Search results for: wound classification

Authors: Andrey V. Timofeev

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The paper presents new results concerning selection of optimal information fusion formula for ensembles of C-OTDR channels. The goal of information fusion is to create an integral classificator designed for effective classification of seismoacoustic target events. The LPBoost (LP-β and LP-B variants), the Multiple Kernel Learning, and Weighing of Inversely as Lipschitz Constants (WILC) approaches were compared. The WILC is a brand new approach to optimal fusion of Lipschitz Classifiers Ensembles. Results of practical usage are presented.

Keywords: Lipschitz Classifier, classifiers ensembles, LPBoost, C-OTDR systems

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Authors: Kseniia Fedorova

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The article is devoted to interlinguistic homonymy and enantiosemy analysis. These phenomena belong to the process of linguistic interference, which leads to violation of the communicative utterances integrity and causes misunderstanding between foreign interlocutors - native speakers of different Slavic languages. More attention is paid to investigation of non-typical speech situations, which occurred spontaneously or created by somebody intentionally being based on described phenomenon mechanism. The classification of typical students' mistakes connected with the paradox of interference is being represented in the article. The survey contributes to speech act theory, contemporary linguodidactics, translation science and comparative lexicology of Slavonic languages.

Keywords: adherent enantiosemy, interference, interslavonic homonymy, speech act

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Authors: Aayushi Pandey, Manoj Kumar Pandey, Ashutosh Tiwari, Kireet Kumar

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Glaciers play an important role in climate change and are sensitive phenomena of global climate change scenario. Glaciers in Himalayas are unique as they are predominantly valley type and are located in tropical, high altitude regions. These glaciers are often covered with debris which greatly affects ablation rate of glaciers and work as a sensitive indicator of glacier health. The aim of this study is to map high altitude Glacier surface with a focus on glacial lake and debris estimation using different techniques in Nagling glacier of dhauliganga basin in Himalayan region. Different Image Classification techniques i.e. thresholding on different band ratios and supervised classification using maximum likelihood classifier (MLC) have been used on high resolution sentinel 2A level 1c satellite imagery of 14 October 2017.Here Near Infrared (NIR)/Shortwave Infrared (SWIR) ratio image was used to extract the glaciated classes (Snow, Ice, Ice Mixed Debris) from other non-glaciated terrain classes. SWIR/BLUE Ratio Image was used to map valley rock and Debris while Green/NIR ratio image was found most suitable for mapping Glacial Lake. Accuracy assessment was performed using high resolution (3 meters) Planetscope Imagery using 60 stratified random points. The overall accuracy of MLC was 85 % while the accuracy of Band Ratios was 96.66 %. According to Band Ratio technique total areal extent of glaciated classes (Snow, Ice ,IMD) in Nagling glacier was 10.70 km2 nearly 38.07% of study area comprising of 30.87 % Snow covered area, 3.93% Ice and 3.27 % IMD covered area. Non-glaciated classes (vegetation, glacial lake, debris and valley rock) covered 61.93 % of the total area out of which valley rock is dominant with 33.83% coverage followed by debris covering 27.7 % of the area in nagling glacier. Glacial lake and Debris were accurately mapped using Band ratio technique Hence, Band Ratio approach appears to be useful for the mapping of debris covered glacier in Himalayan Region.

Keywords: band ratio, Dhauliganga basin, glacier mapping, Himalayan region, maximum likelihood classifier (MLC), Sentinel-2 satellite image

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Authors: Carlos Arias-Alcaide, Cristina Soguero-Ruiz, Paloma Santos-Álvarez, Adrián García-Romero, Inmaculada Mora-Jiménez

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Analysing clinical data with computers in such a way that have an impact on the practitioners’ workflow is a challenge nowadays. This paper provides a first approach for monitoring the health status of COVID-19 patients through the use of some biomarkers (blood tests) and the simplest Naïve Bayes classifier. Data of two Spanish hospitals were considered, showing the potential of our approach to estimate reasonable posterior probabilities even some days before the event.

