Search results for: feature noise

Authors: Aishwarya Ravindra Fursule, Shruti Kshirsagar

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In the intersection of artificial intelligence and human-centered computing, this paper delves into speech emotion recognition (SER). It presents a comparative analysis of machine learning models such as K-Nearest Neighbors (KNN),logistic regression, support vector machines (SVM), decision trees, ensemble classifiers, and random forests, applied to SER. The research employs four datasets: Crema D, SAVEE, TESS, and RAVDESS. It focuses on extracting salient audio signal features like Zero Crossing Rate (ZCR), Chroma_stft, Mel Frequency Cepstral Coefficients (MFCC), root mean square (RMS) value, and MelSpectogram. These features are used to train and evaluate the models’ ability to recognize eight types of emotions from speech: happy, sad, neutral, angry, calm, disgust, fear, and surprise. Among the models, the Random Forest algorithm demonstrated superior performance, achieving approximately 79% accuracy. This suggests its suitability for SER within the parameters of this study. The research contributes to SER by showcasing the effectiveness of various machine learning algorithms and feature extraction techniques. The findings hold promise for the development of more precise emotion recognition systems in the future. This abstract provides a succinct overview of the paper’s content, methods, and results.

Keywords: comparison, ML classifiers, KNN, decision tree, SVM, random forest, logistic regression, ensemble classifiers

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Authors: Guorong Sui, Xingwei Jia, Fei Tong, Xiumin Gao

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Camera calibration is a fundamental issue of high precision noncontact measurement. And it is necessary to analyze and study the reliability and application range of its linear model which is often used in the camera calibration. According to the imaging features of monocular cameras, a camera model which is based on the image pixel coordinates and three dimensional space coordinates is built. Using our own customized template, the image pixel coordinate is obtained by the subpixel corner detection method. Without considering the aberration of the optical system, the feature extraction and linearity analysis of the line segment in the template are performed. Moreover, the experiment is repeated 11 times by constantly varying the measuring distance. At last, the linearity of the camera is achieved by fitting 11 groups of data. The camera model measurement results show that the relative error does not exceed 1%, and the repeated measurement error is not more than 0.1 mm magnitude. Meanwhile, it is found that the model has some measurement differences in the different region and object distance. The experiment results show this linear model is simple and practical, and have good linearity within a certain object distance. These experiment results provide a powerful basis for establishment of the linear model of camera. These works will have potential value to the actual engineering measurement.

Keywords: camera linear model, geometric imaging relationship, image pixel coordinates, three dimensional space coordinates, sub-pixel corner detection

Procedia PDF Downloads 277

Authors: Huajuan Shi

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Background: The biopsy of the preimplantation embryo may increase the potential risk and concern of embryo viability. Clinically discarded spent embryo medium (SEM) has entered the view of researchers, sparking an interest in noninvasive embryo screening. However, one of the major restrictions is the extremelty low quantity of cf-RNA, which is difficult to efficiently and unbiased amplify cf-RNA using traditional methods. Hence, there is urgently need to an efficient and low bias amplification method which can comprehensively and accurately obtain cf-RNA information to truly reveal the state of SEM cf-RNA. Result: In this present study, we established an agarose PCR amplification system, and has significantly improved the amplification sensitivity and efficiency by ~90 fold and 9.29 %, respectively. We applied agarose to sequencing library preparation (named AG-seq) to quantify and characterize cf-RNA in SEM. The number of detected cf-RNAs (3533 vs 598) and coverage of 3' end were significantly increased, and the noise of low abundance gene detection was reduced. The increasing percentage 5' end adenine and alternative splicing (AS) events of short fragments (< 400 bp) were discovered by AG-seq. Further, the profiles and characterizations of cf-RNA in spent cleavage medium (SCM) and spent blastocyst medium (SBM) indicated that 4‐mer end motifs of cf-RNA fragments could remarkably differentiate different embryo development stages. Significance: This study established an efficient and low-cost SEM amplification and library preparation method. Not only that, we successfully described the characterizations of SEM cf-RNA of preimplantation embryo by using AG-seq, including abundance features fragment lengths. AG-seq facilitates the study of cf-RNA as a noninvasive embryo screening biomarker and opens up potential clinical utilities of trace samples.

