Search results for: trees recognition

Authors: Antonio D. Lee, Steven X. Jiang

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A variety of techniques and methods are available to evaluate cognitive performance in Urban Search and Rescue (USAR) applications. However, traditional cognitive performance evaluation techniques typically incorporate either the conscious or systematic aspect, failing to take into consideration the subconscious or intuitive aspect. This leads to incomplete measures and produces ineffective designs. In order to fill the gaps in past research, this study developed a theoretical framework to facilitate the integration of situation awareness (SA) and intuitive pattern recognition (IPR) to enhance the cognitive performance representation in USAR applications. This framework provides guidance to integrate both SA and IPR in order to evaluate the cognitive performance of the USAR responders. The application of this framework will help improve the system design.

Keywords: cognitive performance, intuitive pattern recognition, situation awareness, urban search and rescue

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Authors: Ksheeraj Sai Vepuri, Nada Attar

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We as humans use words with accompanying visual and facial cues to communicate effectively. Classifying facial emotion using computer vision methodologies has been an active research area in the computer vision field. In this paper, we propose a simple method for facial expression recognition that enhances accuracy. We tested our method on the FER-2013 dataset that contains static images. Instead of using Histogram equalization to preprocess the dataset, we used Unsharp Mask to emphasize texture and details and sharpened the edges. We also used ImageDataGenerator from Keras library for data augmentation. Then we used Convolutional Neural Networks (CNN) model to classify the images into 7 different facial expressions, yielding an accuracy of 69.46% on the test set. Our results show that using image preprocessing such as the sharpening technique for a CNN model can improve the performance, even when the CNN model is relatively simple.

Keywords: facial expression recognittion, image preprocessing, deep learning, CNN

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Authors: H. G. C. P. Dinesh, G. Tharshini, M. P. B. Ekanayake, G. M. R. I. Godaliyadda

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This paper presents a new spatial feature extraction method based on principle component analysis (PCA) and Fisher Discernment Analysis (FDA) for facial expression recognition. It not only extracts reliable features for classification, but also reduces the feature space dimensions of pattern samples. In this method, first each gray scale image is considered in its entirety as the measurement matrix. Then, principle components (PCs) of row vectors of this matrix and variance of these row vectors along PCs are estimated. Therefore, this method would ensure the preservation of spatial information of the facial image. Afterwards, by incorporating the spectral information of the eigen-filters derived from the PCs, a feature vector was constructed, for a given image. Finally, FDA was used to define a set of basis in a reduced dimension subspace such that the optimal clustering is achieved. The method of FDA defines an inter-class scatter matrix and intra-class scatter matrix to enhance the compactness of each cluster while maximizing the distance between cluster marginal points. In order to matching the test image with the training set, a cosine similarity based Bayesian classification was used. The proposed method was tested on the Cohn-Kanade database and JAFFE database. It was observed that the proposed method which incorporates spatial information to construct an optimal feature space outperforms the standard PCA and FDA based methods.

Keywords: facial expression recognition, principle component analysis (PCA), fisher discernment analysis (FDA), eigen-filter, cosine similarity, bayesian classifier, f-measure

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Authors: Aya Idriss, Esraa Othman, Lujain Malak

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Artificial intelligence is the emulation of human intelligence processes by machines, particularly computer systems that act logically. Some of the specific applications of AI include natural language processing, speech recognition, and machine vision. Cortana is a virtual assistant and she’s an example of an AI Application. Microsoft made it possible for this app to be accessed not only on laptops and PCs but can be downloaded on mobile phones and used as a virtual assistant which was a huge success. Cortana can offer a lot apart from the basic orders such as setting alarms and marking the calendar. Its capabilities spread past that, for example, it provides us with listening to music and podcasts on the go, managing my to-do list and emails, connecting with my contacts hands-free by simply just telling the virtual assistant to call somebody, gives me instant answers and so on. A questionnaire was sent online to numerous friends and family members to perform the study, which is critical in evaluating Cortana's recognition capacity and the majority of the answers were in favor of Cortana’s capabilities. The results of the questionnaire assisted us in determining the level of Cortana's skills.

Keywords: artificial intelligence, Cortana, AI, abstract

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Authors: Fariba Ebrahimi, Mehdi Ghorbani

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Comprehensive water management considers economic, environmental, technical and social sustainability of water resources for future generations. Integrated water management implies cooperative approach and involves all stakeholders and also introduces issues to managers and decision makers. Solving these issues needs integrated and system approach according to the recognition of actors or key persons in necessary to apply cooperative management of water resources. Therefore, social network analysis can be used to demonstrate the most effective actors for environmental base decisions. The linkage of diverse sets of actors and knowledge systems across management levels and institutional boundaries often poses one of the greatest challenges in adaptive water management. Bridging stakeholder can facilitate interactions among actors in management settings by lowering the transaction costs of collaboration. This research examines how network connections between group members affect in co- management. Cohesive network structures allow groups to more effectively achieve their goals and objectives Strong; centralized leadership is a better predictor of working group success in achieving goals and objectives. Finally, geometric position of each actor was illustrated in the network. The results of the research based on between centrality index have a key and bridging actor in recognition of cooperative management of water resources in Darbandsar village and also will help managers and planners of water in the case of recognition to organization and implementation of sustainable management of water resources and water security.

