Search results for: computer assisted classification

Authors: Irfan Mohiuddin, Ahmad Al Mogren

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In recent years, cloud computing has earned substantial attention in the Oil and Gas Industry and provides services in all the phases of the industry lifecycle. Oil and gas supply infrastructure, in particular, is more vulnerable to accidental, natural and intentional threats because of its widespread distribution. Numerous surveys have been conducted on cloud security and privacy. However, to the best of our knowledge, hardly any survey is carried out that reviews cyber security in all phases with a cloud computing perspective. Moreover, a distinctive classification is performed for all the cloud-based cyber security measures based on the cloud component in use. The classification approach will enable researchers to identify the required technique used to enhance the security in specific cloud components. Also, the limitation of each component will allow the researchers to design optimal algorithms. Lastly, future directions are given to point out the imminent challenges that can pave the way for researchers to further enhance the resilience to cyber security threats in the oil and gas industry.

Keywords: cyber security, cloud computing, safety and security, oil and gas industry, security threats, oil and gas pipelines

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Authors: I. Berger, N. Machour, F. Portet-Koltalo

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Polycyclic aromatic hydrocarbons (PAHs) are toxic and carcinogenic pollutants produced in majority by incomplete combustion processes in industrialized and urbanized areas. After being emitted in atmosphere, these persistent contaminants are deposited to soils or sediments. Even if persistent, some can be partially degraded (photodegradation, biodegradation, chemical oxidation) and they lead to oxygenated metabolites (oxy-PAHs) which can be more toxic than their parent PAH. Oxy-PAHs are less measured than PAHs in sediments and this study aims to compare different analytical tools in order to extract and quantify a mixture of four hydroxylated PAHs (OH-PAHs) and four carbonyl PAHs (quinones) in sediments. Methodologies: Two analytical systems – HPLC with on-line UV and fluorescence detectors (HPLC-UV-FLD) and GC coupled to a mass spectrometer (GC-MS) – were compared to separate and quantify oxy-PAHs. Microwave assisted extraction (MAE) was optimized to extract oxy-PAHs from sediments. Results: First OH-PAHs and quinones were analyzed in HPLC with on-line UV and fluorimetric detectors. OH-PAHs were detected with the sensitive FLD, but the non-fluorescent quinones were detected with UV. The limits of detection (LOD)s obtained were in the range (2-3)×10-4 mg/L for OH-PAHs and (2-3)×10-3 mg/L for quinones. Second, even if GC-MS is not well adapted to the analysis of the thermodegradable OH-PAHs and quinones without any derivatization step, it was used because of the advantages of the detector in terms of identification and of GC in terms of efficiency. Without derivatization, only two of the four quinones were detected in the range 1-10 mg/L (LODs=0.3-1.2 mg/L) and LODs were neither very satisfying for the four OH-PAHs (0.18-0.6 mg/L). So two derivatization processes were optimized, comparing to literature: one for silylation of OH-PAHs, one for acetylation of quinones. Silylation using BSTFA/TCMS 99/1 was enhanced using a mixture of catalyst solvents (pyridine/ethyle acetate) and finding the appropriate reaction duration (5-60 minutes). Acetylation was optimized at different steps of the process, including the initial volume of compounds to derivatize, the added amounts of Zn (0.1-0.25 g), the nature of the derivatization product (acetic anhydride, heptafluorobutyric acid…) and the liquid/liquid extraction at the end of the process. After derivatization, LODs were decreased by a factor 3 for OH-PAHs and by a factor 4 for quinones, all the quinones being now detected. Thereafter, quinones and OH-PAHs were extracted from spiked sediments using microwave assisted extraction (MAE) followed by GC-MS analysis. Several mixtures of solvents of different volumes (10-25 mL) and using different extraction temperatures (80-120°C) were tested to obtain the best recovery yields. Satisfactory recoveries could be obtained for quinones (70-96%) and for OH-PAHs (70-104%). Temperature was a critical factor which had to be controlled to avoid oxy-PAHs degradation during the MAE extraction process. Conclusion: Even if MAE-GC-MS was satisfactory to analyze these oxy-PAHs, MAE optimization has to be carried on to obtain a most appropriate extraction solvent mixture, allowing a direct injection in the HPLC-UV-FLD system, which is more sensitive than GC-MS and does not necessitate a previous long derivatization step.

