activity. Several numerical examples are discussed.

Keywords: PERT, CPM, triangular fuzzy numbers, fuzzy activity times

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Authors: Xiao Yang, Ji Zhang, Yingli Song, Hui Huang, Jing Zhang, Yan Wang, Rongrong Han, Zhixuan Xiang, Lu Chen, Lingling Gao

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Aim and Objectives: To examine physical activity self-efficacy, identify its predictors, and further explore the mechanism of action among the predictors in mainland Chinese pregnant women with high risk for gestational diabetes mellitus (GDM). Background: Physical activity could protect pregnant women from developing GDM. Physical activity self-efficacy was the key predictor of physical activity. Design: A cross-sectional study was conducted from October 2021 to May 2022 in Zhengzhou, China. Methods: 252 eligible pregnant women completed the Pregnancy Physical Activity Self-efficacy Scale, the Social Support for Physical Activity Scale, the Knowledge on Physical Activity Questionnaire, the 7-item Generalized Anxiety Disorder scale, the Edinburgh Postnatal Depression Scale, and a socio-demographic data sheet. Multiple linear regression was applied to explore the predictors of physical activity self-efficacy. Structural equation modeling was used to explore the mechanism of action among the predictors. Results: Chinese pregnant women with a high risk for GDM reported a moderate level of physical activity self-efficacy. The best-fit regression analysis revealed four variables explained 17.5% of the variance in physical activity self-efficacy. Social support for physical activity was the strongest predictor, followed by knowledge of the physical activity, intention to do physical activity, and anxiety symptoms. The model analysis indicated that knowledge of physical activity could release anxiety and depressive symptoms and then increase physical activity self-efficacy. Conclusion: The present study revealed a moderate level of physical activity self-efficacy. Interventions targeting pregnant women with high risk for GDM need to include the predictors of physical activity self-efficacy. Relevance to clinical practice: To facilitate pregnant women with high risk for GDM to engage in physical activity, healthcare professionals may find assess physical activity self-efficacy and intervene as soon as possible on their first antenatal visit. Physical activity intervention programs focused on self-efficacy may be conducted in further research.

Keywords: physical activity, gestational diabetes, self-efficacy, predictors

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Authors: Marek Maruszczak

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Gamified applications have been used successfully in education for years. The rapid development of technologies such as augmented reality and image recognition increases their availability and reduces their prices. Thus, there is an increasing possibility and need for a wide use of such applications in education. The main purpose of this article is to present the effects of work on a mobile application with augmented reality, the aim of which is to motivate tourists to pay more attention to the attractions and increase the likelihood of moving from one attraction to the next while visiting the Palácio Nacional de Sintra in Portugal. Work on the application was carried out together with the employees of Parques de Sintra from 2019 to 2021. Their effect was the preparation of a mobile application using augmented reality and image recognition. The application was tested on the palace premises by both Parques de Sintra employees and tourists visiting Palácio Nacional de Sintra. The collected conclusions allowed for the formulation of good practices and guidelines that can be used when designing gamified apps for the purpose of cultural education.

Keywords: augmented reality, cultural education, gamification, image recognition, mobile games

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Authors: Zunjarrao Kadam, Vikas Minchekar

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The facial recognition is an important task in criminal investigation procedure. The security guards-constantly watching the persons-can help to identify the suspected accused. The forensic psychologists are tackled such cases in the criminal justice system. The security personnel may loss their ability to correctly identify the persons due to constant stress while performing the duty. The present study aimed at to identify the effect of experimentally induced stress on facial recognition ability of security personnel’s. For this study 50, security guards from Sangli, Miraj & Jaysingpur city of the Maharashtra States of India were recruited in the experimental study. The randomized two group design was employed to carry out the research. In the initial condition twenty identity card size photographs were shown to both groups. Afterward, artificial stress was induced in the experimental group through the difficultpuzzle-solvingtask in a limited period. In the second condition, both groups were presented earlier photographs with another additional thirty new photographs. The subjects were asked to recognize the photographs which are shown earliest. The analyzed data revealed that control group has ahighest mean score of facial recognition than experimental group. The results were discussed in the present research.