Keywords: Bayesian model, blood biomarkers, classification, health tracing, machine learning, posterior probability

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Authors: J. R. Wang, S. W. Chen, Y. Chiang, W. S. Hsu, J. H. Yang, Y. S. Tseng, C. Shih

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In this research, the HABIT analysis methodology was established for Maanshan nuclear power plant (NPP). The Final Safety Analysis Report (FSAR), reports, and other data were used in this study. To evaluate the control room habitability under the CO₂ storage burst, the HABIT methodology was used to perform this analysis. The HABIT result was below the R.G. 1.78 failure criteria. This indicates that Maanshan NPP habitability can be maintained. Additionally, the sensitivity study of the parameters (wind speed, atmospheric stability classification, air temperature, and control room intake flow rate) was also performed in this research.

Keywords: PWR, HABIT, Habitability, Maanshan

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Authors: Y. Chiang, W. Y. Li, J. R. Wang, S. W. Chen, W. S. Hsu, J. H. Yang, Y. S. Tseng, C. Shih

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In this research, the HABIT code was used to estimate the concentration under the CO₂ and H₂SO₄ storage burst conditions for Kuosheng nuclear power plant (NPP). The Final Safety Analysis Report (FSAR) and reports were used in this research. In addition, to evaluate the control room habitability for these cases, the HABIT analysis results were compared with the R.G. 1.78 failure criteria. The comparison results show that the HABIT results are below the criteria. Additionally, some sensitivity studies (stability classification, wind speed and control room intake rate) were performed in this study.

Keywords: BWR, HABIT, habitability, Kuosheng

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Authors: Dorra Kallel, Ines Kanoun, Diala Dhouib

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This paper elaborates a Systematic Literature Review SLR) to select the most outstanding studies that address the Menu Planning Problem (MPP) and to classify them according to the to the three following criteria: the used methods, types of patients and the required constraints. At first, a set of 4165 studies was selected. After applying the SLR’s guidelines, this collection was filtered to 13 studies using specific inclusion and exclusion criteria as well as an accurate analysis of each study. Second, the selected papers were invested to answer the proposed research questions. Finally, data synthesis and new perspectives for future works are incorporated in the closing section.

Keywords: Menu Planning Problem (MPP), Systematic Literature Review (SLR), classification, exact and approaches methods

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Authors: Maimona A. E. Elimam, Suhair Rehan, Miskelyemen A. Elmekki, Mogahid M. Elhassan

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Staphylococcus aureus (Staph. aureus), a major cause of potentially life-threatening infections acquired in healthcare and community settings, has developed resistance to most classes of antimicrobial agents as determined by the dramatic increase. The present study aimed to determine the prevalence of MRSA, and VRSA in patients with different clinical manifestations in Khartoum state. The study population (n, 426) were males and females with different age categories, suffering either from wound infections (105), ear infections (121), or UTI (101), in addition to nasal carriers of medical staff (100). Cultures, Gram staining, and other biochemical tests were performed for conventional identification. Modified Kirby-Bauer disk diffusion method was applied and DNA was extracted from MRSA and VRSA isolates and PCR was then performed for amplification of arc, mecA, VanA, and VanB genes. The results confirmed the existence of Staph. aureus in 49/426 (11.5%) cases among which MRSA were isolated from 34/49 (69.4%) when modified Kirby-Bauer disk diffusion method was applied. Ten out of these 34 MRSA were confirmed as VRSA by cultures on BHI agar containing 6μg/ml vancomycin according to NCCLS criteria. PCR revealed that out of the 34 MRSA isolates, 26 were mecA positive (76.5%) while 8 (23.5%) were arcC positive. No vanA or VanB genes were detected. Molecular method confirmed the results for MRSA through the presence of either arcC or mecA genes while it failed to approve the occurrence of VRSA since neither VanA or VanB genes were detected. Thus, VRSA may be attributed to other factors.