Keywords: derivatizations for GC-MS, microwave assisted extraction, on-line HPLC-UV-FLD, oxygenated PAHs, polluted sediments

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Authors: Özlem Ateş Sönmezoğlu, Begüm Terzi, Ahmet Yıldırım, Leyla Gündüz

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Durum wheat quality is defined as its suitability for pasta processing, that is pasta making quality. Another factor that determines the quality of durum wheat is the nutritional value of wheat or its final products. Wheat is a basic source of calories, proteins and minerals for humans in many countries of the world. For this reason, improvement of wheat nutritional value is of great importance. In recent years, deficiencies in protein and micronutrients, particularly in iron and zinc, have seriously increased. Therefore, basic foods such as wheat must be improved for micronutrient content. The effects of some major genes for grain quality established. Gpc-B1 locus is one of the genes increased protein and micronutrients content, and used in improvement studies of durum wheat nutritional value. The aim of this study was to increase the protein content and the micronutrient (Fe, Zn ve Mn) contents of an advanced durum wheat line (TMB 1) that was previously improved for its protein quality. For this purpose, TMB1 advanced durum wheat line were used as the recurrent parent and also, UC1113-Gpc-B1 line containing the Gpc-B1 gene was used as the gene source. In all of the generations, backcrossed plants carrying the targeted gene region were selected by marker assisted selection (MAS). BC4F1 plants MAS method was employed in combination with embryo culture and rapid plant growth in a controlled greenhouse conditions in order to shorten the duration of the transition between generations in backcross breeding. The Gpc-B1 gene was selected specific molecular markers. Since Yr-36 gene associated with Gpc-B1 allele, it was also transferred to the Gpc-B1 transferred lines. Thus, the backcrossed plants selected by MAS are resistance to yellow rust disease. This research has been financially supported by TÜBİTAK (112T910).

Keywords: Durum wheat, Gpc-B1, MAS, Triticum durum, Yr-36

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Authors: Hang Lo Lee, Ki Il Song, Hee Hwan Ryu

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An accurate prediction of TBM(Tunnel Boring Machine) performance is very difficult for reliable estimation of the construction period and cost in preconstruction stage. For this purpose, the aim of this study is to analyze the evaluation process of various prediction models published since 2000 for TBM performance, and to select the optimal input parameters for the prediction model. A classification system of TBM performance prediction model and applied methodology are proposed in this research. Input and output parameters applied for prediction models are also represented. Based on these results, a statistical analysis is performed using the collected data from shield TBM tunnel in South Korea. By performing a simple regression and residual analysis utilizinFg statistical program, R, the optimal input parameters are selected. These results are expected to be used for development of prediction model of TBM performance.

Keywords: TBM performance prediction model, classification system, simple regression analysis, residual analysis, optimal input parameters

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Authors: Xiaoping Su, Gabriel G. Malouf

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Introduction: Breast cancer is a heterogeneous disease that can be classified in 4 subgroups using transcriptional profiling. The role of lncRNA expression in human breast cancer biology, prognosis, and molecular classification remains unknown. Methods and results: Using an integrative comprehensive analysis of lncRNA, mRNA and DNA methylation in 900 breast cancer patients from The Cancer Genome Atlas (TCGA) project, we unraveled the molecular portraits of 1,700 expressed lncRNA. Some of those lncRNAs (i.e, HOTAIR) are previously reported and others are novel (i.e, HOTAIRM1, MAPT-AS1). The lncRNA classification correlated well with the PAM50 classification for basal-like, Her-2 enriched and luminal B subgroups, in contrast to the luminal A subgroup which behaved differently. Importantly, estrogen receptor (ESR1) expression was associated with distinct lncRNA networks in lncRNA clusters III and IV. Gene set enrichment analysis for cis- and trans-acting lncRNA showed enrichment for breast cancer signatures driven by breast cancer master regulators. Almost two third of those lncRNA were marked by enhancer chromatin modifications (i.e., H3K27ac), suggesting that lncRNA expression may result in increased activity of neighboring genes. Differential analysis of gene expression profiling data showed that lncRNA HOTAIRM1 was significantly down-regulated in basal-like subtype, and DNA methylation profiling data showed that lncRNA HOTAIRM1 was highly methylated in basal-like subtype. Thus, our integrative analysis of gene expression and DNA methylation strongly suggested that lncRNA HOTAIRM1 should be a tumor suppressor in basal-like subtype. Conclusion and significance: Our study depicts the first lncRNA molecular portrait of breast cancer and shows that lncRNA HOTAIRM1 might be a novel tumor suppressor.

Keywords: lncRNA profiling, breast cancer, HOTAIRM1, tumor suppressor

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Authors: Rezaei Hachesu Peyman, Oliyaee Azadeh, Salahzadeh Zahra, Alizadeh Somayyeh, Safaei Naser

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Coronary Artery Disease (CAD) is one major cause of disability in adults and one main cause of death in developed. In this study, data mining techniques including Decision Trees, Artificial neural networks (ANNs), and Support Vector Machine (SVM) analyze CAD data. Data of 4948 patients who had suffered from heart diseases were included in the analysis. CAD is the target variable, and 24 inputs or predictor variables are used for the classification. The performance of these techniques is compared in terms of sensitivity, specificity, and accuracy. The most significant factor influencing CAD is chest pain. Elderly males (age > 53) have a high probability to be diagnosed with CAD. SVM algorithm is the most useful way for evaluation and prediction of CAD patients as compared to non-CAD ones. Application of data mining techniques in analyzing coronary artery diseases is a good method for investigating the existing relationships between variables.