Keywords: experimentally induced stress, facial recognition, cognition, security personnel

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Authors: Redouane Tlemsani, Redouane, Belkacem Kouninef, Abdelkader Benyettou

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In this paper, our system is a Markovien system which we can see it like a Dynamic Bayesian Networks. One of the major interests of these systems resides in the complete training of the models (topology and parameters) starting from training data. The Bayesian Networks are representing models of dubious knowledge on complex phenomena. They are a union between the theory of probability and the graph theory in order to give effective tools to represent a joined probability distribution on a set of random variables. The representation of knowledge bases on description, by graphs, relations of causality existing between the variables defining the field of study. The theory of Dynamic Bayesian Networks is a generalization of the Bayesians networks to the dynamic processes. Our objective amounts finding the better structure which represents the relationships (dependencies) between the variables of a dynamic bayesian network. In applications in pattern recognition, one will carry out the fixing of the structure which obliges us to admit some strong assumptions (for example independence between some variables).

Keywords: Arabic on line character recognition, dynamic Bayesian network, pattern recognition, networks

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Authors: Jutta Hämmerle-Uhl, Georg Penn, Gerhard Pötzelsberger, Andreas Uhl

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Iris codes contain bits with different entropy. This work investigates different strategies to reduce the size of iris code templates with the aim of reducing storage requirements and computational demand in the matching process. Besides simple sub-sampling schemes, also a binary multi-resolution representation as used in the JBIG hierarchical coding mode is assessed. We find that iris code template size can be reduced significantly while maintaining recognition accuracy. Besides, we propose a two stage identification approach, using small-sized iris code templates in a pre-selection satge, and full resolution templates for final identification, which shows promising recognition behaviour.

Keywords: iris recognition, compact iris code, fast matching, best bits, pre-selection identification, two-stage identification

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Authors: S. Limpattayanate, M. Hunsom

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A comparison of activity and stability of the as-formed Pt/C, Pt-Co, and Pt-Pd/C electrocatalysts, prepared by a combined approach of impregnation and seeding, was performed. According to the activity test in a single proton exchange membrane (PEM) fuel cell, the oxygen reduction reaction (ORR) activity of the Pt-M/C electro catalyst was slightly lower than that of Pt/C. The j0.9 V and E10 mA/cm2 of the as-prepared electrocatalysts increased in the order of Pt/C>Pt-Co/C>Pt-Pd/C. However, in the medium-to-high current density region, Pt-Pd/C exhibited the best performance. With regard to their stability in a 0.5 M H2SO4 electrolyte solution, the electro chemical surface area decreased as the number of rounds of repetitive potential cycling increased due to the dissolution of the metals within the catalyst structure. For long-term measurement, Pt-Pd/C was the most stable than the other three electrocatalysts.

Keywords: ORR activity, stability, Pt-based electrocatalysts, PEM fuel cell

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Authors: Devarakonda Ramadevi, Dasari Rambabu, K. Suresh Babu, Battu Ganga Rao, Lakshmi Sirisha Kotikalapudi

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The phytochemical and biological evaluation of the whole plant of Derris scandens is belonging to the family fabaceae. The dried plant of D.scandens was procured from the tirumala. The completely dried powder of the whole plant was taken and ground to a coarse powder which was then subjected to Soxhlet extraction with hexane and chloroform successively for 36 hrs. Chloroform extract was filtered and concentrated by using rotary evaporator an about 100g extract was obtained. The chloroform extract was subjected to column chromatographed over silicagel. From the column chromatography seven compounds were isolated named as osajin, scandinone, scandenone, 4,5,7-tri hydroxy biprenyl isoflavone, derris isoflavone-A, scandenin and isoscandinone. D.scandens resulting in the isolation of seven compounds in the plant was confirmed by spectral data (1H NMR, 13C NMR, ESI-MS and FTIR). The isolated compounds were screened for antioxidant activity, antidiabetic activity, α-glucosidase (inhibitory activity) and anti-bacterial activity. The isolated seven compounds were tested for α-glucosidase inhibitory activity and antioxidant activity. All the seven compounds showed good α-glucosidase inhibitory activity and moderate antioxidant activity.