Keywords: cell-free RNA, agarose, spent embryo medium, RNA sequencing, non-invasive detection

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Authors: Jia Liu

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This article explores the shifting nature of physician-patient relationship in mainland China. Specifically, it takes the physician-patient relationship during the barefoot doctor program in 1968-1978, the marketization of healthcare services in 1978-2002, and the healthcare reform in 2003-2020 as three typical historical periods, illustrating how the nature of the physician-patient relationship has changed over time in mainland China. Drawing on recent jurisprudential literature that emphasizes the roles and functions done by and through obligation rather than right, it explores how the obligations of physicians and patients along with the implementation of informed consent, marketization of the healthcare system, and the basic healthcare reform have affected their relationship. One key feature of this article is that it analyzes the ways in which commodification and decommodification of healthcare have defined and in many different ways have determined the expectations and practices of physicians and patients, which illustrates how the trust between physicians and patients threatens to collapse and the bond between the citizen and the state fails to be firmly established in the mainland Chinese healthcare context. It also pays special attention to the role played by law and legal institutions—for instance, the implementation of informed consent and the liability law—in being complicit in facilitating the decoupling of the practices of physicians and patients from their ethical senses of obligation and undermining the bond (the trust relationship) between them.

Keywords: healthcare, marketization, physician-patient relationship, sense of obligation

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Authors: Amr AbdelAzim Ali, G. A. Elsheikh, Moutaz M. Hegazy

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Reaction wheels (RWs) are generally used as main actuator in the attitude control system (ACS) of spacecraft (SC) for fast orientation and high pointing accuracy. In order to achieve the required accuracy for the RWs model, the main characteristics of the RWs that necessitate analysis during the ACS design phase include: technical features, sequence of operating and RW control logic are included in function (behavior) model. A mathematical model is developed including the various errors source. The errors in control torque including relative, absolute, and error due to time delay. While the errors in angular velocity due to differences between average and real speed, resolution error, loose in installation of angular sensor, and synchronization errors. The friction torque is presented in the model include the different feature of friction phenomena: steady velocity friction, static friction and break-away torque, and frictional lag. The model response is compared with the experimental torque and frequency-response characteristics of tested RWs. Based on the created RW model, some criteria of optimization based control torque allocation problem can be recommended like: avoiding the zero speed crossing, bias angular velocity, or preventing wheel from running on the same angular velocity.

Keywords: friction torque, reaction wheels modeling, software in the loop, spacecraft attitude control

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Authors: Mohamed Hamed

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Population studies, essentially deals with the size, growth, and distribution of the population in a given area. Size, growth, and distribution are determined by three major factors, which are reproduction, mortality, and migration. Of these factors, reproduction is a potent socio-demographic force in vital process of population growth. It is a major component of population growth, and has crucial role in population dynamic, because it measures the rate at which a population increased. In fact the most striking feature of human reproduction is its variation. Its levels are vary widely among nations, countries, geographic regions, ethnic. The variations of reproductive behaviour among married women have been empirically documented in a large numbers of countries. For instance, many researchers in developing and developed countries investigated the differential of reproductive behaviour among married women. Most of these studies found that reproductive behaviour is strongly influenced by the socioeconomic and biological factors.Such as education, income, employment of women, marriage pattern, age at marriage, contraceptive use, education, and employment. However, the above socioeconomic and biological factors are determined by cultural factors surrounded by married women. So, this study is going to find out the effect of culture on reproductive behaviour among married women in Sudan, a case of Shendi town.

Keywords: fertilty pattern, sudan, shendi town, factors affecting reproductive behaviour, married women

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Authors: Agustinus Rudatyo Himamunanto, Anastasia Rita Widiarti

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Transliteration using Javanese manuscripts is one of methods to preserve and legate the wealth of literature in the past for the present generation in Indonesia. The transliteration manual process commonly requires philologists and takes a relatively long time. The automatic transliteration process is expected to shorten the time so as to help the works of philologists. The preprocessing and segmentation stage firstly done is used to manage the document images, thus obtaining image script units that will compile input document images free from noise and have the similarity in properties in the thickness, size, and slope. The next stage of characteristic extraction is used to find unique characteristics that will distinguish each Javanese script image. One of characteristics that is used in this research is the number of black pixels in each image units. Each image of Java scripts contained in the data training will undergo the same process similar to the input characters. The system testing was performed with the data of the book Hamong Tani. The book Hamong Tani was selected due to its content, age and number of pages. Those were considered sufficient as a model experimental input. Based on the results of random page automatic transliteration process testing, it was determined that the maximum percentage correctness obtained was 81.53%. The percentage of success was obtained in 32x32 pixel input image size with the 5x5 image window. With regard to the results, it can be concluded that the automatic transliteration model offered is relatively good.

