Search results for: feature extraction multispectral

Authors: Ahmed Abdullah Ahmed

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The task of recognizing the writer of a handwritten text has been an attractive research problem in the document analysis and recognition community with applications in handwriting forensics, paleography, document examination and handwriting recognition. This research presents an automatic method for writer recognition from digitized images of unconstrained writings. Although a great effort has been made by previous studies to come out with various methods, their performances, especially in terms of accuracy, are fallen short, and room for improvements is still wide open. The proposed technique employs optimal codebook based writer characterization where each writing sample is represented by a set of features computed from two codebooks, beginning and ending. Unlike most of the classical codebook based approaches which segment the writing into graphemes, this study is based on fragmenting a particular area of writing which are beginning and ending strokes. The proposed method starting with contour detection to extract significant information from the handwriting and the curve fragmentation is then employed to categorize the handwriting into Beginning and Ending zones into small fragments. The similar fragments of beginning strokes are grouped together to create Beginning cluster, and similarly, the ending strokes are grouped to create the ending cluster. These two clusters lead to the development of two codebooks (beginning and ending) by choosing the center of every similar fragments group. Writings under study are then represented by computing the probability of occurrence of codebook patterns. The probability distribution is used to characterize each writer. Two writings are then compared by computing distances between their respective probability distribution. The evaluations carried out on ICFHR standard dataset of 206 writers using Beginning and Ending codebooks separately. Finally, the Ending codebook achieved the highest identification rate of 98.23%, which is the best result so far on ICFHR dataset.

Keywords: off-line text-independent writer identification, feature extraction, codebook, fragments

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Authors: Regina Chua

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To reduce the costs of re-fielding a large survey, a Random Forest classifier was applied to measure the accuracy of classifying individuals into their assigned segments with the fewest possible questions. Given a long survey, one needed to determine the most predictive ten or fewer questions that would accurately assign new individuals to custom segments. Furthermore, the solution needed to be quick in its classification and usable in non-Python environments. In this paper, a supervised Random Forest classifier was modeled on a dataset with 7,000 individuals, 60 questions, and 254 features. The Random Forest consisted of an iterative collection of individual decision trees that result in a predicted segment with robust precision and recall scores compared to a single tree. A random 70-30 stratified sampling for training the algorithm was used, and accuracy trade-offs at different depths for each segment were identified. Ultimately, the Random Forest classifier performed at 87% accuracy at a depth of 10 with 20 instead of 254 features and 10 instead of 60 questions. With an acceptable accuracy in prioritizing feature selection, new tools were developed for non-Python environments: a worksheet with a formulaic version of the algorithm and an embedded function to predict the segment of an individual in real-time. Random Forest was determined to be an optimal classification model by its feature selection, performance, processing speed, and flexible application in other environments.

Keywords: machine learning, supervised learning, data science, random forest, classification, prediction, predictive modeling

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Authors: K. Asemave, D. O. Abakpa, T. T. Ligom

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The ability of bacteria to develop resistance to many antibiotics cannot be undermined, given the multifaceted health challenges in the present times. For this reason, a lot of attention is on botanicals and their products in search of new antibacterial agents. On the other hand, mango kernel oils (MKO) can be heavily valorized by taking advantage of the myriads bioactive phytochemicals it contains. Herein, we validated the use of MKO as bioactive agent against bacteria. The MKOs for the study were extracted by soxhlet means with ethanol and hexane for 4 h from 3 different mango kernels, namely; 'local' (sample A), 'julie' (sample B), and 'john' (sample C). Prior to the extraction, ground fine particles of the kernels were obtained from the seed kernels dried in oven at 100 °C for 8 h. Hexane gave higher yield of the oils than ethanol. It was also qualitatively confirmed that the mango kernel oils contain some phytochemicals such as phenol, quinone, saponin, and terpenoid. The results of the antibacterial activities of the MKO against both gram positive (Staphylococcus aureus) and gram negative (Pseudomonas aeruginosa) at different concentrations showed that the oils extracted with ethanol gave better antibacterial properties than those of the hexane. More so, the bioactivities were best with the local mango kernel oil. Indeed this work has completely validated the previous claim that MKOs are effective antibacterial agents. Thus, these oils (especially the ethanol-derived ones) can be used as bacteriostatic and antibacterial agents in say food, cosmetics, and allied industries.

Keywords: bacteria, mango, kernel, oil, phytochemicals

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Authors: Suzan M. Shahin, Mohammed A. Salem

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Essential oils are expensive phytochemicals produced and extracted from specific species belonging to particular families in the plant kingdom. In the United Arab Emirates country (UAE), which is located in the arid region of the world, nine species, from the Lamiaceae family, having the capability to produce therapeutic grade essential oils. These species include; Mentha spicata, Ocimum forskolei, Salvia macrosiphon, Salvia aegyptiaca, Salvia macilenta, Salvia spinosa, Teucrium polium, Teucrium stocksianum, and Zataria multiflora. Although, such potential species are indigenous to the UAE, however, there are almost no studies available to investigate the chemical composition and the quality of the extracted essential oils under the UAE climatological conditions. Therefore, great attention has to be given to such valuable natural resources, through conducting highly supported research projects, tailored to the UAE conditions, and investigating different extraction techniques, including the application of the latest available technologies, such as superficial fluid CO2. This is crucially needed; in order to accomplish the greatest possibilities in the medicinal field, specifically in the discovery of new therapeutic chemotypes, as well as, to achieve the sustainability of this natural resource in the country.