Keywords: classification, coronary artery disease, data-mining, knowledge discovery, extract

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Authors: Jiajia Yao, GuanLin Wu, Fang liu, JunShuai Xue, JinCheng Zhang, Yue Hao

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Due to its outstanding material properties like high thermal conductivity and ultra-wide bandgap, Aluminum nitride (AlN) has the promising potential to provide high breakdown voltage and high output power among III-nitrides for various applications in electronics and optoelectronics. This work presents material growth and characterization of strained channel Aluminum nitride/Gallium nitride (AlN/GaN) heterostructures grown by plasma-assisted molecular beam epitaxy (PA-MBE) on AlN-on-sapphire templates. To improve the crystal quality and manifest the ability of the PA-MBE approach, a thick AlN buffer with a thickness of 180 nm is first grown on AlN template, which acts as a back-barrier to enhance the breakdown characteristic and isolates the leakage path existing in the interface between AlN epilayer and AlN template, as well as improve the heat dissipation. The grown AlN buffer features a root-mean-square roughness of 0.2 nm over a scanned area of 2×2 µm2 measured by atomic force microscopy (AFM), and exhibits full-width at half-maximum of 95 and 407 arcsec for the (002) and (102) plane the X-ray rocking curve, respectively, tested by high resolution x-ray diffraction (HR-XRD). With a thin and strained GaN channel, the electron mobility of 294 cm2 /Vs. with a carrier concentration of 2.82×1013 cm-2 at room temperature is achieved in AlN/GaN double-channel heterostructures, and the depletion capacitance is as low as 14 pF resolved by the capacitance-voltage, which indicates the promising opportunities for future applications in next-generation high temperature, high-frequency and high-power electronics with a further increased electron mobility by optimization of heterointerface quality.

Keywords: AlN/GaN, HEMT, MBE, homoepitaxy

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Authors: Alina Muradyan, Lina Babayan, Arsen Nanyan, Gohar Galstyan, Vigen Khachatryan

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We present a photometric method for identifying stars, galaxies and quasars in multi-color surveys, which uses a library of ∼> 65000 color templates for comparison with observed objects. The method aims for extracting the information content of object colors in a statistically correct way, and performs a classification as well as a redshift estimation for galaxies and quasars in a unified approach based on the same probability density functions. For the redshift estimation, we employ an advanced version of the Minimum Error Variance estimator which determines the redshift error from the redshift dependent probability density function itself. The method was originally developed for the Calar Alto Deep Imaging Survey (CADIS), but is now used in a wide variety of survey projects. We checked its performance by spectroscopy of CADIS objects, where the method provides high reliability (6 errors among 151 objects with R < 24), especially for the quasar selection, and redshifts accurate within σz ≈ 0.03 for galaxies and σz ≈ 0.1 for quasars. For an optimization of future survey efforts, a few model surveys are compared, which are designed to use the same total amount of telescope time but different sets of broad-band and medium-band filters. Their performance is investigated by Monte-Carlo simulations as well as by analytic evaluation in terms of classification and redshift estimation. If photon noise were the only error source, broad-band surveys and medium-band surveys should perform equally well, as long as they provide the same spectral coverage. In practice, medium-band surveys show superior performance due to their higher tolerance for calibration errors and cosmic variance. Finally, we discuss the relevance of color calibration and derive important conclusions for the issues of library design and choice of filters. The calibration accuracy poses strong constraints on an accurate classification, which are most critical for surveys with few, broad and deeply exposed filters, but less severe for surveys with many, narrow and less deep filters.

Keywords: VO, ArVO, DFBS, FITS, image processing, data analysis

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Authors: Levent Dumenci, Laura A. Siminoff

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Health literacy has been shown to predict a variety of health outcomes. Reliable identification of persons with limited cancer health literacy (LCHL) has been proved questionable with existing instruments using an arbitrary cut point along a continuum. The CHLT-6, however, uses a latent mixture modeling approach to identify persons with LCHL. The purpose of this study was to estimate two-week and six-month stability of identifying persons with LCHL using the CHLT-6 with a discrete latent variable approach as the underlying measurement structure. Using a test-retest design, the CHLT-6 was administered to cancer patients with two-week (N=98) and six-month (N=51) intervals. The two-week and six-month latent test-retest agreements were 89% and 88%, respectively. The chance-corrected latent agreements estimated from Dumenci’s latent kappa were 0.62 (95% CI: 0.41 – 0.82) and .47 (95% CI: 0.14 – 0.80) for the two-week and six-month intervals, respectively. High levels of latent test-retest agreement between limited and adequate categories of cancer health literacy construct, coupled with moderate to good levels of change-corrected latent agreements indicated that the CHLT-6 classification of limited versus adequate cancer health literacy is relatively stable over time. In conclusion, the measurement structure underlying the instrument allows for estimating classification errors circumventing limitations due to arbitrary approaches adopted by all other instruments. The CHLT-6 can be used to identify persons with LCHL in oncology clinics and intervention studies to accurately estimate treatment effectiveness.