Keywords: Javanese script, character recognition, statistical, automatic transliteration

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Authors: Mariana Sary Khalifa Rezk

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The problem of respect for human rights in Southeast Asia has emerged as a main situation and is attracting the attention of the international network. Basically, the affiliation of Southeast Asian Nations (ASEAN) made human rights certainly one of its main troubles inside the ASEAN constitution in 2008. In the end, the Intergovernmental Fee on Human Rights ASEAN Human Rights (AICHR) was set up. AICHR is the Southeast Asia Human Rights Enforcement fee charged with the duties, functions and powers to sell and defend human rights. However, at the cease of 2016, the protecting feature assigned to the AICHR was no longer fulfilled. That is shown via several instances of human rights violations, which can be nonetheless ongoing and have not been solved. One case that has these days come to light is human rights violations against the Rohingya people in Myanmar. Using a felony-normative method, the study examines the urgency of setting up a human rights tribunal in Southeast Asia able to decide binding on ASEAN members or responsible parties. Information indicates ASEAN desires regional courts to cope with human rights abuses in the ASEAN region. Furthermore, the look also highlights 3 critical elements that ASEAN ought to take into account whilst establishing a human rights tribunal, particularly quantity. A good sized distinction in phrases of democracy and human rights improvement a few of the participants, a consistent implementation of the principle of non-interference and the economic trouble of the continuation of the court docket.

Keywords: politics, human rights, humanities, mankind, law human rights, Nigerian legal provisions, shariah law, comparative study, charter

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Authors: Benga Ebouele, Thomas Tengen

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Implementing either periodic or continuous inventory review model within most manufacturing-companies-supply chains as a management tool may incur higher costs. These high costs affect the system flexibility which in turn affects the level of service required to satisfy customers. However, these effects are not clearly understood because the parameters of both inventory review policies (protection demand interval, order quantity, etc.) are not designed to be fully utilized under different and uncertain conditions such as poor manufacturing, supplies and delivery performance. Coming up with a hybrid model which may combine in some sense the feature of both continuous and a periodic inventory review models should be useful. Therefore, there is a need to build and evaluate such hybrid model on the annual total cost, stock out probability and system’s flexibility in order to search for the most cost effective inventory review model. This work also seeks to find the optimal sets of parameters of inventory management under stochastic condition so as to optimise each policy independently. The results reveal that a continuous inventory system always incurs lesser cost than a periodic (R, S) inventory system, but this difference tends to decrease as time goes by. Although the hybrid inventory is the only one that can yield lesser cost over time, it is not always desirable but also natural to use it in order to help the system to meet high performance specification.

Keywords: demand and lead time randomness, hybrid Inventory model, optimization, supply chain

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Authors: Efil Yusrianto, Noraini Marsi, Noraniah Kassim, Izzati Abdul Manaf, Hafizuddin Hakim Shariff

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Today, municipal solid waste (MSW), noise pollution, and attack fire are three ongoing issues for inhabitants of urban including in Malaysia. To solve these issues, eco-friendly autoclaved aerated concrete (AAC) containing recycled ceramic and gypsum waste (CGW) as a partial replacement for sand with different ratios (0%, 5%, 10%, 15%, 20%, and 25% wt) has been prepared. The performance of samples, such as the physical, mechanical, sound absorption coefficient, and direct fire resistance, has been investigated. All samples showed normal color behavior, i.e., grey and free crack. The compressive strength was increased in the range of 6.10% to 29.88%. The maximum value of compressive strength was 2.13MPa for 15% wt of CGW. The positive effect of CGW on the compressive strength of AAC has also been confirmed by crystalline phase and microstructure analysis. The acoustic performances, such as sound absorption coefficients of samples at low frequencies (500Hz), are higher than the reference sample (RS). AAC-CGW samples are categorized as AAC material classes B and C. The fire resistance results showed the physical surface of the samples had a free crack and was not burned during the direct fire at 950ºC for 300s. The results showed that CGW succeeded in enhancing the performance of fresh AAC, such as compressive strength, crystalline phase, sound absorption coefficient, and fire resistance of samples.

Keywords: physical, mechanical, acoustic, direct fire resistance performance, autoclaved aerated concrete, recycled ceramic-gypsum waste

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Authors: Xinyuan Y. Song, Kai Kang

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Joint models for analyzing longitudinal and survival data are widely used to investigate the relationship between a failure time process and time-variant predictors. A common assumption in conventional joint models in the survival analysis literature is that all predictors are observable. However, this assumption may not always be supported because unobservable traits, namely, latent variables, which are indirectly observable and should be measured through multiple observed variables, are commonly encountered in the medical, behavioral, and financial research settings. In this study, a joint modeling approach to deal with this feature is proposed. The proposed model comprises three parts. The first part is a dynamic factor analysis model for characterizing latent variables through multiple observed indicators over time. The second part is a random coefficient trajectory model for describing the individual trajectories of latent variables. The third part is a proportional hazard model for examining the effects of time-invariant predictors and the longitudinal trajectories of time-variant latent risk factors on hazards of interest. A Bayesian approach coupled with a Markov chain Monte Carlo algorithm to perform statistical inference. An application of the proposed joint model to a study on the Alzheimer's disease neuroimaging Initiative is presented.