Keywords: essential oils, extraction techniques, Lamiaceae, traditional medicine, United Arab Emirates (UAE)

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Authors: Valarmathi Subramaniam

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Ever since the first Indian feature film was produced and released by Shri Dada Saheb Phalke in the year 1931, the Indian Film Industry has grown leaps and bounds. The Indian Film Industry stands as the largest film industry in the world, and it produces more than a thousand films every year with investments and revenues worth several billion rupees. As per the official report published by UNESCO in the year 2017 on their website, it states that in the year 2015, India has produced one thousand nine hundred and seven feature films using digital technology. Not only is the cinema adapted to digital technologies, but the digital technologies also opened up avenues for talents to enter the cinema industry. This paper explores such talents who have emerged in the film industry without any background, neither academic nor from their family background, but holding digital media as their weapon. The research involves two variants of filmmaking technology – Celluloid and Digital. The study used a selective sampling of films that were released from the year 2020-to 2022. The sample has been organized, resulting in popular and fresh talents in the editing phase of filmmaking. There were 48 editors, of which 12 editors were not popular and 6 of them were fresh into the film without any background. Interview methods were used to collect data on what helped them to get into the industry straight. The study found that the digital medium and the digital technology enabled them to get into the film industry.

Keywords: digital media, digital in cinema, digital era talents, emerging new talents

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Authors: Robert Jedrys, Zdzislaw Kobos, Justyna Skrzynska, Zbigniew Wochynski

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Professions connected to aviation require an assessment of the suitability of health, psychological and psychomotor skills and overall physical fitness of the organism, who applies. Assessment of the physical condition is conducted by the committees consisting of aero-medical specialists in clinical medicine and aviation. In addition, psychological predispositions should be evaluated by specialized psychologists familiar with the specifics of the tasks and requirements for the various positions in aviation. Both, physical abilities and general physical fitness of candidates for aviation shall be assessed during the selection exams, which also test the ability to deal with stress what is very important in aviation. Hence, the mentioned exams in physical education not only help to judge on the ranking in candidates in terms of their efficiency and performance, but also allows to evaluate the functioning under stress measured using psychological tests. Moreover, before-test stress is a predictors of successfulness in the next stages of education and practical training in the aviation. The aim of the study was to evaluate the influence of temperamental traits on strategies used for coping with stress during selection exams in physical education, deciding on admission to aviation school. The study involved 30 candidates for fighter pilot training in aviation school . To evaluate the temperament 'The Formal Characteristics of Behavior-Temperament Inventory' (FCB-TI) by B. Zawadzki and J.Strelau was used. To determine the pattern of coping with stress 'The Coping Inventory for Stressful Situations' (CISS) to N. S. Endler and J. D. A. Parker were engaged. Study of temperament and styles of coping with stress was conducted directly before the exam selection of physical education. The results were analyzed with 'Statistica 9' program. The studies showed that:-There is a negative correlation between such a temperament feature as 'perseverance' and preferred style of coping with stress concentrated on the task (r = -0.590; p < 0.004); -There is a positive correlation between such a feature of temperament as 'emotional reactivity,' and preference to deal with a stressful situation with ‘style centered on emotions’ (r = 0.520; p <0.011); -There is a negative correlation between such a feature of temperament as ‘strength’ and ‘style of coping with stress concentrated on emotions’ (r = -0.580; p < 0.004). Studies indicate that temperament traits determine the perception of stress and preferred coping styles used during the selection, as during the exams in physical education.

Keywords: aviation, physical education, stress, temperamental traits

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Authors: Shih-Wen Hsiao, Rong-Qi Chen, Chien-Yu Lin

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The topic of surface flattening plays a vital role in the field of computer aided design and manufacture. Surface flattening enables the production of 2D patterns and it can be used in design and manufacturing for developing a 3D surface to a 2D platform, especially in fashion design. This study describes surface flattening based on minimum energy methods according to the property of different fabrics. Firstly, through the geometric feature of a 3D surface, the less transformed area can be flattened on a 2D platform by geodesic. Then, strain energy that has accumulated in mesh can be stably released by an approximate implicit method and revised error function. In some cases, cutting mesh to further release the energy is a common way to fix the situation and enhance the accuracy of the surface flattening, and this makes the obtained 2D pattern naturally generate significant cracks. When this methodology is applied to a 3D mannequin constructed with feature lines, it enhances the level of computer-aided fashion design. Besides, when different fabrics are applied to fashion design, it is necessary to revise the shape of a 2D pattern according to the properties of the fabric. With this model, the outline of 2D patterns can be revised by distributing the strain energy with different results according to different fabric properties. Finally, this research uses some common design cases to illustrate and verify the feasibility of this methodology.