Keywords: antibiotic resistance, Staphylococcus aureus, VRSA, MRSA, Khartoum, Sudan

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Authors: Alicia Yanadira Nava Fuentes, Ilse Cervantes Camacho, Amadeo José Argüelles Cruz, Ana María Balboa Verduzco

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Augmented reality promises being present in future driving, with its immersive technology let to show directions and maps to identify important places indicating with graphic elements when the car driver requires the information. On the other side, driving is considered a multitasking activity and, for some people, a complex activity where different situations commonly occur that require the immediate attention of the car driver to make decisions that contribute to avoid accidents; therefore, the main aim of the project is the instrumentation of a platform with biometric sensors that allows evaluating the performance in driving vehicles with the influence of augmented reality devices to detect the level of attention in drivers, since it is important to know the effect that it produces. In this study, the physiological sensors EPOC X (EEG), ECG06 PRO and EMG Myoware are joined in the driving test platform with a Logitech G29 steering wheel and the simulation software City Car Driving in which the level of traffic can be controlled, as well as the number of pedestrians that exist within the simulation obtaining a driver interaction in real mode and through a MSP430 microcontroller achieves the acquisition of data for storage. The sensors bring a continuous analog signal in time that needs signal conditioning, at this point, a signal amplifier is incorporated due to the acquired signals having a sensitive range of 1.25 mm/mV, also filtering that consists in eliminating the frequency bands of the signal in order to be interpretative and without noise to convert it from an analog signal into a digital signal to analyze the physiological signals of the drivers, these values are stored in a database. Based on this compilation, we work on the extraction of signal features and implement K-NN (k-nearest neighbor) classification methods and decision trees (unsupervised learning) that enable the study of data for the identification of patterns and determine by classification methods different effects of augmented reality on drivers. The expected results of this project include are a test platform instrumented with biometric sensors for data acquisition during driving and a database with the required variables to determine the effect caused by augmented reality on people in simulated driving.

Keywords: augmented reality, driving, physiological signals, test platform

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Authors: Ching-Yi Huang, Pi-Fen Cheng, Hui-Zhu Chen, Shi Ting Huang, Heng-Hua Wang

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This is a case of oral cancer after extensive resection and modified right lateral neck lymph node dissection followed by reconstruction with a skin flap. The nursing period lasted From September 25 to October 3, 2017, through observation, interview, physical assessment, and medical record review, the author identified the following nursing problems: acute pain, impaired oral mucous membrane, and body image change. During the nursing period, the author provided individual and overall nursing care and established mutual trust through the use of empathy. Author listened and eased the patient's physical indisposition, such as wound pain, we use medications and acupuncture massage to relieve pain. However, for oral mucosa change caused by surgery, provide continuous and complete oral care and oral exercise training to improve oral mucosal healing and restore swallowing function. In the body-image changes, guided him to express his feeling after the body-image change, and enhanced support and from the family, and encouraged him to attend head and neck cancer survivor alliance which allowed the patient to accept the altered body image and reaffirm self-worth. Hopefully, through sharing this nursing experience will help to the nursing care quality of nursing care for oral cancer patients after extensive resection and modified right lateral neck lymph node dissection followed by reconstruction with a skin flap.

Keywords: oral cancer, acute pain, impaired oral mucous membrane, body image change