Keywords: co-management, water management, social network, bridging stakeholder, darbandsar village

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Authors: Christodoulou Christos, Politis Anastasios

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The utilization of Wireless Body Area Network (WBAN) is experiencing a notable surge in popularity as a result of its widespread implementation in the field of smart health. WBANs utilize small sensors implanted within the human body to monitor and record physiological indicators. These sensors transmit the collected data to hospitals and healthcare facilities through designated access points. Bio-sensors exhibit a diverse array of shapes and sizes, and their deployment can be tailored to the condition of the individual. Multiple sensors may be strategically placed within, on, or around the human body to effectively observe, record, and transmit essential physiological indicators. These measurements serve as a basis for subsequent analysis, evaluation, and therapeutic interventions. In conjunction with physical health concerns, numerous smartwatches are engineered to employ artificial intelligence techniques for the purpose of detecting mental health conditions such as depression and anxiety. The utilization of smartwatches serves as a secure and cost-effective solution for monitoring mental health. Physiological signals are widely regarded as a highly dependable method for the recognition of emotions due to the inherent inability of individuals to deliberately influence them over extended periods of time. The techniques that WBANs employ to recognize emotions are thoroughly examined in this article.

Keywords: emotion recognition, wireless body area network, WBAN, ERC, wearable devices, psychological signals, emotion, smart-watch, prediction

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Authors: Jeffrey A. Amelse

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Many corporations aspire to become Net Zero Carbon Carbon Dioxide by 2035-2050. This paper examines what it will take to achieve those goals. Achieving Net Zero CO₂ requires an understanding of where energy is produced and consumed, the magnitude of CO₂ generation, and proper understanding of the Carbon Cycle. The latter leads to the distinction between CO₂ and biomass carbon sequestration. Short reviews are provided for prior technologies proposed for reducing CO₂ emissions from fossil fuels or substitution by renewable energy, to focus on their limitations and to show that none offer a complete solution. Of these, CO₂ sequestration is poised to have the largest impact. It will just cost money, scale-up is a huge challenge, and it will not be a complete solution. CO₂ sequestration is still in the demonstration and semi-commercial scale. Transportation accounts for only about 30% of total U.S. energy demand, and renewables account for only a small fraction of that sector. Yet, bioethanol production consumes 40% of U.S. corn crop, and biodiesel consumes 30% of U.S. soybeans. It is unrealistic to believe that biofuels can completely displace fossil fuels in the transportation market. Bioethanol is traced through its Carbon Cycle and shown to be both energy inefficient and inefficient use of biomass carbon. Both biofuels and CO₂ sequestration reduce future CO₂ emissions from continued use of fossil fuels. They will not remove CO₂ already in the atmosphere. Planting more trees has been proposed as a way to reduce atmospheric CO₂. Trees are a temporary solution. When they complete their Carbon Cycle, they die and release their carbon as CO₂ to the atmosphere. Thus, planting more trees is just 'kicking the can down the road.' The only way to permanently remove CO₂ already in the atmosphere is to break the Carbon Cycle by growing biomass from atmospheric CO₂ and sequestering biomass carbon. Sequestering tree leaves is proposed as a solution. Unlike wood, leaves have a short Carbon Cycle time constant. They renew and decompose every year. Allometric equations from the USDA indicate that theoretically, sequestrating only a fraction of the world’s tree leaves can get the world to Net Zero CO₂ without disturbing the underlying forests. How can tree leaves be permanently sequestered? It may be as simple as rethinking how landfills are designed to discourage instead of encouraging decomposition. In traditional landfills, municipal waste undergoes rapid initial aerobic decomposition to CO₂, followed by slow anaerobic decomposition to methane and CO₂. The latter can take hundreds to thousands of years. The first step in anaerobic decomposition is hydrolysis of cellulose to release sugars, which those who have worked on cellulosic ethanol know is challenging for a number of reasons. The key to permanent leaf sequestration may be keeping the landfills dry and exploiting known inhibitors for anaerobic bacteria.

Keywords: carbon dioxide, net zero, sequestration, biomass, leaves

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Authors: Silvia Moreno, Banu Iyisan, Hannes Gumz, Brigitte Voit, Dietmar Appelhans

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Polymersomes are materials which are considered as artificial counterparts of natural vesicles. The nanotechnology of such smart nanovesicles is very useful to enhance the efficiency of many therapeutic and diagnostic drugs. Those compounds show a higher stability, flexibility, and mechanical strength to the membrane compared to natural liposomes. In addition, they can be designed in detail, the permeability of the membrane can be controlled by different stimuli, and the surface can be functionalized with different biological molecules to facilitate monitoring and target. For this purpose, this study demonstrates the formation of multifunctional and pH sensitive polymersomes and their functionalization with different reactive groups or biomolecules inside and outside of polymersomes´ membrane providing by crossing the membrane and docking/undocking processes for biomedical applications. Overall, they are highly versatile and thus present new opportunities for the design of targeted and selective recognition systems, for example, in mimicking cell functions and in synthetic biology.

