Search results for: automatic image colorization

Authors: Bryan Christian Baybay, M. Francesca Ronario

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This study is intended to answer the question “What are the communication styles of selected Filipino gays in breaking their silence on their sexual orientation to their parents?” In this regard, six cases of Filipino gay disclosures were examined through in-depth interviews. The participants were selected through purposive sampling and snowball technique. The theories, Rhetorical Sensitivity of Roderick Hart and Communicator Style of Robert Norton were used to analyze the gathered data and to give support to the communication attitudes, message processing, message rendering and communication styles exhibited in each disclosure. As secondary data and validation, parents and experts in the field of communication, sociology, and psychology were also interviewed and consulted. The study found that Filipino gays vary in the communication styles they use during the disclosure with their parents. All communication styles: impression-leaving, contentious, open, dramatic, dominant, precise, relaxed, friendly, animated, and communicator image were observed by the gays depending on their motivation, relationship and thoughts contemplated. These results lend ideas for future researchers to look into the communication patterns and/or styles of lesbians, bisexuals, transgenders and queers or expand researches on the same subject and the utilization of Social Judgment and Relational Dialectics theories in determining and analyzing LGBTQ communication.

Keywords: communication attitudes, communication styles, Filipino gays, self-disclosure, sexual orientation

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Authors: Sunita Singh, Rajani Srivastava

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For natural resource management and in other applications about earth observation revolutionary remote sensing technology plays a significant role. One of such application in monitoring and classification of crop types at spatial and temporal scale, as it provides latest, most precise and cost-effective information. Present study emphasizes the use of three different vegetation indices of Rapid Eye imagery on crop type classification. It also analyzed the effect of each indices on classification accuracy. Rapid Eye imagery is highly demanded and preferred for agricultural and forestry sectors as it has red-edge and NIR bands. The three indices used in this study were: the Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Red Edge Index (NDRE) and all of these incorporated the Red Edge band. The study area is Varanasi district of Uttar Pradesh, India and Radial Basis Function (RBF) kernel was used here for the Support Vector Machines (SVMs) classification. Classification was performed with these three vegetation indices. The contribution of each indices on image classification accuracy was also tested with single band classification. Highest classification accuracy of 85% was obtained using three vegetation indices. The study concluded that NDRE has the highest contribution on classification accuracy compared to the other vegetation indices and the Rapid Eye imagery can get satisfactory results of classification accuracy without original bands.

Keywords: GNDVI, NDRE, NDVI, rapid eye, vegetation indices

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Authors: Emmanuel Ophel Gilbert, Williams Speret

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The control for fluid flow behaviours of viscous fluids and heat transfer occurrences within heated mini-channel is considered. Heat transfer and flow characteristics of different viscous liquids, such as engine oil, automatic transmission fluid, one-half ethylene glycol, and deionized water were numerically analyzed. Some mathematical applications such as Fourier series and Laplace Z-Transforms were employed to ascertain the behaviour-wave like structure of these each viscous fluids. The steady, laminar flow and heat transfer equations are reckoned by the aid of numerical simulation technique. Further, this numerical simulation technique is endorsed by using the accessible practical values in comparison with the anticipated local thermal resistances. However, the roughness of this mini-channel that is one of the physical limitations was also predicted in this study. This affects the frictional factor. When an additive such as tetracycline was introduced in the fluid, the heat input was lowered, and this caused pro rata effect on the minor and major frictional losses, mostly at a very minute Reynolds number circa 60-80. At this ascertained lower value of Reynolds numbers, there exists decrease in the viscosity and minute frictional losses as a result of the temperature of these viscous liquids been increased. It is inferred that the three equations and models are identified which supported the numerical simulation via interpolation and integration of the variables extended to the walls of the mini-channel, yields the utmost reliance for engineering and technology calculations for turbulence impacting jets in the near imminent age. Out of reasoning with a true equation that could support this control for the fluid flow, Navier-stokes equations were found to tangential to this finding. Though, other physical factors with respect to these Navier-stokes equations are required to be checkmated to avoid uncertain turbulence of the fluid flow. This paradox is resolved within the framework of continuum mechanics using the classical slip condition and an iteration scheme via numerical simulation method that takes into account certain terms in the full Navier-Stokes equations. However, this resulted in dropping out in the approximation of certain assumptions. Concrete questions raised in the main body of the work are sightseen further in the appendices.

Keywords: frictional losses, heat transfer, laminar flow, mini-channel, number simulation, Reynolds number, turbulence, viscous fluids

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Authors: F. N. Bolu-Steve, T. A. Ajiboye

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A study was carried out on the awareness of counselling needs of psychiatric patients as perceived by medical personnel in the Federal Neuropsychiatric hospital, Aro, Abeokuta, Nigeria. The respondents comprised of medical personnel of the Neuropsychiatric hospital in Aro. Purposive sampling technique was used to select the respondents. The target population of the study consisted of all medical doctors treating the psychiatric patients. A total of 200 respondents participated in the study out of which 143 were males and 57 of them were females. With their years of experience as a medical doctors, 49.5% of them have worked between 1-5 years, 30.5% of the respondents have 6-10 years’ experience while those with 16 years and above experience are 7.0%. The major counselling need of psychiatric patients as expressed by medical doctors is the need to have information about the right balance diet. The data were analyzed using percentages, mean, frequency, Analysis of Variance (ANOVA) and t-test statistical tools. The instrument used for data collection was the structured questionnaire titled “Counselling Needs of Psychiatric Patients Questionnaire” (CNPPQ). This instrument was drafted by the researchers through the review of related literature. The reliability of the instrument was established using test-retest method. A reliability index of 0.74 was obtained. Three of the hypotheses were rejected while two of them were accepted at 0.05 alpha level of significance. Based on the findings of the study, it was recommended that broad based counselling services should be provided to psychiatric patients in order to assist them to develop positive self- image and to cope with their challenges.