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Authors: Mariam Matiashvili

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Argumentation is an integral part of our daily communications - formal or informal. Argumentative reasoning, techniques, and language tools are used both in personal conversations and in the business environment. Verbalization of the opinions requires the use of extraordinary syntactic-pragmatic structural quantities - arguments that add credibility to the statement. The study of argumentative structures allows us to identify the linguistic features that make the text argumentative. Knowing what elements make up an argumentative text in a particular language helps the users of that language improve their skills. Also, natural language processing (NLP) has become especially relevant recently. In this context, one of the main emphases is on the computational processing of argumentative texts, which will enable the automatic recognition and analysis of large volumes of textual data. The research deals with the linguistic analysis of the argumentative structures of Georgian political speeches - particularly the linguistic structure, characteristics, and functions of the parts of the argumentative text - claims, support, and attack statements. The research aims to describe the linguistic cues that give the sentence a judgmental/controversial character and helps to identify reasoning parts of the argumentative text. The empirical data comes from the Georgian Political Corpus, particularly TV debates. Consequently, the texts are of a dialogical nature, representing a discussion between two or more people (most often between a journalist and a politician). The research uses the following approaches to identify and analyze the argumentative structures Lexical Classification & Analysis - Identify lexical items that are relevant in argumentative texts creating process - Creating the lexicon of argumentation (presents groups of words gathered from a semantic point of view); Grammatical Analysis and Classification - means grammatical analysis of the words and phrases identified based on the arguing lexicon. Argumentation Schemas - Describe and identify the Argumentation Schemes that are most likely used in Georgian Political Speeches. As a final step, we analyzed the relations between the above mentioned components. For example, If an identified argument scheme is “Argument from Analogy”, identified lexical items semantically express analogy too, and they are most likely adverbs in Georgian. As a result, we created the lexicon with the words that play a significant role in creating Georgian argumentative structures. Linguistic analysis has shown that verbs play a crucial role in creating argumentative structures.

Keywords: georgian, argumentation schemas, argumentation structures, argumentation lexicon

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Authors: B. Kenny

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The complications associated with transfusions are well documented, with the serious hazards of transfusion (SHOT) reporting system continuing to report deaths and serious morbidity due to the transfusion of allogenic blood. Many different sources including the TRICC trial, NHMRC and Cochrane recommending similar transfusion triggers/guidelines. Recent studies found the rate of infection (deep infection, wound infection, chest infection, urinary tract infection, and others) were purely a dose response relationship, increasing the Relative Risk by 3.44. It was also noted that each transfused patient stayed in hospital for one additional day. We hypothesise that providing an approved/standardised, guideline with a graphical summary of decision pathways for anaemic patients will reduce unnecessary transfusions. We retrospectively assessed patients undergoing primary knee or hip arthroplasties over a 4 year period, 1459 patients. Of these, 339 (23.24%) patients received allogenic blood transfusions and 858 units of blood were transfused, 9.14% of patients transfused had haemoglobin levels above 100 g/L, 7.67% of patients were transfused without knowing the haemoglobin level, 24 hours prior to transfusion initiation and 4.5% had possible transfusion reactions. Overall, 17% of allogenic transfusions topatients admitted to the Orthopaedic department within a 4 year period were outside NHMRC and Cochrane guidelines/recommendations. If our transfusion frequency is compared with that of other authors/hospitals, transfusion rates are consistently being high. We subsequently implemented a simple guideline for transfusion initiation. This guideline was then assessed. We found the transfusion rate post transfusion implementation to be significantly lower, without increase in patient morbidity or mortalitiy, p <0.001). Transfusion rates and patient outcome can be optimized by a simple graphical aid for decision making.

Keywords: transfusion, morbidity, mortality, neck of femur, fracture, arthroplasty, rehabilitation

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Authors: Sharjeel Abid, Tanveer Hussain, Ahsan Nazir, Abdul Zahir, Nabyl Khenoussi

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Localized application of drug has various advantages and fewer side effects as compared with other methods. Burn patients suffer from swear pain and the major aspects that are considered for burn victims include pain and infection management. Nano-fibers (NFs) loaded with drug, applied on local wound area, can solve these problems. Therefore, this study dealt with the fabrication of drug loaded NFs for better pain management. Two layers of NFs were fabricated with different drugs. Contact layer was loaded with Gabapentin (a nerve painkiller) and the second layer with acetaminophen. The fabricated dressing was characterized using scanning electron microscope, Fourier Transform Infrared Spectroscopy, X-Ray Diffraction and UV-Vis Spectroscopy. The double layered based NFs dressing was designed to have both initial burst release followed by slow release to cope with pain for two days. The fabricated nanofibers showed diameter < 300 nm. The liquid absorption capacity of the NFs was also checked to deal with the exudate. The fabricated double layered dressing with dual drug loading and release showed promising results that could be used for dealing pain in burn victims. It was observed that by the addition of drug, the size of nanofibers was reduced, on the other hand, the crystallinity %age was increased, and liquid absorption decreased. The combination of fast nerve pain killer release followed by slow release of non-steroidal anti-inflammatory drug could be a good tool to reduce pain in a more secure manner with fewer side effects.