Keywords: multifunctionalized, pH stimulus, controllable release, cellular uptake

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Authors: Bency Jacob, S.B. Waykar

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Degradations in historical document images appear due to aging of the documents. It is very difficult to understand and retrieve text from badly degraded documents as there is variation between the document foreground and background. Thresholding of such document images either result in broken characters or detection of false texts. Numerous algorithms exist that can separate text and background efficiently in the textual regions of the document; but portions of background are mistaken as text in areas that hardly contain any text. This paper presents a way to overcome these problems by a robust binarization technique that recovers the text from a severely degraded document images and thereby increases the accuracy of optical character recognition systems. The proposed document recovery algorithm efficiently removes degradations from document images. Here we are using the ostus method ,local thresholding and global thresholding and after the binarization training and recognizing the characters in the degraded documents.

Keywords: binarization, denoising, global thresholding, local thresholding, thresholding

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Authors: Jahede Tekeykhah, Seyed Mohsen Hossini, Gholamali Jalali

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Industrial and traffic activities cause large amounts of heavy metals enter into the atmosphere and the use of plant species can be effective in assessing and reducing air pollution by metals. This study aimed to investigate the adsorption level of heavy metals in leaves of Fraxinus rotundifolia, Robinia, Platanus orientalis, Platycladus orientalis and Pinus eldarica trees in Abidar forest park. For this purpose, samples leaves of the trees were prepared from the contaminated and control areas in each region in 3 stations with 3 replicates in mid-August and finally 90 samples were sent to the laboratory. Then, the concentrations of heavy metals were measured by graphite furnace. To do this, factorial experiment based on a completely randomized design with two factors of location on two levels (contaminated area and control area) and the factor of species on five levels (Fraxinus rotundifolia, Robinia, Platanus orientalis, Platycladus orientalis and Pinus eldarica) with three replications was used. The analysis of collected data was performed by SPSS software and Duncan's multiple range test was used to compare the means. The results showed that the accumulation of all metals in the leaves of most species in the infected area with a significant difference at 95% level was higher than the control area. In the contaminated area, with a significant difference at 5% level, the highest accumulations of metals were observed as the following: lead, cadmium, zinc and manganese in Platanus orientalis, nickel in Fraxinus rotundifolia and copper in Platycladus orientalis.

Keywords: airborne, tree species, heavy metals, absorption, Abidar Forest Park

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Authors: Hafiza Javaria Ashraf, Xinghong Wang, Zhanghong Shi, Youming Hou

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Insect’s innate immunity depends on a variety of defense responses for the recognition of invading pathogens. Pathogen recognition involves particular proteins known as pattern recognition receptors (PRRs). These PRRs interact with pathogen-associated molecular patterns (PAMPs) present on the surface of pathogens to distinguish between self and non-self. C-type lectins (CTLs) belong to a superfamily of PPRs which involved in insect immunity and defense mechanism. Rhynchophorus ferrugineus Olivier is a devastating pest of Palm cultivations in China. Although studies on R. ferrugineus immune mechanism and host defense have conducted, however, the role of CTL in immune responses of R. ferrugineus remains elusive. Here, we report RfCTL, which is a secreted protein containing a single-CRD domain. The open reading frame (ORF) of CTL is 226 bp, which encodes a putative protein of 168 amino acids. Transcript expression analysis revealed that RfCTL highly expressed in immune-related tissues, i.e., hemolymph and fat body. The abundance of RfCTL in the gut and fat body dramatically increased upon Staphylococcus aureus and Escherichia coli bacterial challenges, suggesting a role in defense against gram-positive and gram-negative bacterial infection. Taken together, we inferred that RfCTL might be involved in the immune defense of R. ferrugineus and established a solid foundation for future studies on R. ferrugineus CTL domain proteins for better understanding of insect immunity.

Keywords: biological invasion, c-type lectin, insect immunity, Rhynchophorus ferrugineus Oliver

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Authors: R. Chinnaiyaprabhu, S. Bharanidharan, V. Dharsana, Rajalavanya

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The concept of the Metaverse represents a potential evolution in the realm of cyberspace. In the early stages of Web 2.0, we observed a proliferation of online pseudonyms or 'nyms,' which increased the prevalence of fake accounts and made it challenging to establish unique online identities for various roles. However, in the era of Web 3.0, particularly in the context of the Metaverse, an individual's digital identity is intrinsically linked to their real-world identity. Consequently, actions taken in the Metaverse can carry significant consequences in the physical world. In light of these considerations, we propose the development of an innovative authentication system known as 'Metasec.' This system is designed to enhance security for digital assets, online identities, avatars, and user accounts within the Metaverse. Notably, Metasec operates as a password less authentication solution, relying on a multifaceted approach to security, encompassing device attestation, facial recognition, and pattern-based security keys.

Keywords: metaverse, multifactor authentication, security, facial recognition, patten password

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Authors: Baboo Ali, Avni Ugur