Keywords: limited cancer health literacy, the CHLT-6, discrete latent variable modeling, latent agreement

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Authors: Ahmed ElAzab, Amira M. Idrees, Mahmoud A. Mahmoud, Hesham Hefny

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Social networking sites such as Twitter and Facebook attracts over 500 million users across the world, for those users, their social life, even their practical life, has become interrelated. Their interaction with social networking has affected their life forever. Accordingly, social networking sites have become among the main channels that are responsible for vast dissemination of different kinds of information during real time events. This popularity in Social networking has led to different problems including the possibility of exposing incorrect information to their users through fake accounts which results to the spread of malicious content during life events. This situation can result to a huge damage in the real world to the society in general including citizens, business entities, and others. In this paper, we present a classification method for detecting fake accounts on Twitter. The study determines the minimized set of the main factors that influence the detection of the fake accounts on Twitter, then the determined factors have been applied using different classification techniques, a comparison of the results for these techniques has been performed and the most accurate algorithm is selected according to the accuracy of the results. The study has been compared with different recent research in the same area, this comparison has proved the accuracy of the proposed study. We claim that this study can be continuously applied on Twitter social network to automatically detect the fake accounts, moreover, the study can be applied on different Social network sites such as Facebook with minor changes according to the nature of the social network which are discussed in this paper.

Keywords: fake accounts detection, classification algorithms, twitter accounts analysis, features based techniques

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Authors: George Abu-Aqil, Leah Tsror, Elad Shufan, Shaul Mordechai, Mahmoud Huleihel, Ahmad Salman

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Pectobacterium and Dickeya spp which negatively affect a wide range of crops are the main causes of the aggressive diseases of agricultural crops. These aggressive diseases are responsible for a huge economic loss in agriculture including a severe decrease in the quality of the stored vegetables and fruits. Therefore, it is important to detect these pathogenic bacteria at their early stages of infection to control their spread and consequently reduce the economic losses. In addition, early detection is vital for producing non-infected propagative material for future generations. The currently used molecular techniques for the identification of these bacteria at the strain level are expensive and laborious. Other techniques require a long time of ~48 h for detection. Thus, there is a clear need for rapid, non-expensive, accurate and reliable techniques for early detection of these bacteria. In this study, infrared spectroscopy, which is a well-known technique with all its features, was used for rapid detection of Pectobacterium and Dickeya spp. at the strain level. The bacteria were isolated from potato plants and tubers with soft rot symptoms and measured by infrared spectroscopy. The obtained spectra were analyzed using different machine learning algorithms. The performances of our approach for taxonomic classification among the bacterial samples were evaluated in terms of success rates. The success rates for the correct classification of the genus, species and strain levels were ~100%, 95.2% and 92.6% respectively.

Keywords: soft rot enterobacteriaceae (SRE), pectobacterium, dickeya, plant infections, potato, solanum tuberosum, infrared spectroscopy, machine learning

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Authors: Mahani Razali, Abd Halim Masnan, Nordin Mamat, Seah Siok Peh

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This research is based on three main objectives which are to identify children`s social interaction behaviour during computer play activities, teacher’s role and to explore teacher’s beliefs, views and knowledge about computers use in four Malaysian pre-schools.This qualitative study was carried out among 25 pre-school children and three teachers as the research sample. The data collection procedures involved structured observation which was to identify social interaction behavior among pre-school children through computer play activities; as for semi-structured interviews, it was done to study the perception of the teachers on the acquired of social interaction behavior development among the children. A variety of patterns can be seen within the peer interactions indicating that children exhibit a vast range of social interactions at the computer, and they varied each day. The findings of this study guide us to certain conclusions, which have implications in understanding the phenomena of how computers were used and how its relationship to the children’s social interactions emerge in the four Malaysian preschools. This study provides evidence that the children’s social interactions with peers and adults were mediated by the engagement of the children in the computer environments.

Keywords: computer, play, preschool, social interaction

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Authors: Monika Paleczna, Barbara Szmigielska

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The period of adolescence is a time when young people are becoming more and more active and conscious users of the digital world. One of the most frequently undertaken activities by them is computer games. Young players can choose from a wide range of games, including action, adventure, strategy, and logic games. The main aim of this study is to answer the question about the motivation of teenage players. The basic question is what motivates young players to play computer games and what motivates them to play a particular game. Fifty adolescents aged 15-17 participated in the study. They completed a questionnaire in which they determined what motivates them to play, how often they play computer games, and what type of computer games they play most often. It was found that entertainment and learning English are among the most important motives. The most important specific features related to a given game are the knowledge of its previous parts and the ability to play for free. The motives chosen by the players will be described in relation to the concepts of internal and external as well as approach and avoidance motivation. An additional purpose of this study is to present data concerning preferences regarding the type of games and the amount of time they spend playing.