Keywords: surface flattening, strain energy, minimum energy, approximate implicit method, fashion design

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Authors: Shahrokh Barati, Reza Ramezani

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Due to the countries growing attention to the renewable energy producing, the demand for energy from renewable energy has gone up among the renewable energy sources; wind energy is the fastest growth in recent years. In this regard, in order to increase the availability of wind turbines, using of Fault Detection and Isolation (FDI) system is necessary. Wind turbines include of various faults such as sensors fault, actuator faults, network connection fault, mechanical faults and faults in the generator subsystem. Although, sensors and actuators have a large number of faults in wind turbine but have discussed fewer in the literature. Therefore, in this work, we focus our attention to design a sensor and actuator fault detection and isolation algorithm and Fault-tolerant control systems (FTCS) for Wind Turbine. The aim of this research is to propose a comprehensive fault detection and isolation system for sensors and actuators of wind turbine based on data-driven approaches. To achieve this goal, the features of measurable signals in real wind turbine extract in any condition. The next step is the feature selection among the extract in any condition. The next step is the feature selection among the extracted features. Features are selected that led to maximum separation networks that implemented in parallel and results of classifiers fused together. In order to maximize the reliability of decision on fault, the property of fault repeatability is used.

Keywords: FDI, wind turbines, sensors and actuators faults, renewable energy

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Authors: Anike Akinrinlade

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The world population is estimated to grow from 7.1 billion to 9.22 billion by 2075, increasing therefore by 23% from the current global population. Much of the demographic changes up to 2075 will take place in the less developed regions. There are currently 54 countries which fall under the bracket of being defined as having ‘low-middle income’ economies and need new ways to generate valuable products from current resources that is available. Artemisia annua L is well used for the extraction of the phytochemical artemisinin, which accounts for around 0.01 to 1.4 % dry weight of the plant. Artemisinin is used in the treatment of malaria, a disease rampart in sub-Saharan Africa and in many other countries. Once artemisinin has been extracted the spent leaf and waste streams are disposed of as waste. A feasibility study was carried out looking at increasing the biomass value of A. annua, by designing a biorefinery where spent leaf and waste streams are utilized for high product generation. Quercetin, ferulic acid, dihydroartemisinic acid, artemisinic acid and artemsinin were screened for in the waste stream samples and the spent leaf. The analytical results showed that artemisinin, artemisinic acid and dihydroartemisinic acid were present in the waste extracts as well as camphor and arteannuin b. Ongoing effects are looking at using more industrially relevant solvents to extract the phytochemicals from the waste fractions and investigate how microwave pyrolysis of spent leaf can be utilized to generate bio-products.

Keywords: high value product generation, bioinformatics, biomedicine, waste streams, spent leaf

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Authors: Marcela De Oliveira, Marina P. Da Silva, Fernando C. G. Da Rocha, Jorge M. Santos, Jaime S. Cardoso, Paulo N. Lisboa-Filho

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Multiple Sclerosis (MS) is an immune-mediated disease of the central nervous system characterized by neurodegeneration, inflammation, demyelination, and axonal loss. Magnetic resonance imaging (MRI), due to the richness in the information details provided, is the gold standard exam for diagnosis and follow-up of neurodegenerative diseases, such as MS. Brain atrophy, the gradual loss of brain volume, is quite extensive in multiple sclerosis, nearly 0.5-1.35% per year, far off the limits of normal aging. Thus, the brain volume quantification becomes an essential task for future analysis of the occurrence atrophy. The analysis of MRI has become a tedious and complex task for clinicians, who have to manually extract important information. This manual analysis is prone to errors and is time consuming due to various intra- and inter-operator variability. Nowadays, computerized methods for MRI segmentation have been extensively used to assist doctors in quantitative analyzes for disease diagnosis and monitoring. Thus, the purpose of this work was to evaluate the brain volume in MRI of MS patients. We used MRI scans with 30 slices of the five patients diagnosed with multiple sclerosis according to the McDonald criteria. The computational methods for the analysis of images were carried out in two steps: segmentation of the brain and brain volume quantification. The first image processing step was to perform brain extraction by skull stripping from the original image. In the skull stripper for MRI images of the brain, the algorithm registers a grayscale atlas image to the grayscale patient image. The associated brain mask is propagated using the registration transformation. Then this mask is eroded and used for a refined brain extraction based on level-sets (edge of the brain-skull border with dedicated expansion, curvature, and advection terms). In the second step, the brain volume quantification was performed by counting the voxels belonging to the segmentation mask and converted in cc. We observed an average brain volume of 1469.5 cc. We concluded that the automatic method applied in this work can be used for the brain extraction process and brain volume quantification in MRI. The development and use of computer programs can contribute to assist health professionals in the diagnosis and monitoring of patients with neurodegenerative diseases. In future works, we expect to implement more automated methods for the assessment of cerebral atrophy and brain lesions quantification, including machine-learning approaches. Acknowledgements: This work was supported by a grant from Brazilian agency Fundação de Amparo à Pesquisa do Estado de São Paulo (number 2019/16362-5).