Keywords: Derris scandens, phytochemical, antioxident, antidiabetic, antibacterial activity

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Authors: Keyi Wang

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Similar to the touchscreen, hand gesture based human-computer interaction (HCI) is a technology that could allow people to perform a variety of tasks faster and more conveniently. This paper proposes a training method of an image-based hand gesture image and video clip recognition system using a CNN (Convolutional Neural Network) with a dataset. A dataset containing 6 hand gesture images is used to train a 2D CNN model. ~98% accuracy is achieved. Furthermore, a 3D CNN model is trained on a dataset containing 4 hand gesture video clips resulting in ~83% accuracy. It is demonstrated that a Cozmo robot loaded with pre-trained models is able to recognize static and dynamic hand gestures.

Keywords: deep learning, hand gesture recognition, computer vision, image processing

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Authors: P. Shrivastava, A. Shukla, K. Verma, S. Rungta

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Parkinson's disease is a chronic neurological disorder that directly affects human gait. It leads to slowness of movement, causes muscle rigidity and tremors. Gait serve as a primary outcome measure for studies aiming at early recognition of disease. Using gait techniques, this paper implements efficient binary bat algorithm for an early detection of Parkinson's disease by selecting optimal features required for classification of affected patients from others. The data of 166 people, both fit and affected is collected and optimal feature selection is done using PSO and Bat algorithm. The reduced dataset is then classified using neural network. The experiments indicate that binary bat algorithm outperforms traditional PSO and genetic algorithm and gives a fairly good recognition rate even with the reduced dataset.

Keywords: parkinson, gait, feature selection, bat algorithm

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Authors: P. Karpagam, Babu Joseph, P. Ashok Kumar

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The incidence of Candida infections is increasing worldwide. The serious nature of these infections is compounded by increasing levels of drug resistance. Pure cultures of the Candida sp. were obtained from clinical isolates and fresh garlic extracts were obtained by extraction techniques. The antifungal activity of garlic extract was investigated in an in vitro system. The extract (100%, 75% and 50%) showed significant antifungal activity against Candida, whereas, low concentration (25%) of the extract showed less antifungal activity against the test organism. Antifungal activities of honey and lemon juice were tested against the Candida; however, the growth was not inhibited by these extracts. On the other hand honey and lemon when combined with garlic exhibited a good antifungal activity. The study thus confirms the antifungal properties of garlic extract along with additives like honey and lemon have significant antifungal activity against isolates of Candida species.

Keywords: Candida, garlic extract, lemon, synergitic antifungal activity

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Authors: Dawood Al Chanti, Alice Caplier

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Sparse representations of signals have received a great deal of attention in recent years. In this paper, the interest of using sparse representation as a mean for performing sparse discriminative analysis between spontaneous facial expressions is demonstrated. An automatic facial expressions recognition system is presented. It uses a KSVD-SVM approach which is made of three main stages: A pre-processing and feature extraction stage, which solves the problem of shared subspace distribution based on the random projection theory, to obtain low dimensional discriminative and reconstructive features; A dictionary learning and sparse coding stage, which uses the KSVD model to learn discriminative under or over dictionaries for sparse coding; Finally a classification stage, which uses a SVM classifier for facial expressions recognition. Our main concern is to be able to recognize non-basic affective states and non-acted expressions. Extensive experiments on the JAFFE static acted facial expressions database but also on the DynEmo dynamic spontaneous facial expressions database exhibit very good recognition rates.

Keywords: dictionary learning, random projection, pose and spontaneous facial expression, sparse representation

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Authors: Rabab M. Ramadan, Elaraby A. Elgallad

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With extensive application, the performance of unimodal biometrics systems has to face a diversity of problems such as signal and background noise, distortion, and environment differences. Therefore, multimodal biometric systems are proposed to solve the above stated problems. This paper introduces a bimodal biometric recognition system based on the extracted features of the human palm print and iris. Palm print biometric is fairly a new evolving technology that is used to identify people by their palm features. The iris is a strong competitor together with face and fingerprints for presence in multimodal recognition systems. In this research, we introduced an algorithm to the combination of the palm and iris-extracted features using a texture-based descriptor, the Scale Invariant Feature Transform (SIFT). Since the feature sets are non-homogeneous as features of different biometric modalities are used, these features will be concatenated to form a single feature vector. Particle swarm optimization (PSO) is used as a feature selection technique to reduce the dimensionality of the feature. The proposed algorithm will be applied to the Institute of Technology of Delhi (IITD) database and its performance will be compared with various iris recognition algorithms found in the literature.