Keywords: counselling, needs, psychiatric, medical personnel, patients

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Authors: Raymond Limen Njinga, Adeneye Samuel Olaolu, Akinyode Ojumoola Ajimo

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Introduction: Measurement of entrance dose and dose at different depths is essential to avoid overdose and underdose of patients. The aim of this study is to verify the variation in the absorbed dose using a water-equivalent material. Materials and Methods: The plastic phantom was arranged on the couch of the halcyon linear accelerator by Varian, with the farmer ionization chamber inserted and connected to the electrometer. The image of the setup was taken using the High-Quality Single 1280x1280x16 higher on the service mode to check the alignment with the isocenter. The beam quality TPR₂₀,₁₀ (Tissue phantom ratio) was done to check the beam quality of the machine at a field size of 10 cm x 10 cm. The calibration was done using SAD type set-up at a depth of 5 cm. This process was repeated for ten consecutive weeks, and the values were recorded. Results: The results of the beam output for the teletherapy machine were satisfactory and accepted in comparison with the commissioned measurement of 0.62. The beam quality TPR₂₀,₁₀ (Tissue phantom ratio) was reasonable with respect to the beam quality of the machine at a field size of 10 cm x 10 cm. Conclusion: The results of the beam quality and the absorbed dose rate showed a good consistency over the period of ten weeks with the commissioned measurement value.

Keywords: linear accelerator, absorbed dose rate, isocenter, phantom, ionization chamber

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Authors: Norbert Csizmadia

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The paper is based on a thorough investigation regarding the recent global, social and geographical processes. The ‘Geofusion’ book series by the author guides the readers with the help of newly illustrated “associative” geographic maps of the global world in the 21st century through the quest for the winning nations, communities, leaders and powers of this age. Hence, the above mentioned represent the research objectives, the preliminary findings of which are presented in this paper. The most significant recognition is that scientists who are recognized as explorers, geostrategists of this century, in this case, are expected to present guidelines for our new world full of global social and economic challenges. To do so, new maps are needed which do not miss the wisdom and tools of the old but complement them with the new structure of knowledge. Using the lately discovered geographic and economic interrelations, the study behind this presentation tries to give a prognosis of the global processes. The methodology applied contains the survey and analysis of many recent publications worldwide regarding geostrategic, cultural, geographical, social, and economic surveys structured into global networks. In conclusion, the author presents the result of the study, which is a collage of the global map of the 21st century as mentioned above, and it can be considered as a potential contribution to the recent scientific literature on the topic. In summary, this paper displays the results of several-year-long research giving the audience an image of how economic navigation tools can help investors, politicians and travelers to get along in the changing new world.

Keywords: geography, economic geography, geo-fusion, geostrategy

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Authors: Nartan Rajpriya

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City is a multiplex ecological system made up of social, economic and natural sub systems. Green space system is the foundation of the natural system. It is also suitable part of natural productivity in the urban structure. It is dispensable for constructing a high quality human settlements and a high standard ecocity. Ahmedabad is the fastest growing city of India. Today urban green space is under strong pressure in Ahmedabad city. Due to increasing urbanization, combined with a spatial planning policy of densification, more people face the prospect of living in less green residential environments. In this research analyzes the importance of available Green Space at Bodakdev Park, Ahmedabad, using remote sensing and GIS technologies. High resolution IKONOS image and LISS IV data has been used in this project. This research answers the questions like: • Temporal changes in urban green space area. • Proximity to heavy traffic or roads or any recreational facilities. • Importance in terms of health. • Availability of quality infrastructure. • Available green space per area, per sq. km and per total population. This projects incorporates softwares like ArcGIS, Ecognition and ERDAS Imagine, GPS technologies etc. Methodology includes the field work and collection of other relevant data while preparation of land use maps using the IKONOS imagery which is corrected using GPS.

Keywords: urban green space, ecocity, IKONOS, LISS IV

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Authors: Tolulope Aremu

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This paper is based on the idea of using deep learning methodology for optimizing production yield by tuning a few key process parameters in a manufacturing environment. The study was explicitly on how to maximize production yield and minimize operational costs by utilizing advanced neural network models, specifically Long Short-Term Memory and Convolutional Neural Networks. These models were implemented using Python-based frameworks—TensorFlow and Keras. The targets of the research are the precision molding processes in which temperature ranges between 150°C and 220°C, the pressure ranges between 5 and 15 bar, and the material flow rate ranges between 10 and 50 kg/h, which are critical parameters that have a great effect on yield. A dataset of 1 million production cycles has been considered for five continuous years, where detailed logs are present showing the exact setting of parameters and yield output. The LSTM model would model time-dependent trends in production data, while CNN analyzed the spatial correlations between parameters. Models are designed in a supervised learning manner. For the model's loss, an MSE loss function is used, optimized through the Adam optimizer. After running a total of 100 training epochs, 95% accuracy was achieved by the models recommending optimal parameter configurations. Results indicated that with the use of RSM and DOE traditional methods, there was an increase in production yield of 12%. Besides, the error margin was reduced by 8%, hence consistent quality products from the deep learning models. The monetary value was annually around $2.5 million, the cost saved from material waste, energy consumption, and equipment wear resulting from the implementation of optimized process parameters. This system was deployed in an industrial production environment with the help of a hybrid cloud system: Microsoft Azure, for data storage, and the training and deployment of their models were performed on Google Cloud AI. The functionality of real-time monitoring of the process and automatic tuning of parameters depends on cloud infrastructure. To put it into perspective, deep learning models, especially those employing LSTM and CNN, optimize the production yield by fine-tuning process parameters. Future research will consider reinforcement learning with a view to achieving further enhancement of system autonomy and scalability across various manufacturing sectors.