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Pesticide namely, Diflubenzuron, is tremendously used in pear orchards against different insect pests of pear fruit trees in Turkey. The predatory bug, Anthocoris nemoralis (F.) is found in pear orchard feeding on Cacopsylla pyri (L.) (Homoptera: Psyllidae), is an insect pest of pear fruit trees. In this study, the effectiveness of the above mentioned pesticide on various biological stages of predatory bug were investigated under laboratory conditions of 25±1˚C, 75±5% RH, and photoperiod of 16L: 8D h. Newly emerged 1st, 2nd, 3rd, 4th and 5th instars as well as the female and male stages of the predatory bug were placed on treated petri dishes and their mortality was checked after every 24 hours till the survival of the last individual. Prey consumption of surviving instars as well as the adult stages was determined simultaneously. All biological stages of the predatory bug were fed with eggs of Ephestia kuehniella during the whole research work. Percent hatch of treated eggs was recorded after every 24 hours, and the behavioral test of the male and female stages against Diflubenzuron was also determined using Y-tube olfactometer. Consequently, the mortality rate of 1st, 2nd, 3rd, 4th, and 5th instars was 61.32 %, 67.50%, 74. 91%, 80.11%, and 83.04%, respectively. In case of male and female stages, it has been recorded as 95.47% and 95.50%, respectively. Thus, a significant difference was not found between female and male mortality rates. Prey consumption of 1st, 2nd, 3rd, 4th and 5th surviving instars was noted as 8.01, 11. 72, 13.24, 16.93 and 20.49 number of eggs/day while in females and males, it was 12.05 and 12.71 number of eggs/day, respectively. Hatching ratio of treated eggs of predator was 25.32±4.08. As far as the behavioral test is concerned, it has been indicated that Diflubenzuron has 65% repellent effect on the newly emerged male and female stages of the predatory bug while using Y-tube olfactometer under laboratory conditions.

Keywords: behavior, biological stages, diflubenzuron, effectiveness, pesticide, predatory bug

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Authors: M. F. Nawaz, S. Gul, T. H. Farooq, M. T. Siddiqui, M. Asif, I. Ahmad, N. K. Niazi

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In Pakistan, major demands of fuel wood and timber wood are fulfilled by agroforestry. However, the information regarding economic significance of agroforestry and its productivity in Pakistan is still insufficient and unreliable. Survey of field conditions to examine the agroforestry status at local level helps us to know the future trends and to formulate the policies for sustainable wood supply. The objectives of this research were to examine the actual status and potential of agroforestry and to point out the barriers that are faced by farmers in the adoption of agroforestry. Research was carried out in Chiniot district, Pakistan because it is the famous city for furniture industry that is largely dependent on farm trees. A detailed survey of district Chiniot was carried out from 150 randomly selected farmer respondents using multi-objective oriented and pre-tested questionnaire. It was found that linear tree planting method was more adopted (45%) as compared to linear + interplanting (42%) and/or compact planting (12.6%). Chi-square values at P-value <0.5 showed that age (11.35) and education (17.09) were two more important factors in the quick adoption of agroforestry as compared to land holdings (P-value of 0.7). The major reason of agroforestry adoption was to obtain income, fodder and fuelwood. The most dominant species in farmlands was shisham (Dalbergia sissoo) but since last five years, mostly farmers were growing Sufeida (Eucalyptus camaldulensis), kikar (Acacia nilotica) and popular (Populus deltoides) on their fields due to “Shisham die-back” problem. It was found that agro-forestry can be increased by providing good quality planting material to farmers and improving wood markets.

Keywords: agroforestry, trees, services, agriculture, farmers

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Authors: Sonali Banrjee, Swarup Kumar Mitra, Aritra Acharyya

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A piano note recognition method has been proposed by the authors in this paper. The authors have used a comprehensive method for onset detection of each note present in a piano piece followed by segmented short-time Fourier transform (STFT) for the identification of piano notes. The performance evaluation of the proposed method has been carried out in different harsh noisy environments by adding different levels of additive white Gaussian noise (AWGN) having different signal-to-noise ratio (SNR) in the original signal and evaluating the note detection error rate (NDER) of different piano pieces consisting of different number of notes at different SNR levels. The NDER is found to be remained within 15% for all piano pieces under consideration when the SNR is kept above 8 dB.

Keywords: AWGN, onset detection, piano note, STFT

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Authors: S.K. Bhandari, E. Veneklaas, L. McCaw, R. Mazanec, K. Whitford, M. Renton

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Eucalyptus marginata, E. diversicolor and Corymbia calophylla form widespread forests in south-west Western Australia (SWWA). These forests have economic and ecological importance, and therefore, tree growth and sustainable management are of high priority. This paper aimed to analyse and model the growth of these species at both stand and individual levels, but this presentation will focus on predicting the growth of E. Marginata at the individual tree level. More specifically, the study wanted to investigate how well individual E. marginata tree growth could be predicted by considering the diameter and height of the tree at the start of the growth period, and whether this prediction could be improved by also accounting for the competition from neighbouring trees in different ways. The study also wanted to investigate how many neighbouring trees or what neighbourhood distance needed to be considered when accounting for competition. To achieve this aim, the Pearson correlation coefficient was examined among competition indices (CIs), between CIs and dbh growth, and selected the competition index that can best predict the diameter growth of individual trees of E. marginata forest managed under different thinning regimes at Inglehope in SWWA. Furthermore, individual tree growth models were developed using simple linear regression, multiple linear regression, and linear mixed effect modelling approaches. Individual tree growth models were developed for thinned and unthinned stand separately. The developed models were validated using two approaches. In the first approach, models were validated using a subset of data that was not used in model fitting. In the second approach, the model of the one growth period was validated with the data of another growth period. Tree size (diameter and height) was a significant predictor of growth. This prediction was improved when the competition was included in the model. The fit statistic (coefficient of determination) of the model ranged from 0.31 to 0.68. The model with spatial competition indices validated as being more accurate than with non-spatial indices. The model prediction can be optimized if 10 to 15 competitors (by number) or competitors within ~10 m (by distance) from the base of the subject tree are included in the model, which can reduce the time and cost of collecting the information about the competitors. As competition from neighbours was a significant predictor with a negative effect on growth, it is recommended including neighbourhood competition when predicting growth and considering thinning treatments to minimize the effect of competition on growth. These model approaches are likely to be useful tools for the conservations and sustainable management of forests of E. marginata in SWWA. As a next step in optimizing the number and distance of competitors, further studies in larger size plots and with a larger number of plots than those used in the present study are recommended.

