Search results for: pattern classification

Authors: Yangrae Cho, Jinseok Kim, Yongtae Park

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Mobile app service ecosystem has been abruptly emerged, explosively grown, and dynamically transformed. In contrast with product markets in which product sales directly cause increment in firm’s income, customer’s usage is less visible but more valuable in service market. Especially, the market situation with cutthroat competition in mobile app store makes securing and keeping of users as vital. Although a few service firms try to manage their apps’ usage patterns by fitting on S-curve or applying other forecasting techniques, the time series approaches based on past sequential data are subject to fundamental limitation in the market where customer’s attention is being moved unpredictably and dynamically. We therefore propose a new conceptual approach for detecting usage pattern of mobile app service with Hidden Markov Model (HMM) which is based on the dual stochastic structure and mainly used to clarify unpredictable and dynamic sequential patterns in voice recognition or stock forecasting. Our approach could be practically utilized for app service firms to manage their services’ lifecycles and academically expanded to other markets.

Keywords: mobile app service, usage pattern, Hidden Markov Model, pattern detection

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Authors: Bin Wang, Jian Kang

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After years of development, the study of soundscape has been refined to the types of urban space and building. Traffic complex takes traffic function as the core, with obvious design features of architectural space combination and traffic streamline. The acoustic environment is strongly characterized by function, space, material, user and other factors. Traffic complex integrates various functions of business, accommodation, entertainment and so on. It has various forms, complex and varied experiences, and its acoustic environment is turned rich and interesting with distribution and coordination of various functions, division and unification of the mass, separation and organization of different space and the cross and the integration of multiple traffic flow. In this study, it made field recordings of each space of various traffic complex, and extracted and analyzed different acoustic elements, including changes in sound pressure, frequency distribution, steady sound source, sound source information and other aspects, to make cluster analysis of each independent traffic complex buildings. It divided complicated traffic complex building space into several typical sound space from acoustic environment perspective, mainly including stable sound space, high-pressure sound space, rhythm sound space and upheaval sound space. This classification can further deepen the study of subjective evaluation and control of the acoustic environment of traffic complex.

Keywords: soundscape, traffic complex, cluster analysis, classification

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Authors: Igor Belykh

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Anti-scatter grids used in radiographic imaging for the contrast enhancement leave specific artifacts. Those artifacts may be visible or may cause Moiré effect when a digital image is resized on a diagnostic monitor. In this paper, we propose an automated grid artifacts detection and suppression algorithm which is still an actual problem. Grid artifacts detection is based on statistical approach in spatial domain. Grid artifacts suppression is based on Kaiser bandstop filter transfer function design and application avoiding ringing artifacts. Experimental results are discussed and concluded with description of advantages over existing approaches.

Keywords: grid, computed radiography, pattern recognition, image processing, filtering

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Authors: Esmaeil Abbaszadeh Molaei, Zongyan Zhou, Aibing Yu

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The applications of liquid fluidizations have been increased in many parts of industries such as particle classification, backwashing of granular filters, crystal growth, leaching and washing, and bioreactors due to high-efficient liquid–solid contact, favorable mass and heat transfer, high operation flexibilities, and reduced back mixing of phases. In most of these multiphase operations the particles properties, i.e. size, density, and shape, may change during the process because of attrition, coalescence or chemical reactions. Previous studies, either experimentally or numerically, mainly have focused on studies of liquid-solid fluidized beds containing spherical particles; however, the role of particle shape on the hydrodynamics of liquid fluidized beds is still not well-known. A three-dimensional Discrete Element Model (DEM) and Computational Fluid Dynamics (CFD) are coupled to study the influence of particles shape on particles and liquid flow patterns in liquid-solid fluidized beds. In the simulations, ellipsoid particles are used to study the shape factor since they can represent a wide range of particles shape from oblate and sphere to prolate shape particles. Different particle shapes from oblate (disk shape) to elongated particles (rod shape) are selected to investigate the effect of aspect ratio on different flow characteristics such as general particles and liquid flow pattern, pressure drop, and particles orientation. First, the model is verified based on experimental observations, then further detail analyses are made. It was found that spherical particles showed a uniform particle distribution in the bed, which resulted in uniform pressure drop along the bed height. However for particles with aspect ratios less than one (disk-shape), some particles were carried into the freeboard region, and the interface between the bed and freeboard was not easy to be determined. A few particle also intended to leave the bed. On the other hand, prolate particles showed different behaviour in the bed. They caused unstable interface and some flow channeling was observed for low liquid velocities. Because of the non-uniform particles flow pattern for particles with aspect ratios lower (oblate) and more (prolate) than one, the pressure drop distribution in the bed was not observed as uniform as what was found for spherical particles.

Keywords: CFD, DEM, ellipsoid, fluidization, multiphase flow, non-spherical, simulation

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Authors: Rahul Magazine, Shobitha Rao

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The aim of this study was to study the prescribing pattern of drugs in patients with ARDS (Acute Respiratory Distress Syndrome) managed at a tertiary care hospital. This observational study was conducted at Kasturba Hospital, Karnataka, India. Data of patients admitted from January 2010 to December 2012 was collected. A total of 150 patients of ARDS were included. Data included patients’ age, gender, clinical disorders precipitating ARDS, and prescribing pattern of drugs. The mean age of the study population was 42.92±13.91 years. 48% of patients were less than 40 years of age. Infection was the cause of ARDS in 81.3% of subjects. Antibiotics were prescribed in all the subjects and beta-lactams were prescribed in 97.3%. 41.3% were prescribed corticosteroids, 39.3% diuretics and 89.3% intravenous fluids. Infection was the commonest etiology for ARDS, and beta-lactams were the commonest antibiotics prescribed. Corticosteroids and diuretics were prescribed in a significant number of patients. Most of the patients received intravenous fluids.