Keywords: production yield optimization, deep learning, tuning of process parameters, LSTM, CNN, precision manufacturing, TensorFlow, Keras, cloud infrastructure, cost saving

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Authors: Rujia Chen, Ajit Narayanan

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Convolutional neural networks (CNN), as typically applied in deep learning, use layer-wise backpropagation (BP) to construct filters and kernels for feature extraction. Such filters are 2D or 3D groups of weights for constructing feature maps at subsequent layers of the CNN and are shared across the entire input. BP as a gradient descent algorithm has well-known problems of getting stuck at local optima. The use of genetic algorithms (GAs) for evolving weights between layers of standard artificial neural networks (ANNs) is a well-established area of neuroevolution. In particular, the use of crossover techniques when optimizing weights can help to overcome problems of local optima. However, the application of GAs for evolving the weights of filters and kernels in CNNs is not yet an established area of neuroevolution. In this paper, a GA-based filter development algorithm is proposed. The results of the proof-of-concept experiments described in this paper show the proposed GA algorithm can find filter weights through evolutionary techniques rather than BP learning. For some simple classification tasks like geometric shape recognition, the proposed algorithm can achieve 100% accuracy. The results for MNIST classification, while not as good as possible through standard filter learning through BP, show that filter and kernel evolution warrants further investigation as a new subarea of neuroevolution for deep architectures.

Keywords: neuroevolution, convolutional neural network, genetic algorithm, filters, kernels

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Authors: Shalini Attri

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Silence has been 'scriptured' in women within dominating social structures, as the modes of speaking and behaving which deny women free investiture to language. A woman becomes the product of ideological constructions as language substantiates andro-centric bias. Constrained from writing/speaking in the public sphere, women have traditionally been confined to expressing themselves in writing private poetry, letters or diaries. The helplessness of a woman is revealed in the ways in which she is expected to speak a language, which, in fact, is man-made. There are visible binaries of coloniser- colonised; Western-Eastern; White-Black, Nature-Culture, even Male-Female that contribute significantly to our understanding of the concept of representation and its resultant politics. Normally, an author is labeled as feminist, humanist, or propagandist and this process of labeling correspond to a sense of politics besides his inclination to a particular field. One cannot even think of contemporary literature without this representational politics. Thus, each and every bit of analysis of a work of literature demands a political angle to be dealt with. Besides literature, the historical facts and manuscripts are also subject to this politics. The image of woman as someone either dependent on man or is exploited by him only provides half the picture of this representational politics. The present paper is an attempt to study Panchali’s (Draupadi of Mahabharata) voiceless articulation and her representation as a strong woman in Chitra Divakurani’s The Palace of Illusions.

Keywords: politics, representation, silence, social structures

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Authors: Kyi Pyar Zaw, Zin Mar Kyu

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Character recognition is the process of converting a text image file into editable and searchable text file. Feature Extraction is the heart of any character recognition system. The character recognition rate may be low or high depending on the extracted features. In the proposed paper, 25 features for one character are used in character recognition. Basically, there are three steps of character recognition such as character segmentation, feature extraction and classification. In segmentation step, horizontal cropping method is used for line segmentation and vertical cropping method is used for character segmentation. In the Feature extraction step, features are extracted in two ways. The first way is that the 8 features are extracted from the entire input character using eight direction chain code frequency extraction. The second way is that the input character is divided into 16 blocks. For each block, although 8 feature values are obtained through eight-direction chain code frequency extraction method, we define the sum of these 8 feature values as a feature for one block. Therefore, 16 features are extracted from that 16 blocks in the second way. We use the number of holes feature to cluster the similar characters. We can recognize the almost Myanmar common characters with various font sizes by using these features. All these 25 features are used in both training part and testing part. In the classification step, the characters are classified by matching the all features of input character with already trained features of characters.