Keywords: competition, growth, model, thinning

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Authors: Juliana Amaka Ugwu

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Trapping efficiency of methyl eugenol and three locally made food-based lures were evaluated in three locations for trapping of B. dorsalis on mango homestead trees in Ibadan South west Nigeria. The treatments were methyl eugenol, brewery waste, pineapple juice, orange juice, and control (water). The experiment was laid in a Complete Randomized Block Design (CRBD) and replicated three times in each location. Data collected were subjected to analysis of variance and significant means were separated by Turkey’s test. The results showed that B. dorsalis was recorded in all locations of study. Methyl eugenol significantly (P < 0.05) trapped higher population of B. dorsalis in all the study area. The population density of B. dorsalis was highest during the ripening period of mango in all locations. The percentage trapped flies after 7 weeks were 77.85%-82.38% (methyl eugenol), 7.29%-8.64% (pineapple juice), 5.62-7.62% (brewery waste), 4.41%-5.95% (orange juice), and 0.24-0.47% (control). There were no significance differences (p > 0.05) on the population of B. dorsalis trapped in all locations. Similarly, there were no significant differences (p > 0.05) on the population of flies trapped among the food attractants. However, the three food attractants significantly (p < 0.05) trapped higher flies than control. Methyl eugenol trapped only male flies while brewery waste and other food based attractants trapped both male and female flies. The food baits tested were promising attractants for trapping B. dorsalis on mango homestead tress, hence increased dosage could be considered for monitoring and mass trapping as management strategies against fruit fly infestation.

Keywords: attractants, trapping, mango, Bactrocera dorsalis

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Authors: Sara Hasani

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This research focuses on natural sudden onset disasters characterised as ‘occurring with little or no warning and often cause excessive injuries far surpassing the national response capacities’. Based on the panel analysis of the historic record of 4,252 natural onset disasters between 1980 to 2015, a predictive method was developed to predict the human impact of the disaster (fatality, injured, homeless) with less than 3% of errors. The geographical dispersion of the disasters includes every country where the data were available and cross-examined from various humanitarian sources. The records were then filtered into 4252 records of the disasters where the five predictive variables (disaster type, HDI, DRI, population, and population density) were clearly stated. The procedure was designed based on a combination of pattern recognition techniques and rule-based clustering for prediction and discrimination analysis to validate the results further. The result indicates that there is a relationship between the disaster human impact and the five socio-economic characteristics of the affected country mentioned above. As a result, a framework was put forward, which could predict the disaster’s human impact based on their severity rank in the early hours of disaster strike. The predictions in this model were outlined in two worst and best-case scenarios, which respectively inform the lower range and higher range of the prediction. A necessity to develop the predictive framework can be highlighted by noticing that despite the existing research in literature, a framework for predicting the human impact and estimating the needs at the time of the disaster is yet to be developed. This can further be used to allocate the resources at the response phase of the disaster where the data is scarce.

Keywords: disaster management, natural disaster, pattern recognition, prediction

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Authors: Hong Im Shin

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Previous research demonstrated that self-objectification (women view themselves as objects for use) is related to system-justification. Three studies investigated whether cultural disposition as its system-justifying function could have an impact on self-objectification and dehumanization of sexualized women and men. Study 1 (N = 91) employed a survey methodology to examine the relationship between cultural disposition (collectivism vs. individualism), trait of system-justification, and self-objectification. The results showed that the higher tendency of collectivism was related to stronger system-justification and self-objectification. Study 2 (N = 60 females) introduced a single category implicit association task (SC-IAT) to assess the extent to which sexually objectified women were associated with uniquely human attributes (i.e., culture) compared to animal-related attributes (i.e., nature). According to results, female participants associated sexually objectified female targets less with human attributes compared to animal-related attributes. Study 3 (N = 46) investigated whether priming to individualism or collectivism was associated to system justification and sexual objectification of men and women with the use of a recognition task involving upright and inverted pictures of sexualized women and men. The results indicated that the female participants primed to individualism showed an inversion effect for sexualized women and men (person-like recognition), whereas there was no inversion effect for sexualized women in the priming condition of collectivism (object-like recognition). This implies that cultural disposition plays a mediating role for rationalizing the gender status, implicit dehumanization of sexualized females and self-objectification. Future research directions are discussed.

Keywords: cultural disposition, dehumanization, implicit test, self-objectification

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Authors: Bahaa Eltahawy, Mikko Ylihärsilä, Reino Virrankoski, Esko Petäjä

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Sheet metal processing is automated, but the step from product models to the production machine control still requires human intervention. This may cause time consuming bottlenecks in the production process and increase the risk of human errors. In this paper we present a system, which automatically recognizes features from the CAD-model of the sheet metal product. By using these features, the system produces a complete model of the particular sheet metal product. Then the model is used as an input for the sheet metal processing machine. Currently the system is implemented, capable to recognize more than 11 of the most common sheet metal structural features, and the procedure is fully automated. This provides remarkable savings in the production time, and protects against the human errors. This paper presents the developed system architecture, applied algorithms and system software implementation and testing.