Keywords: Bayesian analysis, joint model, longitudinal data, time-to-event data

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

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

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

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Authors: Navdeep Goel, Salvador Gabarda

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Digital images are widely used in computer applications. To store or transmit the uncompressed images requires considerable storage capacity and transmission bandwidth. Image compression is a means to perform transmission or storage of visual data in the most economical way. This paper explains about how images can be encoded to be transmitted in a multiplexing time-frequency domain channel. Multiplexing involves packing signals together whose representations are compact in the working domain. In order to optimize transmission resources each 4x4 pixel block of the image is transformed by a suitable polynomial approximation, into a minimal number of coefficients. Less than 4*4 coefficients in one block spares a significant amount of transmitted information, but some information is lost. Different approximations for image transformation have been evaluated as polynomial representation (Vandermonde matrix), least squares + gradient descent, 1-D Chebyshev polynomials, 2-D Chebyshev polynomials or singular value decomposition (SVD). Results have been compared in terms of nominal compression rate (NCR), compression ratio (CR) and peak signal-to-noise ratio (PSNR) in order to minimize the error function defined as the difference between the original pixel gray levels and the approximated polynomial output. Polynomial coefficients have been later encoded and handled for generating chirps in a target rate of about two chirps per 4*4 pixel block and then submitted to a transmission multiplexing operation in the time-frequency domain.

Keywords: chirp signals, image multiplexing, image transformation, linear canonical transform, polynomial approximation

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Authors: Anthony Proschka, Deepak Mishra, Merlyn Ramanan, Zurab Baratashvili

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Small and medium-sized businesses receive over 160 billion invoices every year. Since these documents exhibit many subtle differences in layout and text, extracting structured fields such as sender name, amount, and VAT rate from them automatically is an open research question. In this paper, existing work in template-based document extraction is extended, and a system is devised that is able to reliably extract all required fields for up to 70% of all documents in the data set, more than any other previously reported method. The approaches are described for 1) detecting through visual features which template a given document belongs to, 2) automatically generating extraction rules for a given new template by composing regular expressions from multiple components, and 3) computing confidence scores that indicate the accuracy of the automatic extractions. The system can generate templates with as little as one training sample and only requires the ground truth field values instead of detailed annotations such as bounding boxes that are hard to obtain. The system is deployed and used inside a commercial accounting software.

Keywords: data mining, information retrieval, business, feature extraction, layout, business data processing, document handling, end-user trained information extraction, document archiving, scanned business documents, automated document processing, F1-measure, commercial accounting software

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Authors: Piyush Bajpai, Sneha Pandey

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Mumbai’s Dhobighat, 150 year old largest open laundry in the world, is the true live-work place and only source of income for some of Mumbai’s highest density ‘urban poor’ residents. The regeneration of Dhobighat, due to its ultra prime location and complex socio-political culture has been a complex issue. This once flourishing urban industrial core has been degrading for the past several decades mainly due to the decline of the open laundry business, the site’s over burdened infrastructure and conflicting socio-political and economic forces. The phenomena of ‘exaptation’ or ‘co-option’ has been observed by evolutionary biologists as a process responsible for producing highly tenacious and resilient offsprings within a species. The reddish egret uses its wings to cast shadow in shallow waters to attract small fish and hunt them. An unrelated feature used opportunistically to produce a very favorable result. How can this idea of co-option be applied to resolve the complex issue of Dhobighat’s regeneration? Our paper proposes a new methodology/approach for the regeneration of Dhobighat through the lens of evolutionary biology. Forces and systems (social, political, economic, cultural and ecological) that seem conflicting or unrelated by nature are opportunistically transformed into symbiotic and complimentary relationships that produce an inclusive, resilient and holistic solution for the regeneration of Dhobighat.

Keywords: urban regeneration, exaptation, resilience, Dhobighat, Mumbai

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Authors: Khadra Ahmed, Khaled Kheireldin

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In this paper, we present a pedestrian detection descriptor called Fused Structure and Texture (FST) features based on the combination of the local phase information with the texture features. Since the phase of the signal conveys more structural information than the magnitude, the phase congruency concept is used to capture the structural features. On the other hand, the Center-Symmetric Local Binary Pattern (CSLBP) approach is used to capture the texture information of the image. The dimension less quantity of the phase congruency and the robustness of the CSLBP operator on the flat images, as well as the blur and illumination changes, lead the proposed descriptor to be more robust and less sensitive to the light variations. The proposed descriptor can be formed by extracting the phase congruency and the CSLBP values of each pixel of the image with respect to its neighborhood. The histogram of the oriented phase and the histogram of the CSLBP values for the local regions in the image are computed and concatenated to construct the FST descriptor. Several experiments were conducted on INRIA and the low resolution DaimlerChrysler datasets to evaluate the detection performance of the pedestrian detection system that is based on the FST descriptor. A linear Support Vector Machine (SVM) is used to train the pedestrian classifier. These experiments showed that the proposed FST descriptor has better detection performance over a set of state of the art feature extraction methodologies.