Keywords: chain code frequency, character recognition, feature extraction, features matching, segmentation

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Authors: Srushti Patil, Preethi Baligar, Gopalkrishna Joshi, Gururaj N. Bhadri

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In designing learning environments, the instructional strategies can be tailored to suit the learning style of an individual to ensure effective learning. In this study, the information shared on social media like Facebook is being used to predict learning style of a learner. Previous research studies have shown that Facebook data can be used to predict user personality. Users with a particular personality exhibit an inherent pattern in their digital footprint on Facebook. The proposed work aims to correlate the user's’ personality, predicted from Facebook data to the learning styles, predicted through questionnaires. For Millennial learners, Facebook has become a primary means for information sharing and interaction with peers. Thus, it can serve as a rich bed for research and direct the design of learning environments. The authors have conducted this study in an undergraduate freshman engineering course. Data from 320 freshmen Facebook users was collected. The same users also participated in the learning style and personality prediction survey. The Kolb’s Learning style questionnaires and Big 5 personality Inventory were adopted for the survey. The users have agreed to participate in this research and have signed individual consent forms. A specific page was created on Facebook to collect user data like personal details, status updates, comments, demographic characteristics and egocentric network parameters. This data was captured by an application created using Python program. The data captured from Facebook was subjected to text analysis process using the Linguistic Inquiry and Word Count dictionary. An analysis of the data collected from the questionnaires performed reveals individual student personality and learning style. The results obtained from analysis of Facebook, learning style and personality data were then fed into an automatic classifier that was trained by using the data mining techniques like Rule-based classifiers and Decision trees. This helps to predict the user personality and learning styles by analysing the common patterns. Rule-based classifiers applied for text analysis helps to categorize Facebook data into positive, negative and neutral. There were totally two models trained, one to predict the personality from Facebook data; another one to predict the learning styles from the personalities. The results show that the classifier model has high accuracy which makes the proposed method to be a reliable one for predicting the user personality and learning styles.

Keywords: educational data mining, Facebook, learning styles, personality traits

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Authors: P. Okoro Ugo Chigozie, P. A. Igbojekwe, E. N. Ukabuilu

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Tourism and terrorism experiences are best viewed as dynamic, complex systems with extreme diverse consequences on any nation’s economy. Tourism is one of the biggest industries in the world and one of the economical sectors which grows rapidly; tourism has positive impact on the nation’s economy. Terrorism is the method or the theory behind the method whereby an organized group or party seeks to achieve its avowed aims chiefly through the systematic use of violence; the consequences of terrorism on tourist destinations are inescapable and can be profound. Especially, it threatens the attractiveness of a tourist destination and strips the competitiveness of that destination. Destination’s vulnerability to politically motivated violence not only retracts tourists, but threatens sustainable tourism development. This paper examines the activities of the Jamaata Ahlis Sunna Liddaawati -an Islamic sect popularly known as Boko Haram – and its impact on sustainable tourism development in the Nigeria state. Possible triggers of this insurgency and potentially evolving measure against its influence on sustainable tourism including, strong image management of the tourism industry, feasible tourist safety policy, viable anti-terrorism measures, proactive respond to the challenge of terrorism, reinforcement of the legitimate frameworks and irrevocable penalty against menace of corruption; are discussed in this paper, as limiting the effects of insurgency on the attractiveness of Nigeria as safe tourists destination.

Keywords: Nigeria, terrorism, sustainable tourism development, corruption and competitiveness

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Authors: ِAbdallah A. A. Shaheen

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Gaza Strip faces economic and political issues such as conflict, siege and urbanization; all these have led to an increase in the air pollution over Gaza Strip. In this study, Particulate matter 10 (PM10) concentration over Gaza Strip has been estimated by Landsat Thematic Mapper (TM) and Landsat Enhanced Thematic Mapper Plus (ETM+) data, based on a multispectral algorithm. Simultaneously, in-situ measurements for the corresponding particulate are acquired for selected time period. Landsat and ground data for eleven years are used to develop the algorithm while four years data (2002, 2006, 2010 and 2014) have been used to validate the results of algorithm. The developed algorithm gives highest regression, R coefficient value i.e. 0.86; RMSE value as 9.71 µg/m³; P values as 0. Average validation of algorithm show that calculated PM10 strongly correlates with measured PM10, indicating high efficiency of algorithm for the mapping of PM10 concentration during the years 2000 to 2014. Overall results show increase in minimum, maximum and average yearly PM10 concentrations, also presents similar trend over urban area. The rate of urbanization has been evaluated by supervised classification of the Landsat image. Urban sprawl from year 2000 to 2014 results in a high concentration of PM10 in the study area.

Keywords: PM10, landsat, atmospheric reflectance, Gaza strip, urbanization

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Authors: Mitchell Gallagher

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This investigation explores the relationship between Chinese government ministers' tweets and publicized anti-US propaganda political cartoons by Chinese state media. Defining "anti-US" tweets as expressions with negative impressions about the United States, its policies, or cultural values, the study considers their context-dependent nature. Analyzing social media's growing role, this research probes the Chinese government's attitudes toward the United States. While China traditionally adhered to a non-interference stance, instances of verbal and visual retorts occurred, driven by efforts to enhance soft power and counter unfavorable portrayals. To navigate global challenges, China embraced proactive image construction, utilizing political cartoons as a messaging tool. As Sino-American political relations continue deteriorating, it has become increasingly commonplace for Chinese officials to circulate anti-US messages and negative impressions of the United States via tweets. The present study is committed to inspecting the nature and frequency of political cartoons casting the United States in an unfavorable light, with the aim of gaining a comprehensive understanding the degree to which the Chinese government and state-affiliated media are aligned in their corresponding messaging.