Keywords: brain volume, magnetic resonance imaging, multiple sclerosis, skull stripper

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Authors: Kaushik Sathupadi, Sandesh Achar

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Pervasive computing is a growing research field that aims to acknowledge human physical life routines (HPLR) based on body-worn sensors such as MEMS sensors-based technologies. The use of these technologies for human activity recognition is progressively increasing. On the other hand, personalizing human life routines using numerous machine-learning techniques has always been an intriguing topic. In contrast, various methods have demonstrated the ability to recognize basic movement patterns. However, it still needs to be improved to anticipate the dynamics of human living patterns. This study introduces state-of-the-art techniques for recognizing static and dy-namic patterns and forecasting those challenging activities from multi-fused sensors. Further-more, numerous MEMS signals are extracted from one self-annotated IM-WSHA dataset and two benchmarked datasets. First, we acquired raw data is filtered with z-normalization and denoiser methods. Then, we adopted statistical, local binary pattern, auto-regressive model, and intrinsic time scale decomposition major features for feature extraction from different domains. Next, the acquired features are optimized using maximum relevance and minimum redundancy (mRMR). Finally, the artificial neural network is applied to analyze the whole system's performance. As a result, we attained a 90.27% recognition rate for the self-annotated dataset, while the HARTH and KU-HAR achieved 83% on nine living activities and 90.94% on 18 static and dynamic routines. Thus, the proposed HPLR system outperformed other state-of-the-art systems when evaluated with other methods in the literature.

Keywords: artificial intelligence, machine learning, gait analysis, local binary pattern (LBP), statistical features, micro-electro-mechanical systems (MEMS), maximum relevance and minimum re-dundancy (MRMR)

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Authors: Aleksandra Zysk, Pawel Badura

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Recognizing and controlling vocal registers during singing is a difficult task for beginner vocalist. It requires among others identifying which part of natural resonators is being used when a sound propagates through the body. Thus, an application has been designed allowing for sound recording, automatic vocal register recognition (VRR), and a graphical user interface providing real-time visualization of the signal and recognition results. Six spectral features are determined for each time frame and passed to the support vector machine classifier yielding a binary decision on the head or chest register assignment of the segment. The classification training and testing data have been recorded by ten professional female singers (soprano, aged 19-29) performing sounds for both chest and head register. The classification accuracy exceeded 93% in each of various validation schemes. Apart from a hard two-class clustering, the support vector classifier returns also information on the distance between particular feature vector and the discrimination hyperplane in a feature space. Such an information reflects the level of certainty of the vocal register classification in a fuzzy way. Thus, the designed recognition and training application is able to assess and visualize the continuous trend in singing in a user-friendly graphical mode providing an easy way to control the vocal emission.

Keywords: classification, singing, spectral analysis, vocal emission, vocal register

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Authors: Yash Bingi, Yiqiao Yin

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Reduction of child mortality is an ongoing struggle and a commonly used factor in determining progress in the medical field. The under-5 mortality number is around 5 million around the world, with many of the deaths being preventable. In light of this issue, Cardiotocograms (CTGs) have emerged as a leading tool to determine fetal health. By using ultrasound pulses and reading the responses, CTGs help healthcare professionals assess the overall health of the fetus to determine the risk of child mortality. However, interpreting the results of the CTGs is time-consuming and inefficient, especially in underdeveloped areas where an expert obstetrician is hard to come by. Using a support vector machine (SVM) and oversampling, this paper proposed a model that classifies fetal health with an accuracy of 99.59%. To further explain the CTG measurements, an algorithm based on Randomized Input Sampling for Explanation ((RISE) of Black-box Models was created, called Feature Alteration for explanation of Black Box Models (FAB), and compared the findings to Shapley Additive Explanations (SHAP) and Local Interpretable Model Agnostic Explanations (LIME). This allows doctors and medical professionals to classify fetal health with high accuracy and determine which features were most influential in the process.

Keywords: machine learning, fetal health, gradient boosting, support vector machine, Shapley values, local interpretable model agnostic explanations

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Authors: Zixi You

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There are a number of verbal prefixes in Old Japanese. However, the selection or the compatibility of verbs and verbal prefixes is among the least investigated topics on Old Japanese language. Unlike other types of prefixes, verbal prefixes in dictionaries are more often than not listed with very brief information such as ‘unknown meaning’ or ‘rhythmic function only’. To fill in a part of this knowledge gap, this paper presents an exhaustive investigation based on the newly developed ‘Oxford Corpus of Old Japanese’ (OCOJ), which included nearly all existing resource of Old Japanese language, with detailed linguistics information in TEI-XML tags. In this paper, we propose the possibility that the following three prefixes, i-, sa-, ta- (with ta- being considered as a variation of sa-), are relevant to split intransitivity in Old Japanese, with evidence that unergative verbs favor i- and that unergative verbs favor sa-(ta-). This might be undermined by the fact that transitives are also found to follow i-. However, with several manifestations of split intransitivity in Old Japanese discussed, the behavior of transitives in verbal prefix selection is no longer as surprising as it may seem to be when one look at the selection of verbal prefix in isolation. It is possible that there are one or more features that played essential roles in determining the selection of i-, and the attested transitive verbs happen to have these features. The data suggest that this feature is a sense of ‘change’ of location or state involved in the event donated by the verb, which is a feature of typical unaccusatives. This is further discussed in the ‘affectedness’ hierarchy. The presentation of this paper, which includes a brief demonstration of the OCOJ, is expected to be of the interest of both specialists and general audiences.