Keywords: iris recognition, particle swarm optimization, feature extraction, feature selection, palm print, the Scale Invariant Feature Transform (SIFT)

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Authors: Rima Hleiss, Elie Bitar, Mahmoud Hassan, Mohamad Khalil

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A comprehensive study of object recognition in the human brain requires combining both spatial and temporal analysis of brain activity. Here, we are mainly interested in three issues: the time perception of visual objects, the ability of discrimination between two particular categories (objects vs. animals), and the possibility to identify a particular spatial representation of visual objects. Our experiment consisted of acquiring dense electroencephalographic (EEG) signals during a picture-naming task comprising a set of objects and animals’ images. These EEG responses were recorded from nine participants. In order to determine the time perception of the presented visual stimulus, we analyzed the Event Related Potentials (ERPs) derived from the recorded EEG signals. The analysis of these signals showed that the brain perceives animals and objects with different time instants. Concerning the discrimination of the two categories, the support vector machine (SVM) was applied on the instantaneous EEG (excellent temporal resolution: on the order of millisecond) to categorize the visual stimuli into two different classes. The spatial differences between the evoked responses of the two categories were also investigated. The results showed a variation of the neural activity with the properties of the visual input. Results showed also the existence of a spatial pattern of electrodes over particular regions of the scalp in correspondence to their responses to the visual inputs.

Keywords: brain activity, categorization, dense EEG, evoked responses, spatio-temporal analysis, SVM, time perception

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Authors: N. N. Mosola, S. J. Molete, L. S. Masoebe, M. Letsae

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Hand gesture recognition is a technique used to locate, detect, and recognize a hand gesture. Detection and recognition are concepts of Artificial Intelligence (AI). AI concepts are applicable in Human Computer Interaction (HCI), Expert systems (ES), etc. Hand gesture recognition can be used in sign language interpretation. Sign language is a visual communication tool. This tool is used mostly by deaf societies and those with speech disorder. Communication barriers exist when societies with speech disorder interact with others. This research aims to build a hand recognition system for Lesotho’s Sesotho and English language interpretation. The system will help to bridge the communication problems encountered by the mentioned societies. The system has various processing modules. The modules consist of a hand detection engine, image processing engine, feature extraction, and sign recognition. Detection is a process of identifying an object. The proposed system uses Canny pruning Haar and Haarcascade detection algorithms. Canny pruning implements the Canny edge detection. This is an optimal image processing algorithm. It is used to detect edges of an object. The system employs a skin detection algorithm. The skin detection performs background subtraction, computes the convex hull, and the centroid to assist in the detection process. Recognition is a process of gesture classification. Template matching classifies each hand gesture in real-time. The system was tested using various experiments. The results obtained show that time, distance, and light are factors that affect the rate of detection and ultimately recognition. Detection rate is directly proportional to the distance of the hand from the camera. Different lighting conditions were considered. The more the light intensity, the faster the detection rate. Based on the results obtained from this research, the applied methodologies are efficient and provide a plausible solution towards a light-weight, inexpensive system which can be used for sign language interpretation.

Keywords: canny pruning, hand recognition, machine learning, skin tracking

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Authors: Abdullah A. AlShaher

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In this paper, we demonstrate how regression curves can be used to recognize 2D non-rigid handwritten shapes. Each shape is represented by a set of non-overlapping uniformly distributed landmarks. The underlying models utilize 2^nd order of polynomials to model shapes within a training set. To estimate the regression models, we need to extract the required coefficients which describe the variations for a set of shape class. Hence, a least square method is used to estimate such modes. We then proceed by training these coefficients using the apparatus Expectation Maximization algorithm. Recognition is carried out by finding the least error landmarks displacement with respect to the model curves. Handwritten isolated Arabic characters are used to evaluate our approach.