Keywords: acute respiratory syndrome, beta lactams, corticosteroids, Acute Respiratory Distress Syndrome (ARDS)

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Authors: Emna Benmohamed, Hela Ltifi, Mounir Ben Ayed

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Bayesian Network (BN) is one of the most efficient classification methods. It is widely used in several fields (i.e., medical diagnostics, risk analysis, bioinformatics research). The BN is defined as a probabilistic graphical model that represents a formalism for reasoning under uncertainty. This classification method has a high-performance rate in the extraction of new knowledge from data. The construction of this model consists of two phases for structure learning and parameter learning. For solving this problem, the K2 algorithm is one of the representative data-driven algorithms, which is based on score and search approach. In addition, the integration of the expert's knowledge in the structure learning process allows the obtainment of the highest accuracy. In this paper, we propose a hybrid approach combining the improvement of the K2 algorithm called K2 algorithm for Parents and Children search (K2PC) and the expert-driven method for learning the structure of BN. The evaluation of the experimental results, using the well-known benchmarks, proves that our K2PC algorithm has better performance in terms of correct structure detection. The real application of our model shows its efficiency in the analysis of the phosphate laundry effluents' impact on the watershed in the Gafsa area (southwestern Tunisia).

Keywords: Bayesian network, classification, expert knowledge, structure learning, surface water analysis

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Authors: Atsuko Okazaki, Jurg Ott

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Some genetic diseases (‘digenic traits’) are due to the interaction between two DNA variants. For example, certain forms of Retinitis Pigmentosa (a genetic form of blindness) occur in the presence of two mutant variants, one in the ROM1 gene and one in the RDS gene, while the occurrence of only one of these mutant variants leads to a completely normal phenotype. Detecting such digenic traits by genetic methods is difficult. A common approach to finding disease-causing variants is to compare 100,000s of variants between individuals with a trait (cases) and those without the trait (controls). Such genome-wide association studies (GWASs) have been very successful but hinge on genetic effects of single variants, that is, there should be a difference in allele or genotype frequencies between cases and controls at a disease-causing variant. Frequent pattern mining (FPM) methods offer an avenue at detecting digenic traits even in the absence of single-variant effects. The idea is to enumerate pairs of genotypes (genotype patterns) with each of the two genotypes originating from different variants that may be located at very different genomic positions. What is needed is for genotype patterns to be significantly more common in cases than in controls. Let Y = 2 refer to cases and Y = 1 to controls, with X denoting a specific genotype pattern. We are seeking association rules, ‘X → Y’, with high confidence, P(Y = 2|X), significantly higher than the proportion of cases, P(Y = 2) in the study. Clearly, generally available FPM methods are very suitable for detecting disease-associated genotype patterns. We use fpgrowth as the basic FPM algorithm and built a framework around it to enumerate high-frequency digenic genotype patterns and to evaluate their statistical significance by permutation analysis. Application to a published dataset on opioid dependence furnished results that could not be found with classical GWAS methodology. There were 143 cases and 153 healthy controls, each genotyped for 82 variants in eight genes of the opioid system. The aim was to find out whether any of these variants were disease-associated. The single-variant analysis did not lead to significant results. Application of our FPM implementation resulted in one significant (p < 0.01) genotype pattern with both genotypes in the pattern being heterozygous and originating from two variants on different chromosomes. This pattern occurred in 14 cases and none of the controls. Thus, the pattern seems quite specific to this form of substance abuse and is also rather predictive of disease. An algorithm called Multifactor Dimension Reduction (MDR) was developed some 20 years ago and has been in use in human genetics ever since. This and our algorithms share some similar properties, but they are also very different in other respects. The main difference seems to be that our algorithm focuses on patterns of genotypes while the main object of inference in MDR is the 3 × 3 table of genotypes at two variants.

Keywords: digenic traits, DNA variants, epistasis, statistical genetics

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Authors: Seonaid Hume, Gordon Dobie, Graeme West