Keywords: computer games, motivation, game preferences, adolescence

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Authors: Şefik Surhan Tabakoğlu, Hipolito Fernández-Palacios, Dominique Schuchardt, Mahmut Ali Gökçe, Celal Erbaş, Oğuz Taşbozan

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This study aimed to clarify some sperm quality parameters such as volumetric sperm quantity, motility, motility duration, sperm density, total number of spermatozoa and pH of meagre (Argyrosomus regius ASSO, 1801) individuals kept in farming conditions and caught from wild (las palmas, gran canary). The sperm was collected in glass tubes graded in millimetres and sperm volume registered immediately following collection by abdominal massage. The sperm quality parameters including motility, total number of spermatozoa and spermatozoa density were determined with computer assisted sperm analysis (CASA) program. The duration of spermatozoa movement was assessed using a sensitive chronometer (1/100s) that was started simultaneously with the addition of activation solution into the sample. Sperm pH was measured with standard pH electrodes within five minutes of sampling. At the end of the study, while amount of sperm (5.20±0.33 ml), duration of motility (7.23±0.7 m) and total number of spermatozoa (131.40±12.22 x10^9) were different statistically (p < 0,05), motility (% 81.03±6.59), pH (7.30±0.08), sperm density (25.27±9.42 x10^9/ml) and morphologic parameters were not significantly different between the two groups. According to our results, amount of sperm, duration of motility and total number of spermatozoa were better in farmed group than that of the other group.

Keywords: Seriola rivoliana, meagre, sperm quality, motility, motility duration

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Authors: Koray U. Erbas

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Neural networks are appealing for many applications since they are able to learn complex non-linear relationships between input and output data. As the number of neurons and layers in a neural network increase, it is possible to represent more complex relationships with automatically extracted features. Nowadays Deep Neural Networks (DNNs) are widely used in Computer Vision problems such as; classification, object detection, segmentation image editing etc. In this work, Facial Emotion Recognition task is performed by proposed Convolutional Neural Network (CNN)-based DNN architecture using FER2013 Dataset. Moreover, the effects of different hyperparameters (activation function, kernel size, initializer, batch size and network size) are investigated and ablation study results for Pooling Layer, Dropout and Batch Normalization are presented.

Keywords: convolutional neural network, deep learning, deep learning based FER, facial emotion recognition

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Authors: Joseph Ampratwum, Yaw Nyadu Offei, Afua Ntoaduro, Frank Twum

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The use of computer assistive technology has captured the attention of individuals with visual impairment. Children with visual impairments who are tactual learners have one unique need which is quite different from all other disability groups. They depend on the use of computer assistive technology for reading, writing, receiving information and sending information as well. The objective of the study was to assess students’ competencies in the use of computer assistive technology at Akropong School for the Blind in Ghana. This became necessary because little research has been conducted to document the competencies and challenges in the use of computer among students with visual impairments in Africa. A case study design with a mixed research strategy was adopted for the study. A purposive sampling technique was used to sample 35 students from Akropong School for the Blind in the eastern region of Ghana. The researcher gathered both quantitative and qualitative data to measure students’ competencies in keyboarding skills and Job Access with Speech (JAWS), as well as the other challenges. The findings indicated that comparatively students’ competency in keyboard skills was higher than JAWS application use. Thus students had reached higher stages in the conscious competencies matrix in the former than the latter. It was generally noted that challenges limiting effective use of students’ competencies in computer assistive technology in the School were more personal than external influences. This was because most of the challenges were due to the individual response to the training and familiarity in developing their competencies in using computer assistive technology. Base on this it was recommended that efforts should be made to stock up the laboratory with additional computers. Directly in line with the first recommendation, it was further suggested that more practice time should be created for the students to maximize computer use. Also Licensed JAWS must be acquired by the school to advance students’ competence in using computer assistive technology.

Keywords: computer assistive technology, job access with speech, keyboard, visual impairment

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Authors: Mubashir Hussain, Mu Lv, Xiaohan Dong, Zhiyang Li, Bin Liu, Nongyue He

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Detection and classification of microbes have a vast range of applications in biomedical engineering especially in detection, characterization, and quantification of bacterial contaminants. For identification of pathogens, different techniques are emerging in the field of biomedical engineering. Latest technology uses light scattering, capable of identifying different pathogens without any need for biochemical processing. Bacterial Pathogens Identification System (BPIS) which uses a laser beam, passes through the sample and light scatters off. An assembly of photodetectors surrounded by the sample at different angles to detect the scattering of light. The algorithm of the system consists of two parts: (a) Library files, and (b) Comparator. Library files contain data of known species of bacterial microbes in the form of binned plots, while comparator compares data of unknown sample with library files. Using collected data of unknown bacterial species, highest voltage values stored in the form of peaks and arranged in 3D histograms to find the frequency of occurrence. Resulting data compared with library files of known bacterial species. If sample data matching with any library file of known bacterial species, sample identified as a matched microbe. An experiment performed to identify three different bacteria particles: Enterococcus faecalis, Pseudomonas aeruginosa, and Escherichia coli. By applying algorithm using library files of given samples, results were compromising. This system is potentially applicable to several biomedical areas, especially those related to cell morphology.