Keywords: pain management, burn wounds, nano-fibers, controlled drug release

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Authors: Arun Prasath Subramanian

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The turbine Inlet Temperature is an important parameter which determines the efficiency of a gas turbine engine. The increase in this parameter is limited by material constraints of the turbine blade.The modern Gas turbine blade has undergone a drastic change from a simple solid blade to a modern multi-pass blade with internal and external cooling techniques. This paper aims to introduce the reader the concept of turbine blade cooling, the classification of techniques and further explain some of the important internal cooling technologies used in a modern gas turbine blade along with the various factors that affect the cooling effectiveness.

Keywords: gas turbine blade, cooling technologies, internal cooling, pin-fin cooling, jet impingement cooling, rib turbulated cooling, metallic foam cooling

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Authors: Evan Lowhorn, Rocio Alba-Flores

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Traditional classification Convolutional Neural Networks (CNN) attempt to classify an image in its entirety. This becomes problematic when trying to perform classification with a drone’s camera in real-time due to unpredictable backgrounds. Object detectors with bounding boxes can be used to isolate individuals and other items, but the original backgrounds remain within these boxes. These basic detectors have been regularly used to determine what type of object an item is, such as “person” or “dog.” Recent advancement in computer vision, particularly with human imaging, is keypoint detection. Human keypoint detection goes beyond bounding boxes to fully isolate humans and plot points, or Regions of Interest (ROI), on their bodies within an image. ROIs can include shoulders, elbows, knees, heads, etc. These points can then be related to each other and used in deep learning methods such as pose estimation. For drone control based on human motions, poses, or signals using the onboard camera, it is important to have a simple method for pilot identification among multiple individuals while also giving the pilot fine control options for the drone. To achieve this, the OpenPose keypoint detection network was used with body and hand keypoint detection enabled. OpenPose supports the ability to combine multiple keypoint detection methods in real-time with a single network. Body keypoint detection allows simple poses to act as the pilot identifier. The hand keypoint detection with ROIs for each finger can then offer a greater variety of signal options for the pilot once identified. For this work, the individual must raise their non-control arm to be identified as the operator and send commands with the hand on their other arm. The drone ignores all other individuals in the onboard camera feed until the current operator lowers their non-control arm. When another individual wish to operate the drone, they simply raise their arm once the current operator relinquishes control, and then they can begin controlling the drone with their other hand. This is all performed mid-flight with no landing or script editing required. When using a desktop with a discrete NVIDIA GPU, the drone’s 2.4 GHz Wi-Fi connection combined with OpenPose restrictions to only body and hand allows this control method to perform as intended while maintaining the responsiveness required for practical use.

Keywords: computer vision, drone control, keypoint detection, openpose

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Authors: Sofia Matoug, Amr Abdel-Dayem

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This paper presents a comprehensive survey of recent research studies to segment and classify brain MR (magnetic resonance) images in order to detect significant changes to brain ventricles. The paper also presents a general framework for detecting regions that atrophy, which can help neurologists in detecting and staging Alzheimer. Furthermore, a prototype was implemented to segment brain MR images in order to extract the region of interest (ROI) and then, a classifier was employed to differentiate between normal and abnormal brain tissues. Experimental results show that the proposed scheme can provide a reliable second opinion that neurologists can benefit from.

Keywords: Alzheimer, brain images, classification techniques, Magnetic Resonance Images MRI

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Authors: Duckyong Kim, Jong Kang Park, Jong Tae Kim

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DOA (Direction of Arrival) estimation is an important method in array signal processing and has a wide range of applications such as direction finding, beam forming, and so on. In this paper, we briefly introduce the MUSIC (Multiple Signal Classification) Algorithm, one of DOA estimation methods for analyzing several targets. Then we apply the MUSIC algorithm to the two-dimensional antenna array to analyze DOA estimation in 3D space through MATLAB simulation. We also analyze the design factors that can affect the accuracy of DOA estimation through simulation, and proceed with further consideration on how to apply the system.