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Detecting and localizing radioactive sources is a necessity for safe and secure decommissioning of nuclear facilities. An important aspect for the management of the sort-and-segregation process is establishing the spatial distributions and quantities of the waste radionuclides, their type, corresponding activity, and ultimately classification for disposal. The data received from surveys directly informs decommissioning plans, on-site incident management strategies, the approach needed for a new cell, as well as protecting the workforce and the public. Manual classification of nuclear waste from a nuclear cell is time-consuming, expensive, and requires significant expertise to make the classification judgment call. Also, in-cell decommissioning is still in its relative infancy, and few techniques are well-developed. As with any repetitive and routine tasks, there is the opportunity to improve the task of classifying nuclear waste using autonomous systems. Hence, this paper proposes a new framework for the automatic classification of nuclear waste. This framework consists of five main stages; 3D spatial mapping and object detection, object classification, radiological mapping, source localisation based on gathered evidence and finally, waste classification. The first stage of the framework, 3D visual mapping, involves object detection from point cloud data. A review of related applications in other industries is provided, and recommendations for approaches for waste classification are made. Object detection focusses initially on cylindrical objects since pipework is significant in nuclear cells and indeed any industrial site. The approach can be extended to other commonly occurring primitives such as spheres and cubes. This is in preparation of stage two, characterizing the point cloud data and estimating the dimensions, material, degradation, and mass of the objects detected in order to feature match them to an inventory of possible items found in that nuclear cell. Many items in nuclear cells are one-offs, have limited or poor drawings available, or have been modified since installation, and have complex interiors, which often and inadvertently pose difficulties when accessing certain zones and identifying waste remotely. Hence, this may require expert input to feature match objects. The third stage, radiological mapping, is similar in order to facilitate the characterization of the nuclear cell in terms of radiation fields, including the type of radiation, activity, and location within the nuclear cell. The fourth stage of the framework takes the visual map for stage 1, the object characterization from stage 2, and radiation map from stage 3 and fuses them together, providing a more detailed scene of the nuclear cell by identifying the location of radioactive materials in three dimensions. The last stage involves combining the evidence from the fused data sets to reveal the classification of the waste in Bq/kg, thus enabling better decision making and monitoring for in-cell decommissioning. The presentation of the framework is supported by representative case study data drawn from an application in decommissioning from a UK nuclear facility. This framework utilises recent advancements of the detection and mapping capabilities of complex radiation fields in three dimensions to make the process of classifying nuclear waste faster, more reliable, cost-effective and safer.

Keywords: nuclear decommissioning, radiation detection, object detection, waste classification

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Authors: Gonca Z. Tuncbilek

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Architectural research tends to define architecture in terms of its permanence. In this study, the term ‘temporality’ and its use in architectural discourse is re-visited. The definition, proposition, and efficacy of the temporality occur both in architecture and in its related knowledge. The temporary architecture not only fulfills the requirement of the architectural programs, but also plays a significant role in generating an environment of architectural discourse. In recent decades, there is a great interest on the temporary architectural practices regarding to the installations, exhibition spaces, pavilions, and expositions; inviting the architects to experience and think about architecture. The temporary architecture has a significant role among the architecture, the architect, and the architectural discourse. Experiencing the contemporary materials, methods and technique; they have proposed the possibilities of the future architecture. These structures give opportunities to the architects to a wide-ranging variety of freedoms to experience the ‘new’ in architecture. In addition to this experimentation, they can be considered as an agent to redefine and reform the boundaries of the architectural discipline itself. Although the definition of architecture is re-analyzed in terms of its temporality rather than its permanence; architecture, in reality, still relies on historically codified types and principles of the formation. The concept of type can be considered for several different sciences, and there is a tendency to organize and understand the world in terms of classification in many different cultures and places. ‘Type’ is used as a classification tool with/without the scope of the critical invention. This study considers theories of type, putting forward epistemological and discursive arguments related to the form of architecture, being related to historical and formal disciplinary knowledge in architecture. This study has been to emphasize the importance of the temporality in architecture as a creative tool to reveal the position within the architectural discourse. The temporary architecture offers ‘new’ opportunities in the architectural field to be analyzed. In brief, temporary structures allow the architect freedoms to the experimentation in architecture. While redefining the architecture in terms of temporality, architecture still relies on historically codified types (pavilions, exhibitions, expositions, and installations). The notion of architectural types and its varying interpretations are analyzed based on the texts of architectural theorists since the Age of Enlightenment. Investigating the classification of type in architecture particularly temporary architecture, it is necessary to return to the discussion of the origin of the knowledge and its classification.

Keywords: classification of architecture, exhibition design, pavilion design, temporary architecture

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Authors: Shaojian Li

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The traditional Chinese patterns, as an integral part of Chinese culture, possess unique values in history, culture, and art. However, with the passage of time and societal changes, many of these traditional patterns are at risk of being lost, damaged, or forgotten. To undertake the digital preservation and protection of these traditional patterns, this paper will collect and organize images of traditional Chinese patterns. It will provide exhaustive and comprehensive semantic annotations, creating a resource library of traditional Chinese pattern images. This will support the digital preservation and application of traditional Chinese patterns.

Keywords: digitization of cultural heritage, traditional Chinese patterns, digital humanities, database construction

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Authors: Ebrahim Taherzadeh, Helmi Z. M. Shafri, Kaveh Shahi

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One of the most important tasks in urban area remote sensing is detection of impervious surface (IS), such as building roof and roads. However, detection of IS in heterogeneous areas still remains as one of the most challenging works. In this study, detection of concrete roof using an object-oriented approach was proposed. A new rule-based classification was developed to detect concrete roof tile. The proposed rule-based classification was applied to WorldView-2 image. Results showed that the proposed rule has good potential to predict concrete roof material from WorldView-2 images with 85% accuracy.

Keywords: object-based, roof material, concrete tile, WorldView-2

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Authors: Yahaya Abdullahi, Ben Coetzee, Linda Van Den Berg

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The study aimed at establishing the global positioning system (GPS) determined singles match characteristics that act as predictors of successful and less-successful male singles badminton players’ group classification. Twenty-two (22) male single players (aged: 23.39 ± 3.92 years; body stature: 177.11 ± 3.06cm; body mass: 83.46 ± 14.59kg) who represented 10 African countries participated in the study. Players were categorised as successful and less-successful players according to the results of five championships’ of the 2014/2015 season. GPS units (MinimaxX V4.0), Polar Heart Rate Transmitter Belts and digital video cameras were used to collect match data. GPS-related variables were corrected for match duration and independent t-tests, a cluster analysis and a binary forward stepwise logistic regression were calculated. A Receiver Operating Characteristic Curve (ROC) was used to determine the validity of the group classification model. High-intensity accelerations per second were identified as the only GPS-determined variable that showed a significant difference between groups. Furthermore, only high-intensity accelerations per second (p=0.03) and low-intensity efforts per second (p=0.04) were identified as significant predictors of group classification with 76.88% of players that could be classified back into their original groups by making use of the GPS-based logistic regression formula. The ROC showed a value of 0.87. The identification of the last-mentioned GPS-related variables for the attainment of badminton performances, emphasizes the importance of using badminton drills and conditioning techniques to not only improve players’ physical fitness levels but also their abilities to accelerate at high intensities.