Keywords: planktons, diversity indices, water quality index, water ways

Procedia PDF Downloads 518

Authors: Areej Salaymeh, Loren Schwiebert, Stephen Remias, Jonathan Waddell

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Extracting information from new data sources has emerged as a crucial task in many traffic planning processes, such as identifying traffic patterns, route planning, traffic forecasting, and locating infrastructure improvements. Given the advanced technologies used to collect Global Positioning System (GPS) data from dedicated GPS devices, GPS equipped phones, and navigation tools, intelligent data analysis methodologies are necessary to mine this raw data. In this research, an automatic detection framework is proposed to help identify and classify the locations of stopped GPS waypoints into two main categories: signalized intersections or highway congestion. The Delaunay triangulation is used to perform this assessment in the clustering phase. While most of the existing clustering algorithms need assumptions about the data distribution, the effectiveness of the Delaunay triangulation relies on triangulating geographical data points without such assumptions. Our proposed method starts by cleaning noise from the data and normalizing it. Next, the framework will identify stoppage points by calculating the traveled distance. The last step is to use clustering to form groups of waypoints for signalized traffic and highway congestion. Next, a binary classifier was applied to find distinguish highway congestion from signalized stop points. The binary classifier uses the length of the cluster to find congestion. The proposed framework shows high accuracy for identifying the stop positions and congestion points in around 99.2% of trials. We show that it is possible, using limited GPS data, to distinguish with high accuracy.

Keywords: Delaunay triangulation, clustering, intelligent transportation systems, GPS data

Procedia PDF Downloads 275

Authors: Chia-Min Lai, Wan-Ching Lin, Hahn-Ming Lee, Ching-Hao Mao

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As world wild internet has non-stop developments, making profit by lending registered domain names emerges as a new business in recent years. Unfortunately, the larger the market scale of domain lending service becomes, the riskier that there exist malicious behaviors or malwares hiding behind parked domains will be. Also, previous work for differentiating parked domain suffers two main defects: 1) too much data-collecting effort and CPU latency needed for features engineering and 2) ineffectiveness when detecting parked domains containing external links that are usually abused by hackers, e.g., drive-by download attack. Aiming for alleviating above defects without sacrificing practical usability, this paper proposes ParkedGuard as an efficient and accurate parked domain detector. Several scripting behavioral features were analyzed, while those with special statistical significance are adopted in ParkedGuard to make feature engineering much more cost-efficient. On the other hand, finding memberships between external links and parked domains was modeled as a graph mining problem, and a coarse-to-fine strategy was elaborately designed by leverage the graphical locality such that ParkedGuard outperforms the state-of-the-art in terms of both recall and precision rates.

Keywords: coarse-to-fine strategy, domain parking service, graphical locality analysis, parked domain

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Authors: Mohammadhossein Moghimi, Robert Stone, Pia Rotshtein

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Recording psychological and physiological correlates of human performance within virtual environments and interpreting their impacts on human engagement, ‘immersion’ and related emotional or ‘effective’ states is both academically and technologically challenging. By exposing participants to an effective, real-time (game-like) virtual environment, designed and evaluated in an earlier study, a psychophysiological database containing the EEG, GSR and Heart Rate of 30 male and female gamers, exposed to 10 games, was constructed. Some 174 features were subsequently identified and extracted from a number of windows, with 28 different timing lengths (e.g. 2, 3, 5, etc. seconds). After reducing the number of features to 30, using a feature selection technique, K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) methods were subsequently employed for the classification process. The classifiers categorised the psychophysiological database into four effective clusters (defined based on a 3-dimensional space – valence, arousal and dominance) and eight emotion labels (relaxed, content, happy, excited, angry, afraid, sad, and bored). The KNN and SVM classifiers achieved average cross-validation accuracies of 97.01% (±1.3%) and 92.84% (±3.67%), respectively. However, no significant differences were found in the classification process based on effective clusters or emotion labels.

Keywords: virtual reality, effective computing, effective VR, emotion-based effective physiological database

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Authors: Qais M. Yousef, Yasmeen A. Alshaer

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Over the last few years, the amount of data available on the globe has been increased rapidly. This came up with the emergence of recent concepts, such as the big data and the Internet of Things, which have furnished a suitable solution for the availability of data all over the world. However, managing this massive amount of data remains a challenge due to their large verity of types and distribution. Therefore, locating the required file particularly from the first trial turned to be a not easy task, due to the large similarities of names for different files distributed on the web. Consequently, the accuracy and speed of search have been negatively affected. This work presents a method using Electroencephalography signals to locate the files based on their contents. Giving the concept of natural mind waves processing, this work analyses the mind wave signals of different people, analyzing them and extracting their most appropriate features using multi-objective metaheuristic algorithm, and then classifying them using artificial neural network to distinguish among files with similar names. The aim of this work is to provide the ability to find the files based on their contents using human thoughts only. Implementing this approach and testing it on real people proved its ability to find the desired files accurately within noticeably shorter time and retrieve them as a first choice for the user.