Keywords: feature recognition, automation, sheet metal manufacturing, CAD, CAM

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Authors: F. Ainlhout, S. Boutaleb, M. C. Diaz-Barradas, M. Zunzunegui

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Argania spinosa is an endemic tree of the southwest of Morocco, occupying 828,000 Ha, distributed mainly between Mediterranean vegetation and the desert. This tree can grow in extremely arid regions in Morocco, where annual rainfall ranges between 100-300 mm where no other tree species can live. It has been designated as a UNESCO Biosphere reserve since 1998. Argania tree is of great importance in human and animal feeding of rural population as well as for oil production, it is considered as a multi-usage tree. Admine forest located in the suburbs of Agadir city, 5 km inland, was selected to conduct this work. The aim of the study was to investigate the temporal variation in root-zone moisture dynamic in response to variation in climatic conditions and vegetation water uptake, using a geophysical technique called Electrical resistivity imaging (ERI). This technique discriminates resistive woody roots, dry and moisture soil. Time-dependent measurements (from April till July) of resistivity sections were performed along the surface transect (94 m Length) at 2 m fixed electrode spacing. Transect included eight Argan trees. The interactions between the tree and soil moisture were estimated by following the tree water status variations accompanying the soil moisture deficit. For that purpose we measured midday leaf water potential and relative water content during each sampling day, and for the eight trees. The first results showed that ERI can be used to accurately quantify the spatiotemporal distribution of root-zone moisture content and woody root. The section obtained shows three different layers: middle conductive one (moistured); a moderately resistive layer corresponding to relatively dry soil (calcareous formation with intercalation of marly strata) on top, this layer is interspersed by very resistant layer corresponding to woody roots. Below the conductive layer, we find the moderately resistive layer. We note that throughout the experiment, there was a continuous decrease in soil moisture at the different layers. With the ERI, we can clearly estimate the depth of the woody roots, which does not exceed 4 meters. In previous work on the same species, analyzing the δ18O in water of xylem and in the range of possible water sources, we argued that rain is the main water source in winter and spring, but not in summer, trees are not exploiting deep water from the aquifer as the popular assessment, instead of this they are using soil water at few meter depth. The results of the present work confirm the idea that the roots of Argania spinosa are not growing very deep.

Keywords: Argania spinosa, electrical resistivity imaging, root system, soil moisture

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Authors: Hadri Hussain, M.Nasir Ibrahim, Chee-Ming Ting, Mariani Idroas, Fuad Numan, Alias Mohd Noor

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A biometric security system has become an important application in client identification and verification system. A conventional biometric system is normally based on unimodal biometric that depends on either behavioural or physiological information for authentication purposes. The behavioural biometric depends on human body biometric signal (such as speech) and biosignal biometric (such as electrocardiogram (ECG) and phonocardiogram or heart sound (HS)). The speech signal is commonly used in a recognition system in biometric, while the ECG and the HS have been used to identify a person’s diseases uniquely related to its cluster. However, the conventional biometric system is liable to spoof attack that will affect the performance of the system. Therefore, a multimodal biometric security system is developed, which is based on biometric signal of ECG, HS, and speech. The biosignal data involved in the biometric system is initially segmented, with each segment Mel Frequency Cepstral Coefficients (MFCC) method is exploited for extracting the feature. The Hidden Markov Model (HMM) is used to model the client and to classify the unknown input with respect to the modal. The recognition system involved training and testing session that is known as client identification (CID). In this project, twenty clients are tested with the developed system. The best overall performance at 44 kHz was 93.92% for ECG and the worst overall performance was ECG at 88.47%. The results were compared to the best overall performance at 44 kHz for (20clients) to increment of clients, which was 90.00% for HS and the worst overall performance falls at ECG at 79.91%. It can be concluded that the difference multimodal biometric has a substantial effect on performance of the biometric system and with the increment of data, even with higher frequency sampling, the performance still decreased slightly as predicted.

Keywords: electrocardiogram, phonocardiogram, hidden markov model, mel frequency cepstral coeffiecients, client identification

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Authors: Chien-Hung Chen, Yan-Chun Hsing, Yung-Chun Chang

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The Internet has grown into a powerful medium for information dispersion and social interaction that leads to a rapid growth of social media which allows users to easily post their emotions and perspectives regarding certain topics online. Our research aims at using natural language processing and text mining techniques to explore the public emotions expressed on Twitter by analyzing the sentiment behind tweets. In this paper, we propose a composite kernel method that integrates tree kernel with the linear kernel to simultaneously exploit both the tree representation and the distributed emotion keyword representation to analyze the syntactic and content information in tweets. The experiment results demonstrate that our method can effectively detect public emotion of tweets while outperforming the other compared methods.

Keywords: emotion recognition, natural language processing, composite kernel, sentiment analysis, text mining

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Authors: Saad Alkentar, Abdulkareem Assalem

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Recognizing small targets has always posed a significant challenge in image analysis. Over long distances, the image signal-to-noise ratio tends to be low, limiting the amount of useful information available to detection systems. Consequently, visual target recognition becomes an intricate task to tackle. In this study, we introduce a Track Before Detect (TBD) approach that leverages target trajectory information (coordinates) to effectively distinguish between noise and potential targets. By reframing the problem as a multivariate time series classification, we have achieved remarkable results. Specifically, our TBD method achieves an impressive 97% accuracy in separating target signals from noise within a mere half-second time span (consisting of 10 data points). Furthermore, when classifying the identified targets into our predefined categories—airplane, drone, and bird—we achieve an outstanding classification accuracy of 96% over a more extended period of 1.5 seconds (comprising 30 data points).