Keywords: badminton, global positioning system, match analysis, inertial movement analysis, intensity, effort

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Authors: Feda Negesse

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One of the most important indicators of research quality is a good data - collection instrument that can yield reliable and valid data. The Swadesh wordlist has been used for more than half a century for collecting data in comparative and historical linguistics though arbitrariness is observed in its application and size. This research compare s the classification results of the 100 Swadesh wordlist with those of its subsets to determine if reducing the size of the wordlist impact s its effectiveness. In the comparison, the 100, 50 and 40 wordlists were used to compute lexical distances of 29 Cushitic and Semitic languages spoken in Ethiopia and neighbouring countries. Gabmap, a based application, was employed to compute the lexical distances and to divide the languages into related clusters. The study shows that the subsets are not as effective as the 100 wordlist in clustering languages into smaller subgroups but they are equally effective in di viding languages into bigger groups such as subfamilies. It is noted that the subsets may lead to an erroneous classification whereby unrelated languages by chance form a cluster which is not attested by a comparative study. The chance to get a wrong result is higher when the subsets are used to classify languages which are not closely related. Though a further study is still needed to settle the issues around the size of the Swadesh wordlist, this study indicates that the 50 and 40 wordlists cannot be recommended as reliable substitute s for the 100 wordlist under all circumstances. The choice seems to be determined by the objective of a researcher and the degree of affiliation among the languages to be classified.

Keywords: classification, Cushitic, Swadesh, wordlist

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Authors: Baha Eddine Aissou, Aichouche Belhadj Aissa

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Light detection and ranging (LiDAR) is an active remote sensing technology used for several applications. Airborne LiDAR is becoming an important technology for the acquisition of a highly accurate dense point cloud. A classification of airborne laser scanning (ALS) point cloud is a very important task that still remains a real challenge for many scientists. Support vector machine (SVM) is one of the most used statistical learning algorithms based on kernels. SVM is a non-parametric method, and it is recommended to be used in cases where the data distribution cannot be well modeled by a standard parametric probability density function. Using a kernel, it performs a robust non-linear classification of samples. Often, the data are rarely linearly separable. SVMs are able to map the data into a higher-dimensional space to become linearly separable, which allows performing all the computations in the original space. This is one of the main reasons that SVMs are well suited for high-dimensional classification problems. Only a few training samples, called support vectors, are required. SVM has also shown its potential to cope with uncertainty in data caused by noise and fluctuation, and it is computationally efficient as compared to several other methods. Such properties are particularly suited for remote sensing classification problems and explain their recent adoption. In this poster, the SVM classification of ALS LiDAR data is proposed. Firstly, connected component analysis is applied for clustering the point cloud. Secondly, the resulting clusters are incorporated in the SVM classifier. Radial basic function (RFB) kernel is used due to the few numbers of parameters (C and γ) that needs to be chosen, which decreases the computation time. In order to optimize the classification rates, the parameters selection is explored. It consists to find the parameters (C and γ) leading to the best overall accuracy using grid search and 5-fold cross-validation. The exploited LiDAR point cloud is provided by the German Society for Photogrammetry, Remote Sensing, and Geoinformation. The ALS data used is characterized by a low density (4-6 points/m²) and is covering an urban area located in residential parts of the city Vaihingen in southern Germany. The class ground and three other classes belonging to roof superstructures are considered, i.e., a total of 4 classes. The training and test sets are selected randomly several times. The obtained results demonstrated that a parameters selection can orient the selection in a restricted interval of (C and γ) that can be further explored but does not systematically lead to the optimal rates. The SVM classifier with hyper-parameters is compared with the most used classifiers in literature for LiDAR data, random forest, AdaBoost, and decision tree. The comparison showed the superiority of the SVM classifier using parameters selection for LiDAR data compared to other classifiers.

Keywords: classification, airborne LiDAR, parameters selection, support vector machine

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Authors: Maryam Ghavam Sadri, Kimia Moiniafshari

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Objectives: The prevalence of Autism in the world has taken on a growing trend. Autism is a neuro-developmental disorder that is identified at the age of three. Studies have been shown that nutritional management can control nutritional deficiencies in Autism. This review study aimed to assess the role of nutritional management strategies for Autism in children has been made. Methods: This review study was accomplished by using the keywords related to the topic, 68 articles were found (2000-2015) and finally 15 articles with criteria such as including dietary pattern, nutritional deficiencies and Autism controlling were selected. Results: The studies showed that intake of vitamins D, E, and calcium because of restricted diet (casein and gluten free) in autistic children is less than typically developing children (TYP) (p value ≤ 0.001) and as a result of restrictions on the consumption of fresh fruits and vegetables, vitamin C and magnesium intake is less than TYP children (p value ≤ 0.001). Autistic children also get omega-3 less than TYP children. Studies have shown that food sources rich in omega-3 can improve behavioral indicators, especially in reducing hyperactivity (95% CI = -2.2 - 5.2). Zinc deficiency in these children leads to a high serum level of mercury, lead and cadmium. As a result of the repetitive dietary pattern, Sodium intake in autistic children is more than TYP children (p value < 0.001).Because of low food variety in autistic children, healthy eating index (HEI) is less than TYP children (p value = 0.008).Food selectivity in Autism due to repetitive and restricted dietary pattern and nutritional deficiencies. Conclusion: Because of restricted (casein and gluten free) and repetitive dietary pattern, the intake of some micronutrients are denied in autistic children. The nutritional strategy programs appear to help controlling of Autism.