Keywords: DOA estimation, MUSIC algorithm, spatial spectrum, array signal processing

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Authors: Dylan Shiting Lu, Samson Okello, Anita Chunyan Wei, Daniel Xiao Li

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Mammotome vacuum-assisted breast biopsy (MVB) introduced in 1995 can be used for the removal of benign breast lesions. Whether or not MVB is a better option compared to conventional open surgery is inconclusive. We aim to compare the clinical and patient-related outcomes between MVB and open surgery to remove benign breast tumors less than 5 cm in women. We searched English and Chinese electronic databases with the keywords of Mammotome, clinical trial (CT), vacuum-assisted breast biopsy for studies comparing MVB and open surgery until May 2021. We performed a systematic review and random-effects meta-analysis to compare incision size, operation time, intraoperative blood loss, healing time, scar length, patient satisfaction, postoperative hematoma rate, wound infection rate, postoperative ecchymosis, and postoperative sunken skin among those who have Mammotome and those who have surgery. Our analysis included nine randomized CTs with 1155 total patients (575 Mammotome, 580 surgery) and mean age 40.32 years (standard deviation 3.69). We found statistically significant favorable outcomes for Mammotome including blood loss (ml) [standardized mean difference SMD -5.03, 95%CI (-7.30, -2.76)], incision size (cm) [SMD -12.22, 95%CI (-17.40, -7.04)], operation time (min) [SMD -6.66, 95%CI (-9.01, -4.31)], scar length (cm) [SMD -7.06, 95%CI (-10.76, -3.36)], healing time (days) [SMD -6.57, 95%CI (-10.18, -2.95)], and patient satisfaction [relative risk RR 0.38, 95%CI (0.13, 1.08)]. In conclusion, Mammotome vacuum-assisted breast biopsy compared to open surgery shows better clinical and patient-related outcomes. Further studies should be done on whether or not MVB is a better option for benign breast tumors excision.

Keywords: clinical and patient outcomes, open surgery, Mammotome vacuum-assisted breast biopsy, meta-analysis

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Authors: Elcin Timur Cakmak, Ayse Oguzlar

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This study presents the results of the tweets sent by Twitter users on social media about the Russia-Ukraine war by bigram and trigram methods. On February 24, 2022, Russian President Vladimir Putin declared a military operation against Ukraine, and all eyes were turned to this war. Many people living in Russia and Ukraine reacted to this war and protested and also expressed their deep concern about this war as they felt the safety of their families and their futures were at stake. Most people, especially those living in Russia and Ukraine, express their views on the war in different ways. The most popular way to do this is through social media. Many people prefer to convey their feelings using Twitter, one of the most frequently used social media tools. Since the beginning of the war, it is seen that there have been thousands of tweets about the war from many countries of the world on Twitter. These tweets accumulated in data sources are extracted using various codes for analysis through Twitter API and analysed by Python programming language. The aim of the study is to find the word sequences in these tweets by the n-gram method, which is known for its widespread use in computational linguistics and natural language processing. The tweet language used in the study is English. The data set consists of the data obtained from Twitter between February 24, 2022, and April 24, 2022. The tweets obtained from Twitter using the #ukraine, #russia, #war, #putin, #zelensky hashtags together were captured as raw data, and the remaining tweets were included in the analysis stage after they were cleaned through the preprocessing stage. In the data analysis part, the sentiments are found to present what people send as a message about the war on Twitter. Regarding this, negative messages make up the majority of all the tweets as a ratio of %63,6. Furthermore, the most frequently used bigram and trigram word groups are found. Regarding the results, the most frequently used word groups are “he, is”, “I, do”, “I, am” for bigrams. Also, the most frequently used word groups are “I, do, not”, “I, am, not”, “I, can, not” for trigrams. In the machine learning phase, the accuracy of classifications is measured by Classification and Regression Trees (CART) and Naïve Bayes (NB) algorithms. The algorithms are used separately for bigrams and trigrams. We gained the highest accuracy and F-measure values by the NB algorithm and the highest precision and recall values by the CART algorithm for bigrams. On the other hand, the highest values for accuracy, precision, and F-measure values are achieved by the CART algorithm, and the highest value for the recall is gained by NB for trigrams.