Keywords: microbial identification, laser scattering, peak identification, binned plots classification

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Authors: Mohamad H. Atyeh, Ahmad Khaldi

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The main objective of this research is to measure the relationship between the Kuwait stock Exchange (KSE) index and its two marketing sectors after the new market classification. The findings of this research are important for Public economic policy makers as they need to know if the new system (new classification) is efficient and to what level, to monitor the markets and intervene with appropriate measures. The data used are the daily index of the whole Kuwaiti market and the daily closing price, number of deals and volume of shares traded of two marketing sectors (consumer goods and consumer services) for the period from the 13th of May 2012 till the 12th of December 2016. The results indicate a positive direct impact of the closing price, volume and deals indexes of the consumer goods and the consumer services companies on the overall KSE index, volume and deals of the Kuwaiti stock market (KSE).

Keywords: correlation, market capitalization, Kuwait Stock Exchange (KSE), marketing sectors, stock performance

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Authors: Swarnadip Ghosh

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Sentiment Analysis is the process of detecting the contextual polarity of text. In other words, it determines whether a piece of writing is positive, negative or neutral.Sentiment analysis of documents holds great importance in today's world, when numerous information is stored in databases and in the world wide web. An efficient algorithm to illicit such information, would be beneficial for social, economic as well as medical purposes. In this project, we have developed an algorithm to classify a document into positive or negative. Using our algorithm, we obtained a feature set from the data, and classified the documents based on this feature set. It is important to note that, in the classification, we have not used the independence assumption, which is considered by many procedures like the Naive Bayes. This makes the algorithm more general in scope. Moreover, because of the sparsity and high dimensionality of such data, we did not use empirical distribution for estimation, but developed a method by finding degree of close clustering of the data points. We have applied our algorithm on a movie review data set obtained from IMDb and obtained satisfactory results.

Keywords: sentiment, Run's Test, cross validation, higher dimensional pmf estimation

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Authors: Manjitsingh Rajput

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Visual communication plays an important role in artificial intelligence (AI) because it enables machines to understand and interpret visual information, similar to how humans do. This abstract explores the importance of visual communication in AI and emphasizes the importance of various applications such as computer vision, object emphasis recognition, image classification and autonomous systems. In going deeper, with deep learning techniques and neural networks that modify visual understanding, In addition to AI programming, the abstract discusses challenges facing visual interfaces for AI, such as data scarcity, domain optimization, and interpretability. Visual communication and other approaches, such as natural language processing and speech recognition, have also been explored. Overall, this abstract highlights the critical role that visual communication plays in advancing AI capabilities and enabling machines to perceive and understand the world around them. The abstract also explores the integration of visual communication with other modalities like natural language processing and speech recognition, emphasizing the critical role of visual communication in AI capabilities. This methodology explores the importance of visual communication in AI development and implementation, highlighting its potential to enhance the effectiveness and accessibility of AI systems. It provides a comprehensive approach to integrating visual elements into AI systems, making them more user-friendly and efficient. In conclusion, Visual communication is crucial in AI systems for object recognition, facial analysis, and augmented reality, but challenges like data quality, interpretability, and ethics must be addressed. Visual communication enhances user experience, decision-making, accessibility, and collaboration. Developers can integrate visual elements for efficient and accessible AI systems.

Keywords: visual communication AI, computer vision, visual aid in communication, essence of visual communication.

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Authors: Mohammed A. Gharawi, Majed M. Khoja

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This paper is a report on the findings of a study conducted at the Institute of Public Administration (IPA) in Saudi Arabia. The paper applied both qualitative and quantitative research methods to assess the levels of basic computer applications’ skills among students enrolled in the preparatory programs of the institution. qualitative data have been collected from semi-structured interviews with the instructors who have previously been assigned to teach Introduction to information technology courses. Quantitative data were collected by executing a self-report questionnaire and a written statistical test. 380 enrolled students responded to the questionnaire and 142 accomplished the statistical test. The results indicate the lack of necessary skills to deal with computer applications among most of the students who are enrolled in the IPA’s preparatory programs.

Keywords: assessment, computer applications, computer literacy, Institute of Public Administration, Saudi Arabia

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Authors: Ali Mohammadi

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Many engineers use the programs that are installed on the computer, but with the arrival of the mobile phone and the possibility of designing apps, many Android programs can be designed similar to the programs that are installed on the computer, and from the mobile phone, in addition to communication Telephone and photography show a more practical use. Engineers are one of the groups that can use specialized apps to have less need to go to the office and computer, and b4a can be considered one of the simplest software for designing apps. This article introduces a number of surveying apps designed using b4a and the impact that using these apps has on productivity in this field of engineering.