Keywords: autism, food selectivity, nutrient intake, nutritional strategies

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Authors: Mahtab Davari, Charles Edward Okon, Somayeh Aghanavesi

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Every institute of learning is usually interested in the performance of enrolled students. The level of these performances determines the approach an institute of study may adopt in rendering academic services. The focus of this paper is to evaluate students' academic performance in given courses of study using machine learning methods. This study evaluated various supervised machine learning classification algorithms such as Logistic Regression (LR), Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbors, Linear Discriminant Analysis, and Quadratic Discriminant Analysis, using selected features to predict study performance. The accuracy, precision, recall, and F1 score obtained from a 5-Fold Cross-Validation were used to determine the best classification algorithm to predict students’ performances. SVM (using a linear kernel), LDA, and LR were identified as the best-performing machine learning methods. Also, using the LR model, this study identified students' educational habits such as reading and paying attention in class as strong determinants for a student to have an above-average performance. Other important features include the academic history of the student and work. Demographic factors such as age, gender, high school graduation, etc., had no significant effect on a student's performance.

Keywords: student performance, supervised machine learning, classification, cross-validation, prediction

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Authors: Girish J. Showkatramani, Arthi M. Krishna, Sashi Nareddi, Naresh Nula, Aaron Pepe, Glen Brown, Greg Gabel, Chris Doninger

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Trademark examination and approval is a complex process that involves analysis and review of the design components of the marks such as the visual representation as well as the textual data associated with marks such as marks' description. Currently, the process of identifying marks with similar visual representation is done manually in United States Patent and Trademark Office (USPTO) and takes a considerable amount of time. Moreover, the accuracy of these searches depends heavily on the experts determining the trademark design codes used to catalog the visual design codes in the mark. In this study, we explore several methods to automate trademark design code classification. Based on recent successes of convolutional neural networks in image classification, we have used several different convolutional neural networks such as Google’s Inception v3, Inception-ResNet-v2, and Xception net. The study also looks into other techniques to augment the results from CNNs such as using Open Source Computer Vision Library (OpenCV) to pre-process the images. This paper reports the results of the various models trained on year of annotated trademark images.

Keywords: trademark design code, convolutional neural networks, trademark image classification, trademark image search, Inception-ResNet-v2

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Authors: Arturo Maldonado

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In the iron ore mining industry, the intact strength of rock units is defined using the uniaxial compressive strength (UCS). This parameter is very important for the classification of shale materials, allowing the split between rock and cohesive soils based on the magnitude of UCS. For this research, it is assumed that UCS less than or equal to 1 MPa is representative of soils. Several researchers have anticipated that the magnitude of UCS reduces with weathering progression, also since UCS is a directional property, its magnitude depends upon the rock fabric orientation. Thus, the paper presents how the UCS of shales is affected by both weathering grade and bedding orientation. The mineralogy of shales has been defined using Hyper-spectral and chemical assays to define the mineral constituents of shale and other non-shale materials. Geological classification tools have been used to define distinct lithological types, and in this manner, the author uses mineralogical datasets to recognize and isolate shales from other rock types and develop tertiary plots for fresh and weathered shales. The mineralogical classification of shales has reduced the contamination of lithology types and facilitated the study of the physical factors affecting the intact strength of shales, like anisotropic strength due to bedding orientation. The analysis of mineralogical characteristics of shales is perhaps the most important contribution of this paper to other researchers who may wish to explore similar methods.

Keywords: rock mechanics, mineralogy, shales, weathering, anisotropy

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Authors: Carlos Tarmeño Noriega, Igor Aguilar Alonso

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Fungal diseases are common in vineyards; they cause a decrease in the quality of the products that can be sold, generating distrust of the customer towards the seller when buying fruit. Currently, technology allows the classification of fruits according to their characteristics thanks to artificial intelligence. This study proposes the implementation of a control system that allows the identification of the main fungal diseases present in the Italia grape, making use of a convolutional neural network (CNN), OpenCV, and TensorFlow. The methodology used was based on a collection of 20 articles referring to the proposed research on quality control, classification, and recognition of fruits through artificial vision techniques.

Keywords: computer vision, convolutional neural networks, quality control, fruit market, OpenCV, TensorFlow

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Authors: Ruchika Malhotra, Megha Khanna

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The development of change prediction models can help the software practitioners in planning testing and inspection resources at early phases of software development. However, a major challenge faced during the training process of any classification model is the imbalanced nature of the software quality data. A data with very few minority outcome categories leads to inefficient learning process and a classification model developed from the imbalanced data generally does not predict these minority categories correctly. Thus, for a given dataset, a minority of classes may be change prone whereas a majority of classes may be non-change prone. This study explores various alternatives for adeptly handling the imbalanced software quality data using different sampling methods and effective MetaCost learners. The study also analyzes and justifies the use of different performance metrics while dealing with the imbalanced data. In order to empirically validate different alternatives, the study uses change data from three application packages of open-source Android data set and evaluates the performance of six different machine learning techniques. The results of the study indicate extensive improvement in the performance of the classification models when using resampling method and robust performance measures.