Keywords: artificial intelligence, data contents search, human active memory, mind wave, multi-objective optimization

Procedia PDF Downloads 175

Authors: N. K. Caecar Pratiwi, Yunendah Nur Fuadah, Rita Magdalena, R. D. Atmaja, Sofia Saidah, Ocky Tiaramukti

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Colon cancer or colorectal cancer is a type of cancer that attacks the last part of the human digestive system. Lymphoma and carcinoma are types of cancer that attack human’s colon. Colon cancer causes deaths about half a million people every year. In Indonesia, colon cancer is the third largest cancer case for women and second in men. Unhealthy lifestyles such as minimum consumption of fiber, rarely exercising and lack of awareness for early detection are factors that cause high cases of colon cancer. The aim of this project is to produce a system that can detect and classify images into type of colon cancer lymphoma, carcinoma, or normal. The designed system used 198 data colon cancer tissue pathology, consist of 66 images for Lymphoma cancer, 66 images for carcinoma cancer and 66 for normal / healthy colon condition. This system will classify colon cancer starting from image preprocessing, feature extraction using Principal Component Analysis (PCA) and classification using K-Nearest Neighbor (K-NN) method. Several stages in preprocessing are resize, convert RGB image to grayscale, edge detection and last, histogram equalization. Tests will be done by trying some K-NN input parameter setting. The result of this project is an image processing system that can detect and classify the type of colon cancer with high accuracy and low computation time.

Keywords: carcinoma, colorectal cancer, k-nearest neighbor, lymphoma, principle component analysis

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Authors: W. Ling, Mohamed Saufi Bin Awang

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Introduction: Mycobacterium tuberculosis post a major health problem worldwide. Central nervous system (CNS) infection by mycobacterium tuberculosis is one of the most devastating complications of tuberculosis. Although with advancement in medical fields, we are yet to understand the pathophysiology of how mycobacterium tuberculosis was able to cross the blood-brain barrier (BBB) and infect the CNS. CNS TB may present with nonspecific clinical symptoms which can mimic other diseases/conditions; this is what makes the diagnosis relatively difficult and challenging. Public health has to be informed and educated about the spread of TB, and early identification of TB is important as it is a curable disease. Case Report: A young 21-year-old Malay gentleman was initially presented to us with symptoms of ear discharge, tinnitus, and right-sided headache for the past one year. Further history reveals that the symptoms have been mismanaged and neglected over the period of 1 year. Initial investigation reveals features of inflammation of the ear. Further imaging showed the feature of chronic inflammation of the otitis media and atypical right cerebral abscess, which has the same characteristic features and consistency. He further underwent a biopsy, and results reveal positive Mycobacterium tuberculosis of the otitis media. With the results and the available imaging, we were certain that this is likely a case of disseminated tuberculosis causing CNS TB. Conclusion: We aim to highlight the challenge and difficult face in our health care system and public health in early identification and treatment.

Keywords: central nervous system tuberculosis, intracranial tuberculosis, tuberculous encephalopathy, tuberculous meningitis

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Authors: Robel Legese Meko

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Natural fibers have been used as end-of-life textiles and made into textile products which have become a well-proven and effective way of processing. Nowadays, resources to make primary synthetic fibers are becoming less and less as the world population is rising. Hence it is necessary to develop processes to fabricate textiles that are easily converted to composite materials. Acoustic comfort is closely related to the concept of sound absorption and includes protection against noise. This research paper presents an experimental study on sound absorption coefficients, for natural fiber composite materials: a natural fiber (Coir/Jute) with different blend proportions of raw materials mixed with rigid polyurethane foam as a binder. The natural fiber composite materials were characterized both acoustically (sound absorption coefficient SAC) and also in terms of heat transfer (thermal conductivity). The acoustic absorption coefficient was determined using the impedance tube method according to the ASTM Standard (ASTM E 1050). The influence of the structure of these materials on the sound-absorbing properties was analyzed. The experimental results signify that the porous natural coir/jute composites possess excellent performance in the absorption of high-frequency sound waves, especially above 2000 Hz, and didn’t induce a significant change in the thermal conductivity of the composites. Thus, the sound absorption performances of natural fiber composites based on coir/jute fiber materials promote environmentally friendly solutions.