Keywords: China, political cartoons, propaganda, twitter, social media

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Authors: Nan Deng, Zhenqiu Liu

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Thousands of patients with metastatic tumors were diagnosed with cancers of unknown primary sites each year. The inability to identify the primary cancer site may lead to inappropriate treatment and unexpected prognosis. Nowadays, a large amount of genomics and transcriptomics cancer data has been generated by next-generation sequencing (NGS) technologies, and The Cancer Genome Atlas (TCGA) database has accrued thousands of human cancer tumors and healthy controls, which provides an abundance of resource to differentiate cancer types. Meanwhile, deep convolutional neural networks (CNNs) have shown high accuracy on classification among a large number of image object categories. Here, we utilize 25 cancer primary tumors and 3 normal tissues from TCGA and convert their RNA-Seq gene expression profiling to color images; train, validate and test a CNN classifier directly from these images. The performance result shows that our CNN classifier can archive >80% test accuracy on most of the tumors and normal tissues. Since the gene expression pattern of distant metastases is similar to their primary tumors, the CNN classifier may provide a potential computational strategy on identifying the unknown primary origin of metastatic cancer in order to plan appropriate treatment for patients.

Keywords: bioinformatics, cancer, convolutional neural network, deep leaning, gene expression pattern

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Authors: Md. Hafezur Rahaman, Md. Sagor Hosen, Md. Abdul Gafur, Rasel Habib

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This research is a systematic study of effects of poly(D-lactic acid) (PDLA) on the properties of poly(L-lactic acid)(PLLA)/microcrystalline cellulose (MCC)/PDLA blends by stereo complex crystallization. Blends were prepared with constant percentage of (3 percent) MCC and different percentage of PDLA by solution casting methods. These blends were characterized by Fourier Transform Infrared Spectroscopy (FTIR) for the confirmation of blends compatibility, Wide-Angle X-ray Scattering (WAXS) and scanning electron microscope (SEM) for the analysis of morphology, thermo-gravimetric analysis (TGA) and differential thermal analysis (DTA) for thermal properties measurement. FTIR Analysis results confirm no new characteristic absorption peaks appeared in the spectrum instead shifting of peaks due to hydrogen bonding help to have compatibility of blends component. Development of three new peaks from XRD analysis indicates strongly the formation of stereo complex crystallinity in the PLLA structure with the addition of PDLA. TGA and DTG results indicate that PDLA can improve the heat resistivity of the PLLA/MCC blends by increasing its degradation temperature. Comparison of DTA peaks also ensure developed thermal properties. Image of SEM shows the improvement of surface morphology.

Keywords: microcrystalline cellulose, poly(l-lactic acid), stereocomplex crystallization, thermal stability

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Authors: Kanika Gupta, Ashok Kumar

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Biofilms are dense, highly hydrated cell clusters that are irreversibly attached to a substratum, to an interface or to each other, and are embedded in a self-produced gelatinous matrix composed of extracellular polymeric substances. Research in biofilm field has become very significant, as biofilm has shown high mechanical resilience and resistance to antibiotic treatment and constituted as a significant problem in both healthcare and other industry related to microorganisms. The massive information both stated and hidden in the biofilm literature are growing exponentially therefore it is not possible for researchers and practitioners to automatically extract and relate information from different written resources. So, the current work proposes and discusses the use of text mining techniques for the extraction of information from biofilm literature corpora containing 34306 documents. It is very difficult and expensive to obtain annotated material for biomedical literature as the literature is unstructured i.e. free-text. Therefore, we considered unsupervised approach, where no annotated training is necessary and using this approach we developed a system that will classify the text on the basis of growth and development, drug effects, radiation effects, classification and physiology of biofilms. For this, a two-step structure was used where the first step is to extract keywords from the biofilm literature using a metathesaurus and standard natural language processing tools like Rapid Miner_v5.3 and the second step is to discover relations between the genes extracted from the whole set of biofilm literature using pubmed.mineR_v1.0.11. We used unsupervised approach, which is the machine learning task of inferring a function to describe hidden structure from 'unlabeled' data, in the above-extracted datasets to develop classifiers using WinPython-64 bit_v3.5.4.0Qt5 and R studio_v0.99.467 packages which will automatically classify the text by using the mentioned sets. The developed classifiers were tested on a large data set of biofilm literature which showed that the unsupervised approach proposed is promising as well as suited for a semi-automatic labeling of the extracted relations. The entire information was stored in the relational database which was hosted locally on the server. The generated biofilm vocabulary and genes relations will be significant for researchers dealing with biofilm research, making their search easy and efficient as the keywords and genes could be directly mapped with the documents used for database development.

Keywords: biofilms literature, classifiers development, text mining, unsupervised learning approach, unstructured data, relational database

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Authors: K. Bresilla, L. Manfrini, B. Morandi, A. Boini, G. Perulli, L. C. Grappadelli

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Image/video processing for fruit in the tree using hard-coded feature extraction algorithms have shown high accuracy during recent years. While accurate, these approaches even with high-end hardware are computationally intensive and too slow for real-time systems. This paper details the use of deep convolution neural networks (CNNs), specifically an algorithm (YOLO - You Only Look Once) with 24+2 convolution layers. Using deep-learning techniques eliminated the need for hard-code specific features for specific fruit shapes, color and/or other attributes. This CNN is trained on more than 5000 images of apple and pear fruits on 960 cores GPU (Graphical Processing Unit). Testing set showed an accuracy of 90%. After this, trained data were transferred to an embedded device (Raspberry Pi gen.3) with camera for more portability. Based on correlation between number of visible fruits or detected fruits on one frame and the real number of fruits on one tree, a model was created to accommodate this error rate. Speed of processing and detection of the whole platform was higher than 40 frames per second. This speed is fast enough for any grasping/harvesting robotic arm or other real-time applications.