Keywords: old Japanese, split intransitivity, unaccusatives, unergatives, verbal prefix selection

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Authors: Hritwik Ghosh, Irfan Sadiq Rahat, Sachi Nandan Mohanty, J. V. R. Ravindra

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In the rapidly evolving landscape of medical diagnostics, the early detection and accurate classification of skin cancer remain paramount for effective treatment outcomes. This research delves into the transformative potential of Artificial Intelligence (AI), specifically Deep Learning (DL), as a tool for discerning and categorizing various skin conditions. Utilizing a diverse dataset of 3,000 images representing nine distinct skin conditions, we confront the inherent challenge of class imbalance. This imbalance, where conditions like melanomas are over-represented, is addressed by incorporating class weights during the model training phase, ensuring an equitable representation of all conditions in the learning process. Our pioneering approach introduces a hybrid model, amalgamating the strengths of two renowned Convolutional Neural Networks (CNNs), VGG16 and ResNet50. These networks, pre-trained on the ImageNet dataset, are adept at extracting intricate features from images. By synergizing these models, our research aims to capture a holistic set of features, thereby bolstering classification performance. Preliminary findings underscore the hybrid model's superiority over individual models, showcasing its prowess in feature extraction and classification. Moreover, the research emphasizes the significance of rigorous data pre-processing, including image resizing, color normalization, and segmentation, in ensuring data quality and model reliability. In essence, this study illuminates the promising role of AI and DL in revolutionizing skin cancer diagnostics, offering insights into its potential applications in broader medical domains.

Keywords: artificial intelligence, machine learning, deep learning, skin cancer, dermatology, convolutional neural networks, image classification, computer vision, healthcare technology, cancer detection, medical imaging

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Authors: Xiaoping Zhang, Fengying Yan

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

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

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Authors: Jayanth C., Anirudh Sai Yetikuri, Kavitha S. N.

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This paper explores the development and application of computer vision and machine learning techniques for real-time pose detection, facial recognition, and number plate extraction. Utilizing MediaPipe for pose estimation, the research presents methods for detecting hand raises and ducking postures through real-time video analysis. Complementarily, facial recognition is employed to compare and verify individual identities using the face recognition library. Additionally, the paper demonstrates a robust approach for extracting and storing vehicle number plates from images, integrating Optical Character Recognition (OCR) with a database management system. The study highlights the effectiveness and versatility of these technologies in practical scenarios, including security and surveillance applications. The findings underscore the potential of combining computer vision techniques to address diverse challenges and enhance automated systems for both individual and vehicular identification. This research contributes to the fields of computer vision and machine learning by providing scalable solutions and demonstrating their applicability in real-world contexts.

Keywords: computer vision, pose detection, facial recognition, number plate extraction, machine learning, real-time analysis, OCR, database management

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Authors: Chung-Ming Lo, Chung-Chien Lee

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In many countries, the lifetime prevalence of shoulder pain is up to 70%. In America, the health care system spends 7 billion per year about the healthy issues of shoulder pain. With respect to the origin, up to 70% of shoulder pain is attributed to rotator cuff lesions This study proposed a computer-aided diagnosis (CAD) system to assist radiologists classifying rotator cuff lesions with less operator dependence. Quantitative features were extracted from the shoulder ultrasound images acquired using an ALOKA alpha-6 US scanner (Hitachi-Aloka Medical, Tokyo, Japan) with linear array probe (scan width: 36mm) ranging from 5 to 13 MHz. During examination, the postures of the examined patients are standard sitting position and are followed by the regular routine. After acquisition, the shoulder US images were drawn out from the scanner and stored as 8-bit images with pixel value ranging from 0 to 255. Upon the sonographic appearance, the boundary of each lesion was delineated by a physician to indicate the specific pattern for analysis. The three lesion categories for classification were composed of 20 cases of tendon inflammation, 18 cases of calcific tendonitis, and 18 cases of supraspinatus tear. For each lesion, second-order statistics were quantified in the feature extraction. The second-order statistics were the texture features describing the correlations between adjacent pixels in a lesion. Because echogenicity patterns were expressed via grey-scale. The grey-scale co-occurrence matrixes with four angles of adjacent pixels were used. The texture metrics included the mean and standard deviation of energy, entropy, correlation, inverse different moment, inertia, cluster shade, cluster prominence, and Haralick correlation. Then, the quantitative features were combined in a multinomial logistic regression classifier to generate a prediction model of rotator cuff lesions. Multinomial logistic regression classifier is widely used in the classification of more than two categories such as the three lesion types used in this study. In the classifier, backward elimination was used to select a feature subset which is the most relevant. They were selected from the trained classifier with the lowest error rate. Leave-one-out cross-validation was used to evaluate the performance of the classifier. Each case was left out of the total cases and used to test the trained result by the remaining cases. According to the physician’s assessment, the performance of the proposed CAD system was shown by the accuracy. As a result, the proposed system achieved an accuracy of 86%. A CAD system based on the statistical texture features to interpret echogenicity values in shoulder musculoskeletal ultrasound was established to generate a prediction model for rotator cuff lesions. Clinically, it is difficult to distinguish some kinds of rotator cuff lesions, especially partial-thickness tear of rotator cuff. The shoulder orthopaedic surgeon and musculoskeletal radiologist reported greater diagnostic test accuracy than general radiologist or ultrasonographers based on the available literature. Consequently, the proposed CAD system which was developed according to the experiment of the shoulder orthopaedic surgeon can provide reliable suggestions to general radiologists or ultrasonographers. More quantitative features related to the specific patterns of different lesion types would be investigated in the further study to improve the prediction.