Keywords: small targets, drones, trajectory information, TBD, multivariate time series

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Authors: P. S. Dhananjaya Swamy, B. Chinnappa, G. B. Ramesh, Naveen P. Kumar

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Kodagu district is one of the most densely forested districts in the India as around sixty five per cent of geographical areas under tree cover. Nearly 53 per cent of the flora of Kodagu is endemic. The district is also a hotspot of endemic orchids found mainly in the Thadiandamol. Shade grown, eco-friendly coffee farms are perhaps a selected few places on this planet where nature runs wild. The Kodagu accounts for more than 8.8 per cent of floral diversity of Karnataka state. Estimation of unit cost of cultivation plays a vital role in determining the governmental program their market intervention policies. On an average, planters incurred around Rs. 17041 per acre. The extent of production risk was highest among small category of planters (66 %) compared to other two exhibiting production instability. The result shows that, the coffee productivity in medium plantations was 1051.2 kg per acre as against 758.5 and 789.2 kg in the case of small and large plantations. An annual net return per acre was highest in the case of medium planters (Rs. 26109.3) as against Rs. 20566.7 and Rs. 18572.7 in the case of small and large planters. Cost of production was lowest in the case of small planters (Rs. 18.9 per kg of output) followed by medium planters (Rs. 21.2 per kg of output) and large planters (Rs. 22.5 per kg of output). The productivity of coffee is less whenever it is grown under high shade and native tree cover; it is around 6 quintals per acre when compared with low shade conditions, which is around 8.9 quintals per acre, without a significant difference in the amount invested for growing coffee. Net gain was lower by Rs. 15.5 per kg for the planters growing under high shade and native trees cover when compared with low shade and exotic trees cover.

Keywords: coffee, cultivation, economics, Kodagu

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Authors: LuoJiaoyang, Yu Hongyang

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In this paper, we experimented with a new approach to multimodal identification using RGB, RGB-D and voice data. The multimodal combination of RGB and voice data has been applied in tasks such as emotion recognition and has shown good results and stability, and it is also the same in identity recognition tasks. We believe that the data of different modalities can enhance the effect of the model through mutual reinforcement. We try to increase the three modalities on the basis of the dual modalities and try to improve the effectiveness of the network by increasing the number of modalities. We also implemented the single-modal identification system separately, tested the data of these different modalities under clean and noisy conditions, and compared the performance with the multimodal model. In the process of designing the multimodal model, we tried a variety of different fusion strategies and finally chose the fusion method with the best performance. The experimental results show that the performance of the multimodal system is better than that of the single modality, especially in dealing with noise, and the multimodal system can achieve an average improvement of 5%.

Keywords: multimodal, three modalities, RGB-D, identity verification

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Authors: Mustafa Var, Yasin Kültiğin Yaman, Elif Berna Var, Müberra Pulatkan

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This study deals with real application of an urban transformation project in Trabzon, Turkey. It aims to highlight the significance of using native species in terms of planting design of transformation projects which will also promote sustainability of urban identity. Urban identity is a phenomenon shaped not only by physical, but also by natural, spatial, social, historical and cultural factors. Urban areas face with continuous change which can be whether positive or negative way. If it occurs in a negative way that may have some destructive effects on urban identity. To solve this problematic issue, urban renewal movements initally started after 1840s around the world especially in the cities with ports. This process later followed by the places where people suffered a lot from fires and has expanded to all over the world. In Turkey, those processes have been experienced mostly after 1980s as country experienced the worst effects of unplanned urbanization especially in 1950-1990 period. Also old squares, streets, meeting points, green areas, Ottoman bazaars have changed slowly. This change was resulted in alienation of inhabitants to their environments. As a solution, several actions were taken like Mass Housing Laws which was enacted in 1981 and 1984 or urban transformation projects. Although projects between 1990-2000 were tried to satisfy the expectations of local inhabitants by the help of several design solutions to promote cultural identity; unfortunately those modern projects has also been resulted in alienation of urban environments to the inhabitants. Those projects were initially done by TOKI (Housing Development Administration of Turkey) and later followed by the Ministry of Environment and Urbanization after 2011. Although they had significant potentials to create healthy urban environments, they could not use this opportunity in an effective way. The reason for their failure is that their architectural styles and planting designs are unrespectful to local identity and environments. Generally, it can be said that the most of the urban transformation projects implementing in Turkey nearly have no concerns about the locality. However, those projects can be used as a positive tool for enhanching the urban identity of cities by means of local planting material. For instance, Kyoto can be identified by Japanese Maple trees or Seattle can be specified by Dahlia. In the same way, in Turkey, Istanbul city can be identified by Judas and Stone Pine trees or Giresun city can be identified by Cherry trees. Thus, in this paper, the importance of conserving urban identity is discussed specificly with the help of using local planting elements. After revealing the mistakes that are made during urban transformation projects, the techniques and design criterias for preserving and promoting urban identity are examined. In the end, it is emphasized that every city should have their own original, local character and specific planting design which can be used for highlighting its identity as well as architectural elements.