Keywords: artificial intelligence, computer vision, deep learning, fruit recognition, harvesting robot, precision agriculture

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Authors: Jimeng Shi, Mahek Jain, Giri Narasimhan

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Time Series Forecasting (TSF) is used to predict the target variables at a future time point based on the learning from previous time points. To keep the problem tractable, learning methods use data from a fixed-length window in the past as an explicit input. In this paper, we study how the performance of predictive models changes as a function of different look-back window sizes and different amounts of time to predict the future. We also consider the performance of the recent attention-based Transformer models, which have had good success in the image processing and natural language processing domains. In all, we compare four different deep learning methods (RNN, LSTM, GRU, and Transformer) along with a baseline method. The dataset (hourly) we used is the Beijing Air Quality Dataset from the UCI website, which includes a multivariate time series of many factors measured on an hourly basis for a period of 5 years (2010-14). For each model, we also report on the relationship between the performance and the look-back window sizes and the number of predicted time points into the future. Our experiments suggest that Transformer models have the best performance with the lowest Mean Average Errors (MAE = 14.599, 23.273) and Root Mean Square Errors (RSME = 23.573, 38.131) for most of our single-step and multi-steps predictions. The best size for the look-back window to predict 1 hour into the future appears to be one day, while 2 or 4 days perform the best to predict 3 hours into the future.

Keywords: air quality prediction, deep learning algorithms, time series forecasting, look-back window

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Authors: Anusmita Das

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In the post-industrial scenario, cities in India have become critical sites of negotiation and are expected to become some of the largest urban agglomeration of the twenty-first century. This has created a pluralist identity resulting in a new multifarious urbanism pervading throughout the entire urban landscape. There is an ambiguity regarding the character of present day Indian cities with new meanings emerging and no methodical study to understand them. More than an abstract diagram, the present day cities can be looked at as an ensemble of meanings. One of the ways in which the meaning is reflected is through events. Festivals such as Diwali, Dussera, Durga Puja, Ganesh Chaturthi, etc have transpired as the phenomenon of the city, and their presence in the everyday landscape weaves itself through the urban fabric dominating the popular visual culture of Indian cities. Festivals influence people’s idea of a city. Ritual, festival, celebrations are important in shaping of the urban environment and in their influence on the intangible aspect of the urban setting. These festivals pertaining to the city in motion have emerged as the symbolic image of the emerging urban Indian condition giving birth to new urban identities. The study undertaken to understand the present context of temporality of Indian cities is important in analyzing the process of its formation and transformation. This study aims to review the evolution of new dimensions of urbanism in India as well as its implication on the identity of cities.

Keywords: urban identities, urban design, festivals, rituals, celebrations, inter-disciplinary study

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Authors: Joanna Grzybowska, Magdalena Igras, Mariusz Ziółko

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Association rule learning is an approach for discovering interesting relationships in large databases. The analysis of relations, invisible at first glance, is a source of new knowledge which can be subsequently used for prediction. We used this data mining technique (which is an automatic and objective method) to learn about interesting affects associations in a corpus of emergency phone calls. We also made an attempt to match revealed rules with their possible situational context. The corpus was collected and subjectively annotated by two researchers. Each of 3306 recordings contains information on emotion: (1) type (sadness, weariness, anxiety, surprise, stress, anger, frustration, calm, relief, compassion, contentment, amusement, joy) (2) valence (negative, neutral, or positive) (3) intensity (low, typical, alternating, high). Also, additional information, that is a clue to speaker’s emotional state, was annotated: speech rate (slow, normal, fast), characteristic vocabulary (filled pauses, repeated words) and conversation style (normal, chaotic). Exponentially many rules can be extracted from a set of items (an item is a previously annotated single information). To generate the rules in the form of an implication X → Y (where X and Y are frequent k-itemsets) the Apriori algorithm was used - it avoids performing needless computations. Then, two basic measures (Support and Confidence) and several additional symmetric and asymmetric objective measures (e.g. Laplace, Conviction, Interest Factor, Cosine, correlation coefficient) were calculated for each rule. Each applied interestingness measure revealed different rules - we selected some top rules for each measure. Owing to the specificity of the corpus (emergency situations), most of the strong rules contain only negative emotions. There are though strong rules including neutral or even positive emotions. Three examples of the strongest rules are: {sadness} → {anxiety}; {sadness, weariness, stress, frustration} → {anger}; {compassion} → {sadness}. Association rule learning revealed the strongest configurations of affects (as well as configurations of affects with affect-related information) in our emergency phone calls corpus. The acquired knowledge can be used for prediction to fulfill the emotional profile of a new caller. Furthermore, a rule-related possible context analysis may be a clue to the situation a caller is in.