Keywords: change proneness, empirical validation, imbalanced learning, machine learning techniques, object-oriented metrics

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Authors: Levent Basayigit, Sinan Demir, Burhan Kara, Yusuf Ucar

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Cannabis is mostly used for drug production. In some countries, an excessive amount of illegal cannabis is cultivated and sold. Most of the illegal cannabis cultivation occurs on the lands far from settlements. In farmlands, it is cultivated with other crops. In this method, cannabis is surrounded by tall plants like corn and sunflower. It is also cultivated with tall crops as the mixed culture. The common method of the determination of the illegal cultivation areas is to investigate the information obtained from people. This method is not sufficient for the determination of illegal cultivation in remote areas. For this reason, more effective methods are needed for the determination of illegal cultivation. Remote Sensing is one of the most important technologies to monitor the plant growth on the land. The aim of this study is to monitor cannabis cultivation area using satellite imagery. The main purpose of this study was to develop an applicable method for monitoring the cannabis cultivation. For this purpose, cannabis was grown as single or surrounded by the corn and sunflower in plots. The morphological characteristics of cannabis were recorded two times per month during the vegetation period. The spectral signature library was created with the spectroradiometer. The parcels were monitored with high-resolution satellite imagery. With the processing of satellite imagery, the cultivation areas of cannabis were classified. To separate the Cannabis plots from the other plants, the multiresolution segmentation algorithm was found to be the most successful for classification. WorldView Improved Vegetative Index (WV-VI) classification was the most accurate method for monitoring the plant density. As a result, an object-based classification method and vegetation indices were sufficient for monitoring the cannabis cultivation in multi-temporal Earthwiev images.

Keywords: Cannabis, drug, remote sensing, object-based classification

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Authors: Lily Ingsrisawang, Tasanee Nacharoen

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Introduction: The problems of unbalanced data sets generally appear in real world applications. Due to unequal class distribution, many research papers found that the performance of existing classifier tends to be biased towards the majority class. The k -nearest neighbors’ nonparametric discriminant analysis is one method that was proposed for classifying unbalanced classes with good performance. Hence, the methods of discriminant analysis are of interest to us in investigating misclassification error rates for class-imbalanced data of three diabetes risk groups. Objective: The purpose of this study was to compare the classification performance between parametric discriminant analysis and nonparametric discriminant analysis in a three-class classification application of class-imbalanced data of diabetes risk groups. Methods: Data from a healthy project for 599 staffs in a government hospital in Bangkok were obtained for the classification problem. The staffs were diagnosed into one of three diabetes risk groups: non-risk (90%), risk (5%), and diabetic (5%). The original data along with the variables; diabetes risk group, age, gender, cholesterol, and BMI was analyzed and bootstrapped up to 50 and 100 samples, 599 observations per sample, for additional estimation of misclassification error rate. Each data set was explored for the departure of multivariate normality and the equality of covariance matrices of the three risk groups. Both the original data and the bootstrap samples show non-normality and unequal covariance matrices. The parametric linear discriminant function, quadratic discriminant function, and the nonparametric k-nearest neighbors’ discriminant function were performed over 50 and 100 bootstrap samples and applied to the original data. In finding the optimal classification rule, the choices of prior probabilities were set up for both equal proportions (0.33: 0.33: 0.33) and unequal proportions with three choices of (0.90:0.05:0.05), (0.80: 0.10: 0.10) or (0.70, 0.15, 0.15). Results: The results from 50 and 100 bootstrap samples indicated that the k-nearest neighbors approach when k = 3 or k = 4 and the prior probabilities of {non-risk:risk:diabetic} as {0.90:0.05:0.05} or {0.80:0.10:0.10} gave the smallest error rate of misclassification. Conclusion: The k-nearest neighbors approach would be suggested for classifying a three-class-imbalanced data of diabetes risk groups.

Keywords: error rate, bootstrap, diabetes risk groups, k-nearest neighbors

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Authors: Danilo Habermann, Aleksander Medella, Carla Cremon, Yusef Caceres

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This paper aims to analyze the ability of 2d point clouds features to classify different models of helicopters using radars. This method does not need to estimate the blade length, the number of blades of helicopters, and the period of their micro-Doppler signatures. It is also not necessary to generate spectrograms (or any other image based on time and frequency domain). This work transforms a radar return signal into a 2D point cloud and extracts features of it. Three classifiers are used to distinguish 9 different helicopter models in order to analyze the performance of the features used in this work. The high accuracy obtained with each of the classifiers demonstrates that the 2D point clouds features are very useful for classifying helicopters from radar signal.