Keywords: classification algorithms, machine learning, sentiment analysis, Twitter

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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: Melis Inalpulat, Levent Genc

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Land Use Land Cover (LULC) changes due to human activities and natural causes have become a major environmental concern. Assessment of temporal remote sensing data provides information about LULC impacts on environment. Land Surface Temperature (LST) is one of the important components for modeling environmental changes in climatological, hydrological, and agricultural studies. In this study, LULC changes (September 7, 1984 and July 8, 2014) especially in agricultural lands together with population changes (1985-2014) and LST status were investigated using remotely sensed and census data in South Marmara Watershed, Turkey. LULC changes were determined using Landsat TM and Landsat OLI data acquired in 1984 and 2014 summers. Six-band TM and OLI images were classified using supervised classification method to prepare LULC map including five classes including Forest (F), Grazing Land (G), Agricultural Land (A), Water Surface (W), and Residential Area-Bare Soil (R-B) classes. The LST image was also derived from thermal bands of the same dates. LULC classification results showed that forest areas, agricultural lands, water surfaces and residential area-bare soils were increased as 65751 ha, 20163 ha, 1924 ha and 20462 ha respectively. In comparison, a dramatic decrement occurred in grazing land (107985 ha) within three decades. The population increased % 29 between years 1984-2014 in whole study area. Along with the natural causes, migration also caused this increase since the study area has an important employment potential. LULC was transformed among the classes due to the expansion in residential, commercial and industrial areas as well as political decisions. In the study, results showed that agricultural lands around the settlement areas transformed to residential areas in 30 years. The LST images showed that mean temperatures were ranged between 26-32 °C in 1984 and 27-33 °C in 2014. Minimum temperature of agricultural lands was increased 3 °C and reached to 23 °C. In contrast, maximum temperature of A class decreased to 41 °C from 44 °C. Considering temperatures of the 2014 R-B class and 1984 status of same areas, it was seen that mean, min and max temperatures increased by 2 °C. As a result, the dynamism of population, LULC and LST resulted in increasing mean and maximum surface temperatures, living spaces/industrial areas and agricultural lands.

Keywords: census data, landsat, land surface temperature (LST), land use land cover (LULC)

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Authors: E. Mike Dison, T. Pathinathan

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Computing with Words (CWW) and Possibilistic Relational Universal Fuzzy (PRUF) are the two concepts which widely represent and measure the vaguely defined natural phenomenon. In this paper, we study the positional alteration of the phrases by which the impact of a natural language proposition gets affected and/or modified. We observe the gradations due to sensitivity/feeling of a statement towards the positional alterations. We derive the classification and modification of the meaning of words due to the positional alteration. We present the results with reference to set theoretic interpretations.

Keywords: appositive, computing with words, possibilistic relational universal fuzzy (PRUF), semantic sentiment analysis, set-theoretic interpretations

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Authors: Kefaya Qaddoum

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The aim of this study is to predict breast cancer and to construct a supportive model that will stimulate a more reliable prediction as a factor that is fundamental for public health. In this study, we utilize general regression neural networks (GRNN) to replace the normal predictions with prediction periods to achieve a reasonable percentage of confidence. The mechanism employed here utilises a machine learning system called conformal prediction (CP), in order to assign consistent confidence measures to predictions, which are combined with GRNN. We apply the resulting algorithm to the problem of breast cancer diagnosis. The results show that the prediction constructed by this method is reasonable and could be useful in practice.