Keywords: data mining, emergency phone calls, emotional profiles, rules

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Authors: Iryna Biskub

Abstract:

The interdisciplinary approach to modern linguistic studies is exemplified by the merge of various research methods, which sometimes causes complications related to the verification of the research results. This methodological confusion can be resolved by means of creating new techniques of linguistic analysis combining several scientific paradigms. Modern linguistics has developed really productive and efficient methods for the investigation of cognitive and communicative phenomena of which language is the central issue. In the field of discourse studies, one of the best examples of research methods is the method of Critical Discourse Analysis (CDA). CDA can be viewed both as a method of investigation, as well as a critical multidisciplinary perspective. In CDA the position of the scholar is crucial from the point of view exemplifying his or her social and political convictions. The generally accepted approach to obtaining scientifically reliable results is to use a special well-defined scientific method for researching special types of language phenomena: cognitive methods applied to the exploration of cognitive aspects of language, whereas communicative methods are thought to be relevant only for the investigation of communicative nature of language. In the recent decades discourse as a sociocultural phenomenon has been the focus of careful linguistic research. The very concept of discourse represents an integral unity of cognitive and communicative aspects of human verbal activity. Since a human being is never able to discriminate between cognitive and communicative planes of discourse communication, it doesn’t make much sense to apply cognitive and communicative methods of research taken in isolation. It is possible to modify the classical CDA procedure by means of mapping human cognitive procedures onto the strategic communicative planning of discourse communication. The analysis of the electronic petition 'Block Donald J Trump from UK entry. The signatories believe Donald J Trump should be banned from UK entry' (584, 459 signatures) and the parliamentary debates on it has demonstrated the ability to map cognitive and communicative levels in the following way: the strategy of discourse modeling (communicative level) overlaps with the extraction of semantic macrostructures (cognitive level); the strategy of discourse management overlaps with the analysis of local meanings in discourse communication; the strategy of cognitive monitoring of the discourse overlaps with the formation of attitudes and ideologies at the cognitive level. Thus, the experimental data have shown that it is possible to develop a new complex methodology of discourse analysis, where cognition would meet communication, both metaphorically and literally. The same approach may appear to be productive for the creation of computational models of human-computer interaction, where the automatic generation of a particular type of a discourse could be based on the rules of strategic planning involving cognitive models of CDA.

Keywords: cognition, communication, discourse, strategy

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Authors: Xiaoping Zhang, Fengying Yan

Abstract:

The implementation of the 'Urban Design Management Measures' requires that the master urban design should aim at creating a livable urban space. However, to our best knowledge, the existing researches and practices of master urban design not only focus less on the livable space but also face a number of problems such as paying more attention to the image of the city, ignoring the people-oriented and lacking dynamic continuity. In order to make the master urban design can better guide the construction of city. Firstly, the paper proposes the livable city hierarchy system to meet the needs of different groups of people and then constructs the framework of livable goal-oriented master urban design based on the theory of livable content and the ideological origin of people-oriented. Secondly, the paper takes the master urban design practice of Zibo as a sample and puts forward the design strategy of strengthening the pattern, improve the quality of space, shape the feature, and establish a series of action plans based on the strategy of urban space development. Finally, the paper explores the method system of livable goal-oriented master urban design from the aspects of safety pattern, morphology pattern, neighborhood scale, open space, street space, public interface, style feature, public participation and action plans.

Keywords: livable, master urban design, public participation, zibo city

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Authors: Mahitab A. A. Mahmoud

Abstract:

Despite the international community’s galvanized efforts to achieve gender equality, the Arab world still lags behind for their sustained suppression of diversity and freedoms. In Egypt, homosexuals are defamed and hunted not only by authorities but also by politicized religious institutions and media platforms. The resultant state-sponsored homophobia is reflected in media. This paper offers a critical discourse analysis of the representation of LGBTQs in Egypt’s local news articles and movies in an attempt to investigate the underlying ideology. The results reveal a clichéd portrayal of homosexuals as a social parasite that requires cleansing by the government. LGBTQs are depicted as an outcome of debauchery, unhappy marriage, sexual deviancy, deficiency of masculinity/femininity, absence of the mother and/or father figure(s), abject poverty, excessive wealth, psychiatric disorder, debased instincts, childhood sexual molestation, immorality, deviation from religion, chaos, treason, conspiracy against the regime, to name only a few. This image, which is imposed and sustained by the state, exposes homosexuals to a violation of their human rights by both the police and the society, endangers their lives, breeds intolerance, social inequality and violence, prevents healthy coexistence; and deprives them of living a normal life.

Keywords: critical discourse analysis, gender studies, homophobia, homosexuality, ideology, media studies

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Authors: Wei Hu, Wenguang Chen, Chong Dong

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In order to improve the efficiency and safety of photovoltaic power generation devices, this photovoltaic power generation system combines Artificial Intelligence (AI) and the Internet of Things (IoT) to control the chasing photovoltaic power generation device to track the sun to improve power generation efficiency and then convert energy management. The system uses artificial intelligence as the control terminal, the power generation device executive end uses the Linux system, and Exynos4412 is the CPU. The power generating device collects the sun image information through Sony CCD. After several power generating devices feedback the data to the CPU for processing, several CPUs send the data to the artificial intelligence control terminal through the Internet. The control terminal integrates the executive terminal information, time information, and environmental information to decide whether to generate electricity normally and then whether to convert the converted electrical energy into the grid or store it in the battery pack. When the power generation environment is abnormal, the control terminal authorizes the protection strategy, the power generation device executive terminal stops power generation and enters a self-protection posture, and at the same time, the control terminal synchronizes the data with the cloud. At the same time, the system is more intelligent, more adaptive, and longer life.