Keywords: helicopter classification, point clouds features, radar, supervised classifiers

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Authors: Sarika Gupta, Harshika Khanna, Ajay K Verma, Surya Kant

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Background: Drug-resistant tuberculosis (DR-TB) is a growing health challenge to global TB control efforts. Pediatric DR-TB is one of the neglected infectious diseases. In our previously published report, we have notified an increased prevalence of DR-TB in the pediatric population at a tertiary health care centre in North India which was estimated as 17.4%, 15.1%, 18.4%, and 20.3% in (%) in the year 2018, 2019, 2020, and 2021. Limited evidence exists about a pattern of drug resistance, side effect profile and programmatic outcomes of Paediatric DR-TB treatment. Therefore, this study was done to find out the pattern of resistance, side effect profile and treatment outcome. Methodology: This was a prospective cohort study conducted at the nodal drug-resistant tuberculosis centre of a tertiary care hospital in North India from January 2021 to December 2022. Subjects included children aged between 0-18 years of age with a diagnosis of DR-TB, on the basis of GeneXpert (rifampicin [RIF] resistance detected), line probe assay and drug sensitivity testing (DST) of M. tuberculosis (MTB) grown on a culture of body fluids. Children were classified as monoresistant TB, polyresistant TB (resistance to more than 1 first-line anti-TB drug, other than both INH and RIF), MDR-TB, pre-XDR-TB and XDR-TB, as per the WHO classification. All the patients were prescribed DR TB treatment as per the standard guidelines, either shorter oral DR-TB regimen or a longer all-oral MDR/XDR-TB regimen (age below five years needed modification). All the patients were followed up for side effects of treatment once per month. The patient outcomes were categorized as good outcomes if they had completed treatment and cured or were improving during the course of treatment, while bad outcomes included death or not improving during the course of treatment. Results: Of the 50 pediatric patients included in the study, 34 were females (66.7%) and 16 were male (31.4%). Around 33 patients (64.7%) were suffering from pulmonary TB, while 17 (33.3%) were suffering from extrapulmonary TB. The proportions of monoresistant TB, polyresistant TB, MDR-TB, pre-XDR-TB and XDR-TB were 2.0%, 0%, 50.0%, 30.0% and 18.0%, respectively. Good outcome was reported in 40 patients (80.0%). The 10 bad outcomes were 7 deaths (14%) and 3 (6.0%) children who were not improving. Adverse events (single or multiple) were reported in all the patients, most of which were mild in nature. The most common adverse events were metallic taste 16(31.4%), rash and allergic reaction 15(29.4%), nausea and vomiting 13(26.0%), arthralgia 11 (21.6%) and alopecia 11 (21.6%). Serious adverse event of QTc prolongation was reported in 4 cases (7.8%), but neither arrhythmias nor symptomatic cardiac side effects occurred. Vestibular toxicity was reported in 2(3.9%), and psychotic symptoms in 4(7.8%). Hepatotoxicity, hypothyroidism, peripheral neuropathy, gynaecomastia, and amenorrhea were reported in 2 (4.0%), 4 (7.8%), 2 (3.9%), 1(2.0%), and 2 (3.9%) respectively. None of the drugs needed to be withdrawn due to uncontrolled adverse events. Conclusion: Paediatric DR TB treatment achieved favorable outcomes in a large proportion of children. DR TB treatment regimen drugs were overall well tolerated in this cohort.

Keywords: pediatric, drug-resistant, tuberculosis, adverse events, treatment

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Authors: Ali Rabiee, Hessam Ghasemnejad

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Extensive experimental investigation on the effect of stitching pattern on tubular composite structures was conducted. The effect of stitching reinforcement through thickness on using glass flux yarn on energy absorption of fiber-reinforced polymer (FRP) was investigated under high speed loading conditions at axial loading. Keeping the mass of the structure at 125 grams and applying different pattern of stitching at various locations in theory enables better energy absorption, and also enables the control over the behaviour of force-crush distance curve. The study consists of simple non-stitch absorber comparison with single and multi-location stitching behaviour and its effect on energy absorption capabilities. The locations of reinforcements are 10 mm, 20 mm, 30 mm, 10-20 mm, 10-30 mm, 20-30 mm, 10-20-30 mm and 10-15-20-25-30-35 mm from the top of the specimen. The effect of through the thickness reinforcements has shown increase in energy absorption capabilities and crushing load. The significance of this is that as the stitching locations are closer, the crushing load increases and consequently energy absorption capabilities are also increased. The implementation of this idea would improve the mean force by applying stitching and controlling the behaviour of force-crush distance curve.

Keywords: through-thickness stitching, 3D enforcement​, energy absorption, tubular composite structures

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Authors: Balthazar Temu, Zhao Yan, Bogdan-Petrin Ratiu, Sang Soon Oh, Qiang Li

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Nanowire based Photonic Crystal Surface Emitting Lasers (PCSELs) reported in the literature have been designed using a triangular, square or honeycomb patterns. The triangular and square pattern PCSELs have limited degrees of freedom in tuning the design parameters which hinders the ability to design high quality factor (Q-factor) devices. Nanowire based PCSELs designed using triangular and square patterns have been reported with the lasing thresholds of 130 kW/〖cm〗^2 and 7 kW/〖cm〗^2 respectively. On the other hand the honeycomb pattern gives more degrees of freedom in tuning the design parameters, which can allow one to design high Q-factor devices. A deformed honeycomb pattern device was reported with lasing threshold of 6.25 W/〖cm〗^2 corresponding to a simulated Q-factor of 5.84X〖10〗^5.Despite this achievement, the design principles which can lead to realization of even higher Q-factor honeycomb pattern PCSELs have not yet been investigated. In this work we show that through deforming the honeycomb pattern and tuning the heigh and lattice constants of the nanowires, it is possible to achieve even higher Q-factor devices. Considering three different band edge modes, we investigate how the resonance wavelength changes as the device is deformed, which is useful in designing high Q-factor devices in different wavelength bands. We eventually establish the design and simulation of honeycomb PCSELs operating around the wavelength of 960nm , in the O and the C band with Q-factors up to 7X〖10〗^7. We also investigate the Q-factors of undeformed device, and establish that the mode at the band edge close to 960nm can attain highest Q-factor of all the modes when the device is undeformed and the Q-factor degrades as the device is deformed. This work is a stepping stone towards the fabrication of very high Q-factor, nanowire based honey comb PCSELs, which are expected to have very low lasing threshold.