Keywords: shoulder ultrasound, rotator cuff lesions, texture, computer-aided diagnosis

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Authors: Kamalesh Kumar Singh

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Due to increasing trends of electronic waste, specially the ICT related gadgets, their green recycling is still a greater challenge. This article presents a two-stage, eco-friendly hydrometallurgical route for the recovery of gold from the delaminated metallic layers of waste mobile phone Printed Circuit Boards (PCBs). Initially, mobile phone PCBs are downsized (1x1 cm²) and treated with an organic solvent dimethylacetamide (DMA) for the separation of metallic fraction from non-metallic glass fiber. In the first stage, liberated metallic sheets are used for the selective dissolution of copper in an aqueous leaching reagent. Influence of various parameters such as type of leaching reagent, the concentration of the solution, temperature, time and pulp density are optimized for the effective leaching (almost 100%) of copper. Results have shown that 3M nitric acid is a suitable reagent for copper leaching at room temperature and considering chemical features, gold remained in solid residue. In the second stage, the separated residue is used for the recovery of gold by using sulphuric acid with a combination of halide salt. In this halide leaching, Cl₂ or Br₂ is generated as an in-situ oxidant to improve the leaching of gold. Results have shown that almost 92 % of gold is recovered at the optimized parameters.

Keywords: printed circuit boards, delamination, leaching, solvent extraction, recovery

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Authors: Belayihun Missaw

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Cotton cationization is an emerging area that solves the environmental problems associated with the reactive dyeing of cotton. In this study, keratin hydrolysate cationizing agent from chicken feather was extracted and optimized to eliminate the usage of salt during dyeing. Cationization of cotton using the extracted keratin hydrolysate and dyeing of the cationized cotton without salt was made. The effect of extraction parametric conditions like concentration of caustic soda, temperature and time were studied on the yield of protein from chicken feather and colour strength (K/S) values, and these process conditions were optimized. The optimum extraction conditions were. 25g/l caustic soda, at 500C temperature and 105 minutes with average yield = 91.2% and 4.32 colour strength value. The effect of salt addition, pH and concentration of cationizing agent on yield colour strength was also studied and optimized. It was observed that slightly acidic condition with 4% (% owf) concentration of cationizing agent gives a better dyeability as compared to normal cotton reactive dyeing. The physical properties of cationized-dyed fabric were assessed, and the result reveals that the cationization has a similar effect as normal dyeing of cotton. The cationization of cotton with keratin extract was found to be successful and economically viable.

Keywords: cotton materials, cationization, reactive dye, keratin hydrolysate

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Authors: Balamurali Gunji, B. B. V. L. Deepak, B. B. Biswal, Amrutha Rout, Golak Bihari Mohanta

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Robots play an important role in the operations like pick and place, assembly, spot welding and much more in manufacturing industries. Out of those, assembly is a very important process in manufacturing, where 20% of manufacturing cost is wholly occupied by the assembly process. To do the assembly task effectively, Assembly Sequences Planning (ASP) is required. ASP is one of the multi-objective non-deterministic optimization problems, achieving the optimal assembly sequence involves huge search space and highly complex in nature. Many researchers have followed different algorithms to solve ASP problem, which they have several limitations like the local optimal solution, huge search space, and execution time is more, complexity in applying the algorithm, etc. By keeping the above limitations in mind, in this paper, a new automated optimal robotic assembly sequence planning using Artificial Bee Colony (ABC) Algorithm is proposed. In this algorithm, automatic extraction of assembly predicates is done using Computer Aided Design (CAD) interface instead of extracting the assembly predicates manually. Due to this, the time of extraction of assembly predicates to obtain the feasible assembly sequence is reduced. The fitness evaluation of the obtained feasible sequence is carried out using ABC algorithm to generate the optimal assembly sequence. The proposed methodology is applied to different industrial products and compared the results with past literature.

Keywords: assembly sequence planning, CAD, artificial Bee colony algorithm, assembly predicates

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Authors: Zehai Yu, Hui Zhu, Linglong Lin, Huawei Liang, Biao Yu, Weixin Huang

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With the development of intelligent vehicle systems, a high-precision road map is increasingly needed in many aspects. The automatic lane lines extraction and modeling are the most essential steps for the generation of a precise lane-level road map. In this paper, an automatic lane-level road map generation system is proposed. To extract the road markings on the ground, the multi-region Otsu thresholding method is applied, which calculates the intensity value of laser data that maximizes the variance between background and road markings. The extracted road marking points are then projected to the raster image and clustered using a two-stage clustering algorithm. Lane lines are subsequently recognized from these clusters by the shape features of their minimum bounding rectangle. To ensure the storage efficiency of the map, the lane lines are approximated to cubic polynomial curves using a Bayesian estimation approach. The proposed lane-level road map generation system has been tested on urban and expressway conditions in Hefei, China. The experimental results on the datasets show that our method can achieve excellent extraction and clustering effect, and the fitted lines can reach a high position accuracy with an error of less than 10 cm.