Keywords: photo-voltaic power generation, the pursuit of light, artificial intelligence, internet of things, photovoltaic array, power management

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Authors: Kolja Them, Priyal Chikhaliwala, Sudeshna Chandra

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Purpose: The purpose of this work is to examine possibilities for noninvasive imaging and identification of tumor markers for cancer diagnosis. The proposed method uses antibody conjugated iron oxide nanoparticles and multicolor Magnetic Particle Imaging (mMPI). The method has the potential for radiation exposure free real-time estimation of local tumor marker concentrations in vivo. In this study, the method is applied to human Alpha-1-Fetoprotein. Materials and Methods: As tracer material AFP antibody-conjugated Dendrimer-Fe3O4 nanoparticles were used. The nanoparticle bioconjugates were then incubated with bovine serum albumin (BSA) to block any possible nonspecific binding sites. Parts of the resulting solution were then incubated with AFP antigen. MPI measurements were done using the preclinical MPI scanner (Bruker Biospin MRI GmbH) and the multicolor method was used for image reconstruction. Results: In multicolor MPI images the nanoparticles incubated only with BSA were clearly distinguished from nanoparticles incubated with BSA and AFP antigens. Conclusion: Tomographic imaging of human tumor marker Alpha-1-Fetoprotein is possible using AFP antibody conjugated iron oxide nanoparticles in presence of BSA. This opens interesting perspectives for cancer diagnosis.

Keywords: noninvasive imaging, tumor antigens, antibody conjugated iron oxide nanoparticles, multicolor magnetic particle imaging, cancer diagnosis

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Authors: Ki Moo Lim, Iman R. Tayibnapis

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According to WHO estimation, 38 out of 56 million (68%) global deaths in 2012, were due to noncommunicable diseases (NCDs). To avert NCD, one of the solutions is early detection of diseases. In order to do that, we developed 'U-Healthcare Mirror', which is able to measure vital sign such as heart rate (HR) and respiration rate without any physical contact and consciousness. To measure HR in the mirror, we utilized digital camera. The camera records red, green, and blue (RGB) discoloration from user's facial image sequences. We extracted blood volume pulse (BVP) from the RGB discoloration because the discoloration of the facial skin is accordance with BVP. We used blind source separation (BSS) to extract BVP from the RGB discoloration and adaptive filters for removing noises. We utilized singular value decomposition (SVD) method to implement the BSS and the adaptive filters. HR was estimated from the obtained BVP. We did experiment for HR measurement by using our method and previous method that used independent component analysis (ICA) method. We compared both of them with HR measurement from commercial oximeter. The experiment was conducted under various distance between 30~110 cm and light intensity between 5~2000 lux. For each condition, we did measurement 7 times. The estimated HR showed 2.25 bpm of mean error and 0.73 of pearson correlation coefficient. The accuracy has improved compared to previous work. The optimal distance between the mirror and user for HR measurement was 50 cm with medium light intensity, around 550 lux.

Keywords: blood volume pulse, heart rate, photoplethysmography, independent component analysis

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Authors: P. Praveen Kumar, P. Vimal Prabhu, Renu John

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In biology most microscopy specimens, in particular living cells are transparent. In cell imaging, it is hard to create an image of a cell which is transparent with a very small refractive index change with respect to the surrounding media. Various techniques like addition of staining and contrast agents, markers have been applied in the past for creating contrast. Many of the staining agents or markers are not applicable to live cell imaging as they are toxic. In this paper, we report theoretical and experimental results from quantitative phase imaging of yeast cells with a commercial bright field microscope. We reconstruct the phase of cells non-interferometrically based on the transport of intensity equations (TIE). This technique estimates the axial derivative from positive through-focus intensity measurements. This technique allows phase imaging using a regular microscope with white light illumination. We demonstrate nano-metric depth sensitivity in imaging live yeast cells using this technique. Experimental results will be shown in the paper demonstrating the capability of the technique in 3-D volume estimation of living cells. This real-time imaging technique would be highly promising in real-time digital pathology applications, screening of pathogens and staging of diseases like malaria as it does not need any pre-processing of samples.

Keywords: axial derivative, non-interferometric imaging, quantitative phase imaging, transport of intensity equation

Procedia PDF Downloads 384

Authors: Nadiah Mohamad, Wan Samiati Andriana Wan Mohd Daud, M. Suhaimi Tohid, Mohd Fazli Othman, Mohamad Rizal Salleh

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This is a collaborative project of the mural art between The Department of Fine Art from Universiti Teknologi MARA (UiTM) and Petronas Dagangan Berhad (PDB), the most leading retailer and marketer of downstream oil and gas products in Malaysia. Five different states in the Peninsular of Malaysia that has been identified in showcasing the National Identity of Malaysia at each Petronas gas station, this also includes the Air Keroh in Melaka, Pasir Pekan in Kelantan, Pontian in Johor, Simpang Pulai in Perak, and also Wakaf Bharu in Terengganu. This project is to analyze the element of national identity that has been demonstrated at the Petronas's Mural. The ultimate aim of the mural is to let the community and local people to be aware about what Malaysians are consists and proud of and how everyone is able to connect with the idea through visual art. The method that is being explained in this research is by using visual data through research and also self-experience in collecting the visual data in identifying what images is considered as the national identity and idea development and visual analysis is being transferred based upon the visual data collection. In this stage, elements and principles of design will be the key in highlighting what is necessary for a work of art. In conclusion, visual image of the National Identity of Malaysia is able to connect to the audience from local and also to the people from outside the country to learn and understand the beauty and diversity of Malaysia as a unique country with art through the wall of five Petronas gas station.

Keywords: community, fine art, mural art, national identity

Procedia PDF Downloads 207