Keywords: app, tunnel, total station, map

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Authors: Shu-Hui Jan, Hui-Ping Tserng, Shih-Ping Ho

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Construction project control attempts to obtain real-time as-built schedule information and to eliminate project delays by effectively enhancing dynamic schedule control and management. Suitable platforms for enhancing an as-built schedule visually during the construction phase are necessary and important for general contractors. As the application of building information modeling (BIM) becomes more common, schedule management integrated with the BIM approach becomes essential to enhance visual construction management implementation for the general contractor during the construction phase. To enhance visualization of the updated as-built schedule for the general contractor, this study presents a novel system called the Construction BIM-assisted Schedule Management (ConBIM-SM) system for general contractors in Taiwan. The primary purpose of this study is to develop a web ConBIM-SM system for the general contractor to enhance visual as-built schedule information sharing and efficiency in tracking construction as-built schedule. Finally, the ConBIM-SM system is applied to a case study of a commerce building project in Taiwan to verify its efficacy and demonstrate its effectiveness during the construction phase. The advantages of the ConBIM-SM system lie in improved project control and management efficiency for general contractors, and in providing BIM-assisted as-built schedule tracking and management, to access the most current as-built schedule information through a web browser. The case study results show that the ConBIM-SM system is an effective visual as-built schedule management platform integrated with the BIM approach for general contractors in a construction project.

Keywords: building information modeling (BIM), construction schedule management, as-built schedule management, BIM schedule updating mechanism

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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: Manaranjan Pradhan, Shailaja Grover, U. Dinesh Kumar

Abstract:

Farming community in India, as well as other parts of the world, is one of the highly stressed communities due to reasons such as increasing input costs (cost of seeds, fertilizers, pesticide), droughts, reduced revenue leading to farmer suicides. Lack of integrated farm advisory system in India adds to the farmers problems. Farmers need right information during the early stages of crop’s lifecycle to prevent damage and loss in revenue. In this paper, we use deep learning techniques to develop an early warning system for detection of crop diseases using images taken by farmers using their smart phone. The research work leads to building a smart assistant using analytics and big data which could help the farmers with early diagnosis of the crop diseases and corrective actions. The classical approach for crop disease management has been to identify diseases at crop level. Recently, ImageNet Classification using the convolutional neural network (CNN) has been successfully used to identify diseases at individual plant level. Our model uses convolution filters, max pooling, dense layers and dropouts (to avoid overfitting). The models are built for binary classification (healthy or not healthy) and multi class classification (identifying which disease). Transfer learning is used to modify the weights of parameters learnt through ImageNet dataset and apply them on crop diseases, which reduces number of epochs to learn. One shot learning is used to learn from very few images, while data augmentation techniques are used to improve accuracy with images taken from farms by using techniques such as rotation, zoom, shift and blurred images. Models built using combination of these techniques are more robust for deploying in the real world. Our model is validated using tomato crop. In India, tomato is affected by 10 different diseases. Our model achieves an accuracy of more than 95% in correctly classifying the diseases. The main contribution of our research is to create a personal assistant for farmers for managing plant disease, although the model was validated using tomato crop, it can be easily extended to other crops. The advancement of technology in computing and availability of large data has made possible the success of deep learning applications in computer vision, natural language processing, image recognition, etc. With these robust models and huge smartphone penetration, feasibility of implementation of these models is high resulting in timely advise to the farmers and thus increasing the farmers' income and reducing the input costs.

Keywords: analytics in agriculture, CNN, crop disease detection, data augmentation, image recognition, one shot learning, transfer learning

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Authors: Josef Drahorad, Milos Beran, Ondrej Vltavsky, Marian Urban, Martin Fronek, Jiri Sova

Abstract:

Engineering efforts of researchers of the Food research institute Prague and the Czech Technical University in spray drying technologies led to the introduction of a demonstrator ATOMIZER and a new technology of Carbon Dioxide-Assisted Spray Nebulization Drying (CASND). The equipment combines the spray drying technology, when the liquid to be dried is atomized by a rotary atomizer, with Carbon Dioxide Assisted Nebulization - Bubble Dryer (CAN-BD) process in an original way. A solution, emulsion or suspension is saturated by carbon dioxide at pressure up to 80 bar before the drying process. The atomization process takes place in two steps. In the first step, primary droplets are produced at the outlet of the rotary atomizer of special construction. In the second step, the primary droplets are divided in secondary droplets by the CO2 expansion from the inside of primary droplets. The secondary droplets, usually in the form of microbubbles, are rapidly dried by warm air stream at temperatures up to 60ºC and solid particles are formed in a drying chamber. Powder particles are separated from the drying air stream in a high efficiency fine powder separator. The product is frequently in the form of submicron hollow spheres. The CASND technology has been used to produce microparticulated protein concentrates for human nutrition from alternative plant sources - hemp and canola seed filtration cakes. Alkali extraction was used to extract the proteins from the filtration cakes. The protein solutions after the alkali extractions were dried with the demonstrator ATOMIZER. Aerosol particle size distribution and concentration in the draying chamber were determined by two different on-line aerosol spectrometers SMPS (Scanning Mobility Particle Sizer) and APS (Aerodynamic Particle Sizer). The protein powders were in form of hollow spheres with average particle diameter about 600 nm. The particles were characterized by the SEM method. The functional properties of the microparticulated protein concentrates were compared with the same protein concentrates dried by the conventional spray drying process. Microparticulated protein has been proven to have improved foaming and emulsifying properties, water and oil absorption capacities and formed long-term stable water dispersions. This work was supported by the research grants TH03010019 of the Technology Agency of the Czech Republic.