Keywords: curve fitting, lane-level road map, line recognition, multi-thresholding, two-stage clustering

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Authors: Thi Thi Zin, Pyke Tin, Ikuo Kobayashi, Yoichiro Horii

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Today dairy farm experts and farmers have well recognized the importance of dairy cow Body Condition Score (BCS) since these scores can be used to optimize milk production, managing feeding system and as an indicator for abnormality in health even can be utilized to manage for having healthy calving times and process. In tradition, BCS measures are done by animal experts or trained technicians based on visual observations focusing on pin bones, pin, thurl and hook area, tail heads shapes, hook angles and short and long ribs. Since the traditional technique is very manual and subjective, the results can lead to different scores as well as not cost effective. Thus this paper proposes an algebraic geometric imaging approach for an automatic dairy cow BCS system. The proposed system consists of three functional modules. In the first module, significant landmarks or anatomical points from the cow image region are automatically extracted by using image processing techniques. To be specific, there are 23 anatomical points in the regions of ribs, hook bones, pin bone, thurl and tail head. These points are extracted by using block region based vertical and horizontal histogram methods. According to animal experts, the body condition scores depend mainly on the shape structure these regions. Therefore the second module will investigate some algebraic and geometric properties of the extracted anatomical points. Specifically, the second order polynomial regression is employed to a subset of anatomical points to produce the regression coefficients which are to be utilized as a part of feature vector in scoring process. In addition, the angles at thurl, pin, tail head and hook bone area are computed to extend the feature vector. Finally, in the third module, the extracted feature vectors are trained by using Markov Classification process to assign BCS for individual cows. Then the assigned BCS are revised by using multiple regression method to produce the final BCS score for dairy cows. In order to confirm the validity of proposed method, a monitoring video camera is set up at the milk rotary parlor to take top view images of cows. The proposed method extracts the key anatomical points and the corresponding feature vectors for each individual cows. Then the multiple regression calculator and Markov Chain Classification process are utilized to produce the estimated body condition score for each cow. The experimental results tested on 100 dairy cows from self-collected dataset and public bench mark dataset show very promising with accuracy of 98%.

Keywords: algebraic geometric imaging approach, body condition score, Markov classification, polynomial regression

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Authors: Luis Alvarado, Victor Poblete, Isaac Gonzalez, Yetzabeth Gonzalez

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In MIREX 2016 (http://www.music-ir.org/mirex), the deep neural network (DNN)-Deep Chroma Extractor, proposed by Korzeniowski and Wiedmer, reached the highest score in an audio chord recognition task. In the present paper, this tool is assessed under acoustic reverberant environments and distinct source-microphone distances. The evaluation dataset comprises The Beatles and Queen datasets. These datasets are sequentially re-recorded with a single microphone in a real reverberant chamber at four reverberation times (0 -anechoic-, 1, 2, and 3 s, approximately), as well as four source-microphone distances (32, 64, 128, and 256 cm). It is expected that the performance of the trained DNN will dramatically decrease under these acoustic conditions with signals degraded by room reverberation and distance to the source. Recently, the effect of the bio-inspired Locally-Normalized Cepstral Coefficients (LNCC), has been assessed in a text independent speaker verification task using speech signals degraded by additive noise at different signal-to-noise ratios with variations of recording distance, and it has also been assessed under reverberant conditions with variations of recording distance. LNCC showed a performance so high as the state-of-the-art Mel Frequency Cepstral Coefficient filters. Based on these results, this paper proposes a variation of locally-normalized triangular filters called Locally-Normalized Quarter Tone (LNQT) filters. By using the LNQT spectrogram, robustness improvements of the trained Deep Chroma Extractor are expected, compared with classical triangular filters, and thus compensating the music signal degradation improving the accuracy of the chord recognition system.

Keywords: chord recognition, deep neural networks, feature extraction, music information retrieval

Procedia PDF Downloads 232

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

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

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

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Authors: Zerroug Abdelhamid, Danielle Chassoux

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Various models are given to simulate homogeneous or heterogeneous cancerous tumors and extract in each case the boundary. The fractal dimension is then estimated by least squares method and compared to some previous methods.

Keywords: simulation, cancerous tumor, Markov fields, fractal dimension, extraction, recovering

Procedia PDF Downloads 365

Authors: Diana Ruth Caga-Anan

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Classroom management is a critical skill but the styles are constantly evolving. It is constantly under pressure particularly in the college education level due to diversity in student profiles, modes of delivery, and marketization of higher education. This study sought to analyze the extent of implementation of classroom management practices (CMPs) of the college instructors of the Hotel, Restaurant, and Institution Management of a premier university in the Philippines. It was also determined if their length of teaching affects their classroom management style. A questionnaire with sixteen 'evidenced-based' CMPs grouped into five critical features of classroom management, adopted from the literature search of Simonsen et al. (2008), was administered to 4 instructor-respondents and to their 88 students. Weighted mean scores of each of the CMPs revealed that there were differences between the instructors’ self-scores and their students’ ratings on their implementation of CMPs. The critical feature of classroom management 'actively engage students in observable ways' got the highest mean score, corresponding to 'always' from the instructors’ self-rating and 'frequently' from their students’ ratings. However, 'use a continuum of strategies to respond to inappropriate behaviors' got the lowest scores from both the instructors and their students corresponding only to 'occasionally'. Analysis of variance showed that the only CMP affected by the length of teaching is the practice of 'prompting students to respond'. Based on the findings, some recommendations for the instructors to improve on the critical feature where they scored low are discussed and suggestions are included for future research.