Keywords: urban identity, urban transformation, planting design, landscape architecture

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Authors: P. M. Sreekanth

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Teak (Tectona grandis L. f.) is a tree species indigenous to India and other Southeastern countries. It produces high-value timber and is easily established in plantations. Reforestation requires a constant supply of high quality seeds. Seed Production Areas (SPA) of teak are improved stands used for collection of open-pollinated quality seeds in large quantities. Information on the genetic diversity of major teak SPAs in India is scanty. The genetic structure of four important seed production areas of Kerala State in Southern India was analyzed employing amplified fragment length polymorphism markers using ten selective primer combinations on 80 samples (4 populations X 20 trees). The study revealed that the gene diversity of the SPAs varied from 0.169 (Konni SPA) to 0.203 (Wayanad SPA). The percentage of polymorphic loci ranged from 74.42 (Parambikulam SPA) to 84.06 (Konni SPA). The mean total gene diversity index (HT) of all the four SPAs was 0.2296 ±0.02. A high proportion of genetic diversity was observed within the populations (83%) while diversity between populations was lower (17%) (GST = 0.17). Principal coordinate analysis and STRUCTURE analysis of the genotypes indicated that the pattern of clustering was in accordance with the origin and geographic location of SPAs, indicating specific identity of each population. A UPGMA dendrogram was prepared and showed that all the twenty samples from each of Konni and Parambikulam SPAs clustered into two separate groups, respectively. However, five Nilambur genotypes and one Wayanad genotype intruded into the Konni cluster. The higher gene flow estimated (Nm = 2.4) reflected the inclusion of Konni origin planting stock in the Nilambur and Wayanad plantations. Evidence for population structure investigated using 3D Principal Coordinate Analysis of FAMD software 1.30 indicated that the pattern of clustering was in accordance with the origin of SPAs. The present study showed that assessment of genetic diversity in seed production plantations can be achieved using AFLP markers. The AFLP fingerprinting was also capable of identifying the geographical origin of planting stock and there by revealing the occurrence of the errors in genotype labeling. Molecular marker-based selective culling of genetically similar trees from a stand so as to increase the genetic base of seed production areas could be a new proposition to improve quality of seeds required for raising commercial plantations of teak. The technique can also be used to assess the genetic diversity status of plus trees within provenances during their selection for raising clonal seed orchards for assuring the quality of seeds available for raising future plantations.

Keywords: AFLP, genetic structure, spa, teak

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Authors: Agoston Restas

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Introduction: In case of managing disasters decision makers can face many times such a special situation where any pre-sign of the drastically change is missing therefore the improvised decision making can be required. The complexity, ambiguity, uncertainty or the volatility of the situation can require many times the improvisation as decision making. It can be taken at any level of the management (strategic, operational and tactical) but at tactical level the main reason of the improvisation is surely time pressure. It is certainly the biggest problem during the management. Methods: The author used different tools and methods to achieve his goals; one of them was the study of the relevant literature, the other one was his own experience as a firefighting manager. Other results come from two surveys that are referred to; one of them was an essay analysis, the second one was a word association test, specially created for the research. Results and discussion: This article proves that, in certain situations, the multi-criteria, evaluating decision-making processes simply cannot be used or only in a limited manner. However, it can be seen that managers, directors or commanders are many times in situations that simply cannot be ignored when making decisions which should be made in a short time. The functional background of decisions made in a short time, their mechanism, which is different from the conventional, was studied lately and this special decision procedure was given the name recognition-primed decision. In the article, author illustrates the limits of the possibilities of analytical decision-making, presents the general operating mechanism of recognition-primed decision-making, elaborates on its special model relevant to managers at tactical level, as well as explore and systemize the factors that facilitate (catalyze) the processes with an example with fire managers.

Keywords: decision making, disaster managers, recognition primed decision, model for making decisions in emergencies

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Authors: Saikiran Subbagari, Avinash Malladhi

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Anti Money Laundering (AML) regulations are designed to prevent money laundering and terrorist financing activities worldwide. Financial institutions around the world are legally obligated to identify, assess and mitigate the risks associated with money laundering and report any suspicious transactions to governing authorities. With increasing volumes of data to analyze, financial institutions seek to automate their AML processes. In the rise of financial crimes, optical character recognition (OCR), in combination with machine learning (ML) algorithms, serves as a crucial tool for automating AML processes by extracting the data from documents and identifying suspicious transactions. In this paper, we examine the utilization of OCR for AML and delve into various OCR techniques employed in AML processes. These techniques encompass template-based, feature-based, neural network-based, natural language processing (NLP), hidden markov models (HMMs), conditional random fields (CRFs), binarizations, pattern matching and stroke width transform (SWT). We evaluate each technique, discussing their strengths and constraints. Also, we emphasize on how OCR can improve the accuracy of customer identity verification by comparing the extracted text with the office of foreign assets control (OFAC) watchlist. We will also discuss how OCR helps to overcome language barriers in AML compliance. We also address the implementation challenges that OCR-based AML systems may face and offer recommendations for financial institutions based on the data from previous research studies, which illustrate the effectiveness of OCR-based AML.

Keywords: anti-money laundering, compliance, financial crimes, fraud detection, machine learning, optical character recognition

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