Keywords: coir/jute fiber, sound absorption coefficients, compression molding, impedance tube, thermal insulating properties, SEM analysis

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Authors: Rafat Y. Alwazna

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Certain religious texts are deemed part of legal texts that are characterised by high sensitivity and sacredness. Amongst such religious texts are Islamic legal texts that are replete with Islamic legal terms that designate particular legal concepts peculiar to Islamic legal system and legal culture. However, from the syntactic perspective, Islamic legal texts prove lengthy, condensed and convoluted, with little use of punctuation system, but with an extensive use of subordinations and co-ordinations, which separate the main verb from the subject, and which, of course, carry a heavy load of legal detail. The present paper seeks to examine the syntactic features of Islamic legal texts through analysing a short text of Islamic jurisprudence in an attempt at exploring the syntactic features that characterise this type of legal text. A translation of this text into legal English is then exercised to find the translation implications that have emerged as a result of the English translation. Based on these implications, the paper compares and contrasts the syntactic features of Islamic legal texts to those of legal English texts. Finally, the present paper argues that there are a number of syntactic features of Islamic legal texts, such as nominalisation, passivisation, little use of punctuation system, the use of the Arabic cohesive device, etc., which are also possessed by English legal texts except for the last feature and with some variations. The paper also claims that when rendering an Islamic legal text into legal English, certain implications emerge, such as the necessity of a sentence break, the omission of the cohesive device concerned and the increase in the use of nominalisation, passivisation, passive participles, and so on.

Keywords: English legal texts, Islamic legal texts, nominalisation, participles, passivisation, syntactic features, translation implications

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Authors: Arth Bohra, Marwa Mahmoud

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In this paper, we utilize computer vision to develop a tool for youth coaches to formulate set-piece tactics for their players. We used the Soccernet database to extract the ResNet features and camera calibration data for over 3000 corner kick across 500 professional matches in the top 6 European leagues (English Premier League, UEFA Champions League, Ligue 1, La Liga, Serie A, Bundesliga). Leveraging the provided homography matrix, we construct a feature vector representing the formation of players on these corner kicks. Additionally, labeling the videos manually, we obtained the pass-trajectory of each of the 3000+ corner kicks by segmenting the field into four zones. Next, after determining the localization of the players and ball, we used event data to give the corner kicks a rating on a 1-4 scale. By employing a Convolutional Neural Network, our model managed to predict the success of a corner kick given the formations of players. This suggests that with the right formations, teams can optimize the way they approach corner kicks. By understanding this, we can help coaches formulate set-piece tactics for their own teams in order to maximize the success of their play. The proposed model can be easily extended; our method could be applied to even more game situations, from free kicks to counterattacks. This research project also gives insight into the myriad of possibilities that artificial intelligence possesses in transforming the domain of sports.

Keywords: soccer, corner kicks, AI, computer vision

Procedia PDF Downloads 174

Authors: Adel Ahmadi Hosseini, Fatemeh Tavanaei, Alessandro Navarra, Ferri Hassani

Abstract:

Mining engineering, as a part of geoscience, must address modern concerns. Traditional mining methods incorporate drill and blast technologies, which are followed by different issues, including excessive noise, vibration, air pollution, and safety hazards. Over the past two decades, mining engineers have sought alternative solutions to move from drill and blast to continuous methods to prevent such issues and improve sustainability in mining. Among the suggested methods, microwave treatment has shown promising results by creating micro/macro cracks in the rock structure prior to the operations. This research utilizes an energy-based analysis methodology to evaluate the efficiency of the microwave treatment in improving mining operations. The data analysis shows that increasing the input microwave energy dosage intensifies the rock damage. However, this approach can decrease the energy efficiency of the method by more than 50% in some cases. In this study, rock samples were treated with three power levels (3 kW, 7 kW, and 12 kW) and two energy dosages (20 kWh/t and 50 kWh/t), resulting in six conditions. To evaluate the impact of microwave treatment on the geomechanical behavior of the rocks, Unconfined Compressive Strength (UCS) tests were conducted on the microwave-treated samples, yielding stress-strain curves. Using the stress-strain curves, the effect of the different powers and energy dosages of microwaves are discussed. This research shows the potential of using microwave treatment to lead the industry to more sustainable mining.