Keywords: designing nanowire PCSEL, designing PCSEL on silicon substrates, low threshold nanowire laser, simulation of photonic crystal lasers

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Authors: Danielle Shackley, Yetunde Folajimi

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As more people turn to the internet seeking health-related information, there is more risk of finding false, inaccurate, or dangerous information. Sentiment analysis is a natural language processing technique that assigns polarity scores to text, ranging from positive, neutral, and negative. In this research, we evaluate the weight of a sentiment analysis feature added to fake health news classification models. The dataset consists of existing reliably labeled health article headlines that were supplemented with health information collected about COVID-19 from social media sources. We started with data preprocessing and tested out various vectorization methods such as Count and TFIDF vectorization. We implemented 3 Naive Bayes classifier models, including Bernoulli, Multinomial, and Complement. To test the weight of the sentiment analysis feature on the dataset, we created benchmark Naive Bayes classification models without sentiment analysis, and those same models were reproduced, and the feature was added. We evaluated using the precision and accuracy scores. The Bernoulli initial model performed with 90% precision and 75.2% accuracy, while the model supplemented with sentiment labels performed with 90.4% precision and stayed constant at 75.2% accuracy. Our results show that the addition of sentiment analysis did not improve model precision by a wide margin; while there was no evidence of improvement in accuracy, we had a 1.9% improvement margin of the precision score with the Complement model. Future expansion of this work could include replicating the experiment process and substituting the Naive Bayes for a deep learning neural network model.

Keywords: sentiment analysis, Naive Bayes model, natural language processing, topic analysis, fake health news classification model

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Authors: Seyed Mohammad Ali Zahedi

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This field experiment was conducted in Isfahan in 2011 in order to study the effect of plant density and planting pattern on growth, yield and quality of silage corn (SC 704) using a randomized complete block design with split plot layout and four replications. The main plot consisted of three planting patterns (60 and 75 cm single planting row and 75 cm double planting row referred to as 60S, 75S and 75T, respectively). The subplots consisted of four levels of plant densities (65000, 80000, 95000 and 110000 plants per hectare). Each subplot consisted of 7 rows, each with 10m length. Vegetative and reproductive characteristics of plants at silking and hard dough stages (when the plants were harvested for silage) were evaluated. Results of variance analysis showed that the effects of planting pattern and plant density were significant on leaf area per plant, leaf area index (at silking), plant height, stem diameter, dry weights of leaf, stem and ear in silking and harvest stages and on fresh and dry yield, dry matter percentage and crude protein percentage at harvest. There was no planting pattern × plant density interaction for these parameters. As row space increased from 60 cm with single planting to 75 cm with single planting, leaf area index and plant height increased, but leaf area per plant, stem diameter, dry weight of leaf, stem and ear, dry matter percentage, dry matter yield and crude protein percentage decreased. Dry matter yield reduced from 24.9 to 18.5 t/ha and crude protein percentage decreased from 6.11 to 5.60 percent. When the plant density increased from 65000 to 110000 plant per hectare, leaf area index, plant height, dry weight of leaf, stem and ear and dry matter yield increased from 19.2 to 23.3 t/ha, whereas leaf area per plant, stem diameter, dry matter percentage and crude protein percentage decreased from 6.30 to 5.25. The best results were obtained with 60 cm row distance with single planting and 110000 plants per hectare.

Keywords: silage corn, plant density, planting pattern, yield

Procedia PDF Downloads 336

Authors: Sangeeta Dhamdhere, G. V. P. Rao

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Adolescent students need more energy, proteins, vitamins, and minerals because they grow to maturity in this age. Balanced diet plays important role in their wellbeing and health. The study conducted showed 48% students are not normal in their height and weight. 26% students found underweight, 18% overweight and 4% students found obese. The annual income group of underweight students was below 7 Lac and more than 90% students were staying at their home. The researcher has analysed the eating pattern of these students and concluded that there is need of awareness among the parents and students about balance diet and nutrition. The present research will help students improve their dietary habits and health, increase the number of attendees, and achieve academic excellence.

Keywords: balanced diet, nutrition, malnutrition, obesity, health education

Procedia PDF Downloads 67

Authors: Bo Liu, Chi-Man Pun

Abstract:

A novel technique for digital forgery detection by signal noise inconsistency is proposed in this paper. The forged area spliced from the other picture contains some features which may be inconsistent with the rest part of the image. Noise pattern and the level is a possible factor to reveal such inconsistency. To detect such noise discrepancies, the test picture is initially segmented into small pieces. The noise pattern and level of each segment are then estimated by using various filters. The noise features constructed in this step are utilized in energy-based graph cut to expose forged area in the final step. Experimental results show that our method provides a good illustration of regions with noise inconsistency in various scenarios.

Keywords: forgery detection, splicing forgery, noise estimation, noise

Procedia PDF Downloads 459