Keywords: carbon dioxide-assisted spray nebulization drying, canola seed, hemp seed, microparticulated proteins

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Authors: P. R. Prabhu, S. M. Kulkarni, S. S. Sharma, K. Jagannath, Achutha Kini U.

Abstract:

In the present study, response surface methodology has been used to optimize turn-assisted deep cold rolling process of AISI 4140 steel. A regression model is developed to predict surface hardness and surface roughness using response surface methodology and central composite design. In the development of predictive model, deep cold rolling force, ball diameter, initial roughness of the workpiece, and number of tool passes are considered as model variables. The rolling force and the ball diameter are the significant factors on the surface hardness and ball diameter and numbers of tool passes are found to be significant for surface roughness. The predicted surface hardness and surface roughness values and the subsequent verification experiments under the optimal operating conditions confirmed the validity of the predicted model. The absolute average error between the experimental and predicted values at the optimal combination of parameter settings for surface hardness and surface roughness is calculated as 0.16% and 1.58% respectively. Using the optimal processing parameters, the hardness is improved from 225 to 306 HV, which resulted in an increase in the near surface hardness by about 36% and the surface roughness is improved from 4.84µm to 0.252 µm, which resulted in decrease in the surface roughness by about 95%. The depth of compression is found to be more than 300µm from the microstructure analysis and this is in correlation with the results obtained from the microhardness measurements. Taylor Hobson Talysurf tester, micro Vickers hardness tester, optical microscopy and X-ray diffractometer are used to characterize the modified surface layer.

Keywords: hardness, response surface methodology, microstructure, central composite design, deep cold rolling, surface roughness

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Authors: Soha A. Bahanshal, Byung G. Kim

Abstract:

Identification of patients at high risk for hospital readmission is of crucial importance for quality health care and cost reduction. Predicting hospital readmissions among diabetic patients has been of great interest to many researchers and health decision makers. We build a prediction model to predict hospital readmission for diabetic patients within 30 days of discharge. The core of the prediction model is a modified k Nearest Neighbor called Hybrid Fuzzy Weighted k Nearest Neighbor algorithm. The prediction is performed on a patient dataset which consists of more than 70,000 patients with 50 attributes. We applied data preprocessing using different techniques in order to handle data imbalance and to fuzzify the data to suit the prediction algorithm. The model so far achieved classification accuracy of 80% compared to other models that only use k Nearest Neighbor.

Keywords: machine learning, prediction, classification, hybrid fuzzy weighted k-nearest neighbor, diabetic hospital readmission

Procedia PDF Downloads 186

Authors: Sabine Ambros, Mine Oezcelik, Evelyn Dachmann, Ulrich Kulozik

Abstract:

Nutritional quality and taste of dried fruit products is still often unsatisfactory and does not meet anymore the current consumer trends. Dried foams from fruit puree could be an attractive alternative. Due to their open-porous structure, a new sensory perception with a sudden and very intense aroma release could be generated. To make such high quality fruit snacks affordable for the consumer, a gentle but at the same time fast drying process has to be applied. Therefore, microwave-assisted freeze drying of raspberry foams was investigated in this work and compared with the conventional freeze drying technique in terms of nutritional parameters such as antioxidative capacity, anthocyanin content and vitamin C and the physical parameters colour and wettability. The following process settings were applied: 0.01 kPa chamber pressure and a maximum temperature of 30 °C for both freeze and microwave freeze drying. The influence of microwave power levels on the dried foams was investigated between 1 and 5 W/g. Intermediate microwave power settings led to the highest nutritional values, a colour appearance comparable to the undried foam and a proper wettability. A proper process stability could also be guaranteed for these power levels. By the volumetric energy input of the microwaves drying time could be reduced from 24 h in conventional freeze drying to about 6 h. The short drying times further resulted in an equally high maintenance of the above mentioned parameters in both drying techniques. Hence, microwave assisted freeze drying could lead to a process acceleration in comparison to freeze drying and be therefore an interesting alternative drying technique which on industrial scale enables higher efficiency and higher product throughput.

Keywords: foam drying, freeze drying, fruit puree, microwave freeze drying, raspberry

Procedia PDF Downloads 341

Authors: Bhekisipho Twala, Mantepu Masetshaba, Ramapulana Nkoana

Abstract:

Multiple classifier systems combine several individual classifiers to deliver a final classification decision. However, an increasingly controversial question is whether such systems can outperform the single best classifier, and if so, what form of multiple classifiers system yields the most significant benefit. Also, multi-target tracking detection using multiple sensors is an important research field in mobile techniques and military applications. In this paper, several multiple classifiers systems are evaluated in terms of their ability to predict a system’s failure or success for multi-sensor target tracking tasks. The Bristol Eden project dataset is utilised for this task. Experimental and simulation results show that the human activity identification system can fulfill requirements of target tracking due to improved sensors classification performances with multiple classifier systems constructed using boosting achieving higher accuracy rates.

Keywords: single classifier, ensemble learning, multi-target tracking, multiple classifiers

Procedia PDF Downloads 268