Keywords: microwave treatment, microwave energy dosage, sustainable mining, rock fragmentation

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Authors: Parisa Mansour

Abstract:

The coronavirus disease 2019 (COVID-19) was discovered and rapidly spread to various countries around the world since the end of 2019. Computed tomography (CT) images have been used as an important alternative to the time-consuming RT. PCR test. However, manual segmentation of CT images alone is a major challenge as the number of suspected cases increases. Thus, accurate and automatic segmentation of COVID-19 infections is urgently needed. Because the imaging features of the COVID-19 infection are different and similar to the background, existing medical image segmentation methods cannot achieve satisfactory performance. In this work, we try to build a deep convolutional neural network adapted for the segmentation of chest CT images with COVID-19 infections. First, we maintain a large and novel chest CT image database containing 165,667 annotated chest CT images from 861 patients with confirmed COVID-19. Inspired by the observation that the boundary of an infected lung can be improved by global intensity adjustment, we introduce a feature variable block into the proposed deep CNN, which adjusts the global features of features to segment the COVID-19 infection. The proposed PV array can effectively and adaptively improve the performance of functions in different cases. We combine features of different scales by proposing a progressive atrocious space pyramid fusion scheme to deal with advanced infection regions with various aspects and shapes. We conducted experiments on data collected in China and Germany and showed that the proposed deep CNN can effectively produce impressive performance.

Keywords: chest, COVID-19, chest Image, coronavirus, CT image, chest CT

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Authors: K. Umbleja, M. Ichino

Abstract:

Symbolic data mining has been developed to analyze data in very large datasets. It is also useful in cases when entry specific details should remain hidden. Symbolic data mining is quickly gaining popularity as datasets in need of analyzing are becoming ever larger. One type of such symbolic data is a histogram, which enables to save huge amounts of information into a single variable with high-level of granularity. Other types of symbolic data can also be described in histograms, therefore making histogram a very important and general symbolic data type - a method developed for histograms - can also be applied to other types of symbolic data. Due to its complex structure, analyzing histograms is complicated. This paper proposes a method, which allows to compare two histogram-valued variables and therefore find a dissimilarity between two histograms. Proposed method uses the Ichino-Yaguchi dissimilarity measure for mixed feature-type data analysis as a base and develops a dissimilarity measure specifically for histogram data, which allows to compare histograms with different number of bins and bin widths (so called general histogram). Proposed dissimilarity measure is then used as a measure for clustering. Furthermore, linkage method based on weighted averages is proposed with the concept of cluster compactness to measure the quality of clustering. The method is then validated with application on real datasets. As a result, the proposed dissimilarity measure is found producing adequate and comparable results with general histograms without the loss of detail or need to transform the data.

Keywords: dissimilarity measure, hierarchical clustering, histograms, symbolic data analysis

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Authors: Melody Yin

Abstract:

Concussions impact a large amount of adolescents; they make up as much as half of the diagnosed concussions in America. This research proposes a hybrid machine learning model based on the combination of human/knowledge-based domains and computer-generated feature rankings to improve the accuracy of diagnosing sports related concussion (SRC). Using a data set of symptoms collected on the sideline post-SRC events, the symptom selection criteria method has been developed by using Google AutoML's important score function to identify the top 10 symptom features. In addition, symptom domains have been introduced as another parameter, categorizing the symptoms into physical, cognitive, sleep, and emotional domains. The hybrid machine learning model has been trained with a combination of the top 10 symptoms and 4 domains. From the results, the hybrid model was the best performer for symptom resolution time prediction in 2 and 4-week thresholds. This research is a proof of concept study in the use of domains along with machine learning in order to improve concussion prediction accuracy. It is also possible that the use of domains can make the model more efficient due to reduced training time. This research examines the use of a hybrid method in predicting sports-related concussion. This achievement is based on data preprocessing, using a hybrid method to select criteria to achieve high performance.

Keywords: hybrid model, machine learning, sports related concussion, symptom resolution time

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Authors: Livia Guerini, Christian Paglia

Abstract:

In Switzerland, only a fraction of plastic waste from food packaging is collected and recycled for further use in the food industry. Therefore, reusing these waste plastics for building applications can be an attractive alternative to disposal in order to reduce the problem of waste management and to make up for the depletion of raw materials needed for construction. In this study, experiments were conducted on the mechanical properties (compressive and flexural strength, elastic modulus), physical properties (density, workability, porosity, and water permeability) and durability (freeze/thaw resistance) of cementitious mortars with additions of recycled low-/high-density polyethylene (LDPE/HDPE)/ polypropylene (PP) regrind (addition of 5% and 10% by weight) and LDPE sheets (addition of 0.5% and 1.5% by weight) coming from food packaging. The results show that as the addition of plastic material increases, the density and mechanical properties of the mortars decrease compared to conventional ones. Porosity is similar in all the mixtures made, while the workability and the permeability are affected not only by the amount added but also by the shape of the plastic aggregate. Freeze/thaw resistance, on the other hand, is significantly higher in mortars with plastic aggregates than in traditional mortar. This feature may be interesting for the realization of outdoor mortars in cold environments.

Keywords: food packaging waste, durability properties, mechanical properties, mortar, recycled PE, recycled PP

Procedia PDF Downloads 145