Keywords: character recognition, regression curves, handwritten Arabic letters, expectation maximization algorithm

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Authors: Thuy Bui Anh, Ngan Nguyen Thi Thanh

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Currently, social work in Vietnam is entering the first step in the development process to become a true profession with a strong position in society. However, Spirit of helping and sharing of social work has already existed in the daily life of Vietnamese people for a very long time, becoming a precious heritage passed down from ancestors to the next generations while expanding the territory, building and defending for the country. Following the stream of history, charity work in Vietnam has gradually transformed itself towards a more professional work, especially in the last 2 decades. Accordingly, more than 50 universities and educational institutions in Vietnam have been licensed to train social work, ensuring a stronger foundation on human resources working in this field. Despite the strong growth, social work profession, social work education and the recognition of the role of the social workers still need to be fueled to develop, responded to the increasing demand of Vietnam society.

Keywords: education, history, recognition, social work, Vietnam

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Authors: Suppawan Lertpongpakpoom, Anongnat Boonrat, Kunya BoontummoSuppawan

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This descriptive cost analysis aimed to analyze the unit cost of patients in medical intensive care unit. Purposive sampling was used to select 20 nurses, 6 practical nurses, 5 nurses aid and select samples 30 patients. Data were collected from both primary source (activity and average time of nursing care) and secondary source Z bill of payment and patient record). Instruments were cost recording form, activity observation form, and service recording form. Content validity of all instruments were evaluated by three experts (CVI = 0.87). Descriptive statistics was employed for data analysis. The results of the Activity-Based Costing Analysis showed that total activity cost of 4 service types for the patients was 14,776.92 Bath. The highest cost was nursing record was 5,674.78 Bath, followed direct nursing activity was 5,176.18 Bath, medical treatment was 1,976.6 Bath. The lowest cost was management activity was 1,003.64 Bath per visit. The result suggested that Activity-Base Costing Analysis could be applied to give better understanding of cost structure, enabling better consideration wasted expense and non-value-added activity, and improvement of effective utilization.

Keywords: activity-based costing, medical intensive care, nursing care, cost analysis

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Authors: Muhammad Azam, Micheal Olaolu Arowolo, Fei He, Mihail Popescu, Dong Xu

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The number of biological papers is growing quickly, which means that the number of biological pathway figures in those papers is also increasing quickly. Each pathway figure shows extensive biological information, like the names of genes and how the genes are related. However, manually annotating pathway figures takes a lot of time and work. Even though using advanced image understanding models could speed up the process of curation, these models still need to be made more accurate. To improve gene name recognition from pathway figures, we applied a Siamese network to map image segments to a library of pictures containing known genes in a similar way to person recognition from photos in many photo applications. We used a triple loss function and a triplet spatial pyramid pooling network by combining the triplet convolution neural network and the spatial pyramid pooling (TSPP-Net). We compared VGG19 and VGG16 as the Siamese network model. VGG16 achieved better performance with an accuracy of 93%, which is much higher than OCR results.

Keywords: biological pathway, image understanding, gene name recognition, object detection, Siamese network, VGG

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Authors: Ilias A. Makris

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In this work, we attempt to analyse the contribution of innovative activities to firm performance and growth. We examine economic data from some of the economies that were heavily affected by current economic crisis: the countries of southern Europe (Portugal, Italy, Greece, and Spain) and Ireland. Following literature, an appropriate econometric model is developed and several indicators are tested in order to disclose possible relation with innovative activity. Findings confirm the crucial effect of innovative process in economic activity, in firm and country level.