Keywords: classroom management, CMPs, critical features, evidence-based classroom management practices

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Authors: Tengku Muhammad Rafi Nazmi Bin Tengku Razali, Habsah Alwi

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This article based on effect of temperature towards extraction of Aquilaria Subintegra. Aquilaria Subintegra which its main habitat is in Asia-tropical and particularly often found in its native which is Thailand. There is claim which is Aquilaria Subintegra contains antipyretic properties that helps fight fever. Research nowadays also shown that paracetamol consumed bring bad effect towards consumers. This sample will first dry using Vacuum Far Infrared which provides better drying than conventional oven. Soxhlet extractor used to extract oil from sample. Gas Chromatography Mass Spectrometer used to analyze sample to determine its compound. Objective from this research was to determine the active ingredients that exist in the Aquilaria Subintegra leaves and to determine whether compound of Acetaminophen exist or not inside the leaves. Moisture content from 400C was 80%, 500C was 620% and 600C was 36%. The greater temperature resulting lower moisture content inside sample leaves. 7 components were identified in sample T=400C while only 5 components were identified in sample at T=50C and T=60C. Four components were commonly identified in three sample which is 1n-Hexadecanoic acid, 9,12,15-Octadecatrienoic acid, methyl ester (z,z,z), Vitamin E and Squalene. Further studies are needed with new series of temperature to refine the best results.

Keywords: aquilaria subintegra, vacuum far infrared, SOXHLET extractor, gas chromatography mass spectrometer, paracetamol

Procedia PDF Downloads 484

Authors: Jun Wang, Chenglei Diao, Emanuele Pagone, Jialuo Ding, Stewart Williams

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As a crucial component in aircraft, landing gear systems are responsible for supporting the plane during parking, taxiing, takeoff, and landing. Given the need for high load-bearing capacity over extended periods, 300M ultra-high strength steel (UHSS) is often the material of choice for crafting these systems due to its exceptional strength, toughness, and fatigue resistance. In the quest for cost-effective and sustainable manufacturing solutions, Wire Arc Additive Manufacturing (WAAM) emerges as a promising alternative for fabricating 300M UHSS landing gears. This is due to its advantages in near-net-shape forming of large components, cost-efficiency, and reduced lead times. Cranfield University has conducted an extensive preliminary study on WAAM 300M UHSS, covering feature deposition, interface analysis, and post-heat treatment. Both Gas Metal Arc (GMA) and Plasma Transferred Arc (PTA)-based WAAM methods were explored, revealing their feasibility for defect-free manufacturing. However, as-deposited 300M features showed lower strength but higher ductility compared to their forged counterparts. Subsequent post-heat treatments were effective in normalising the microstructure and mechanical properties, meeting qualification standards. A 300M UHSS landing gear demonstrator was successfully created using PTA-based WAAM, showcasing the method's precision and cost-effectiveness. The demonstrator, measuring Ф200mm x 700mm, was completed in 16 hours, using 7 kg of material at a deposition rate of 1.3kg/hr. This resulted in a significant reduction in the Buy-to-Fly (BTF) ratio compared to traditional manufacturing methods, further validating WAAM's potential for this application. A "cradle-to-gate" environmental impact assessment, which considers the cumulative effects from raw material extraction to customer shipment, has revealed promising outcomes. Utilising Wire Arc Additive Manufacturing (WAAM) for landing gear components significantly reduces the need for raw material extraction and refinement compared to traditional subtractive methods. This, in turn, lessens the burden on subsequent manufacturing processes, including heat treatment, machining, and transportation. Our estimates indicate that the carbon footprint of the component could be halved when switching from traditional machining to WAAM. Similar reductions are observed in embodied energy consumption and other environmental impact indicators, such as emissions to air, water, and land. Additionally, WAAM offers the unique advantage of part repair by redepositing only the necessary material, a capability not available through conventional methods. Our research shows that WAAM-based repairs can drastically reduce environmental impact, even when accounting for additional transportation for repairs. Consequently, WAAM emerges as a pivotal technology for reducing environmental impact in manufacturing, aiding the industry in its crucial and ambitious journey towards Net Zero. This study paves the way for transformative benefits across the aerospace industry, as we integrate manufacturing into a hybrid solution that offers substantial savings and access to more sustainable technologies for critical component production.

Keywords: WAAM, aircraft landing gear, microstructure, mechanical performance, life cycle assessment

Procedia PDF Downloads 159

Authors: Krishna Mohan Bathula, Fatou Bintou Loucoubar, FNU Kaleemunnisa, Christelle Scharff, Mark Anthony De Castro

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Voice recognition algorithms such as automatic speech recognition and text-to-speech systems with African languages can play an important role in bridging the digital divide of Artificial Intelligence in Africa, contributing to the establishment of a fully inclusive information society. This paper proposes a Deep Learning model that can classify the user responses as inputs for an interactive voice response system. A dataset with Wolof language words ‘yes’ and ‘no’ is collected as audio recordings. A two stage Data Augmentation approach is adopted for enhancing the dataset size required by the deep neural network. Data preprocessing and feature engineering with Mel-Frequency Cepstral Coefficients are implemented. Convolutional Neural Networks (CNNs) have proven to be very powerful in image classification and are promising for audio processing when sounds are transformed into spectra. For performing voice response classification, the recordings are transformed into sound frequency feature spectra and then applied image classification methodology using a deep CNN model. The inference model of this trained and reusable Wolof voice response recognition system can be integrated with many applications associated with both web and mobile platforms.

Keywords: automatic speech recognition, interactive voice response, voice response recognition, wolof word classification

Procedia PDF Downloads 116