Keywords: Eurozone periphery, firm performance, innovative activity, R&D

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Authors: Henni Sid Ahmed, Belbachir Mohamed Faouzi, Jean Caelen

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Behavior recognition has been studied for realizing drivers assisting system and automated navigation and is an important studied field in the intelligent Building. In this paper, a recognition method of behavior recognition separated from a real image was studied. Images were divided into several categories according to the actual weather, distance and angle of view etc. SIFT was firstly used to detect key points and describe them because the SIFT (Scale Invariant Feature Transform) features were invariant to image scale and rotation and were robust to changes in the viewpoint and illumination. My goal is to develop a robust and reliable system which is composed of two fixed cameras in every room of intelligent building which are connected to a computer for acquisition of video sequences, with a program using these video sequences as inputs, we use SIFT represented different images of video sequences, and SVM (support vector machine) Lights as a programming tool for classification of images in order to classify people’s behaviors in the intelligent building in order to give maximum comfort with optimized energy consumption.

Keywords: video analysis, people behavior, intelligent building, classification

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Authors: Sanjay Bari, Vinod Ugale, Kamalkishor Patil

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Over the past several years the emergence of micro-organisms resistant to nearly all the class of antimicrobial agents has become a serious public health concern. In the present research, we report the synthesis and in-vitro antimicrobial activity of a new series of novel quinazolino-thiadiazoles 3 (a-j). The synthesized compounds were confirmed by melting point, IR, 1H-NMR, 13C NMR and Mass spectroscopy. In general, the results of the in-vitro antibacterial activity are encouraging, as out of 10 compounds tested, Compound 3f and 3i with a 4-chloro phenyl and 4-nitro phenyl at C-2 of thiadiazolyl of quinazolino-thiadiazoles, displayed the excellent antibacterial and antifungal activities against all the tested microorganisms (Bacterial and Fungal strain) with MIC values of 62.5 μg/mL. It is worth to mention that the combination of two biologically active moieties quinazoline and thiadiazole profoundly influences the biological activity. While evaluating the antimicrobial activity, it was observed that compounds having electron withdrawing groups on thiazole has shown profound activity in comparison to compounds having electron releasing groups. As a result of this study, it can be concluded that halogen substituent on thiazole ring increases antimicrobial activity. Possible improvements in the antimicrobial activity can be further achieved by slight modifications in the substituent’s and/or additional structural activity investigations to have good antimicrobial activity.

Keywords: antifungal, antimicrobial, quinazolino-thiazoles, synthesis

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Authors: M. Lamamra, H. Laouer, A. Adjaoud, Sahli Farida

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Essential oils can have significant antimicrobial activities and can successfully replace antibiotics that show their ineffectiveness against resistant germs. The chemical composition of the essential oil obtained by hydrodistillation from the aerial part of Daucus aristidis (Apiaceae) at the pre-flowering stage was investigated for the first time, by GC and GC-MS and evaluated for in vitro antimicrobial activity by the disk diffusion method. The Main components of D. aristidis oil were α-pinene (20.13%), cedrol (20.11%), and E- asarone (18.53%). The oil exhibited an antibacterial activity against almost strains tested except for Klebsiella pneumoniae ATCC 700603 K6 and Enterococcus faecalis ATCC 49452, the oil of D. aristidis had no activity against all fungi tested.

Keywords: α-pinene, antimicrobial activity, Daucus aridtidis, essential oil

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Authors: Huy Hieu Pham, Houssam Salmane, Louahdi Khoudour, Alain Crouzil, Pablo Zegers, Sergio Velastin

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We present a deep learning-based multitask framework for joint 3D human pose estimation and action recognition from color video sequences. Our approach proceeds along two stages. In the first, we run a real-time 2D pose detector to determine the precise pixel location of important key points of the body. A two-stream neural network is then designed and trained to map detected 2D keypoints into 3D poses. In the second, we deploy the Efficient Neural Architecture Search (ENAS) algorithm to find an optimal network architecture that is used for modeling the Spatio-temporal evolution of the estimated 3D poses via an image-based intermediate representation and performing action recognition. Experiments on Human3.6M, Microsoft Research Redmond (MSR) Action3D, and Stony Brook University (SBU) Kinect Interaction datasets verify the effectiveness of the proposed method on the targeted tasks. Moreover, we show that our method requires a low computational budget for training and inference.

Keywords: human action recognition, pose estimation, D-CNN, deep learning

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