Keywords: neural network, conformal prediction, cancer classification, regression

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Authors: Ashvamedh, Chandra Bhushan Singh, Anil Kumar Sarda

Abstract:

Introduction: Ileal perforation is a common cause for peritonitis in developing countries. Surgery is the ideal treatment as it eliminates soilage of peritoneal cavity in an effort to lessen the toxaemia and enhance the recovery of the patient. However, there is no uniformity of standardized operative procedure that is most effective for management. Material and method: The study was conducted on 66 patients of perforation peritonitis from November 2013 to February 2015 in Lok Nayak Hospital. Data of each patient were recorded on a pre-determined proforma. The methods used for repair were Primary repair, Resection anastomosis (RA) and Ileostomy. Result: Male preponderance was noticed among the patients with majority in their third decade. Of all perforations 40.9% were tubercular and 34.8% were typhoid. Amongst operated cases 27.3% underwent primary repair, RA was performed in 45.5%, Ileostomy in 27.3%patients. The average time taken for RA and ileostomy was more than primary repair. The type of repair bear no significance to size or no of perforation but was significant statistically for distance from I/C valve(P=.005) and edema of bowel wall(p=.002) when analysed for post op complications. Wound infection, dehiscence, intra-abdominal collections were complications observed bearing no significance to type of repair. Ileostomy per se has its own complications peristomal skin excoriation seen in 83.3%, electrolyte imbalance in 33.3%, duration for closure averaged 188 days (median 150 days, range 85-400 days). Conclusion: Primary closure is preferable in patients with single, small perforations. RA is advocated in patients with multiple or large perforation, perforation proximal to stricture. Ileostomy should not be considered as primary definitive procedure and reserved only for moribund patients as a lifesaving procedure. It has more morbidity and requires a second surgery for closure increasing the cost of treatment as well.

Keywords: ileal perforation, ileostomy, perforation peritonitis, typhoid perforation management

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Authors: Emin Gundogar, Burak Erkayman, Nusret Sazak

Abstract:

Providing effective management performance through the whole supply chain is critical issue and hard to applicate. The proper evaluation of integrated data may conclude with accurate information. Analysing the supply chain data through OLAP (On-Line Analytical Processing) technologies may provide multi-angle view of the work and consolidation. In this study, association rules and classification techniques are applied to measure the supply chain performance metrics of an automotive manufacturer in Turkey. Main criteria and important rules are determined. The comparison of the results of the algorithms is presented.

Keywords: supply chain performance, performance measurement, data mining, automotive

Procedia PDF Downloads 513

Authors: Amin Derakhshan, Mohammad Mikaili, Mohammad Ali Khalilzadeh, Amin Mohammadian

Abstract:

This paper proposes a stress recognition system from multi-modal bio-potential signals. For stress recognition, Support Vector Machines (SVM) and LDA are applied to design the stress classifiers and its characteristics are investigated. Using gathered data under psychological polygraph experiments, the classifiers are trained and tested. The pattern recognition method classifies stressful from non-stressful subjects based on labels which come from polygraph data. The successful classification rate is 96% for 12 subjects. It means that facial thermal imaging due to its non-contact advantage could be a remarkable alternative for psycho-physiological methods.

Keywords: stress, thermal imaging, face, SVM, polygraph

Procedia PDF Downloads 487

Authors: Onur Ozveri, Korkut Karabag, Cagri Keles

Abstract:

It is an important issue that the occurring of production downtime and repair costs when machines fail in the machine intensive production industries. In the case of failure of more than one machine at the same time, which machines will have the priority to repair, how to determine the optimal repair time should be allotted for this machines and how to plan the resources needed to repair are the key issues. In recent years, Business Process Management (BPM) technique, bring effective solutions to different problems in business. The main feature of this technique is that it can improve the way the job done by examining in detail the works of interest. In the industries, maintenance and repair works are operating as a process and when a breakdown occurs, it is known that the repair work is carried out in a series of process. Maintenance main-process and repair sub-process are evaluated with process management technique, so it is thought that structure could bring a solution. For this reason, in an international manufacturing company, this issue discussed and has tried to develop a proposal for a solution. The purpose of this study is the implementation of maintenance and repair works which is integrated with process management technique and at the end of implementation, analyzing the maintenance related parameters like quality, cost, time, safety and spare part. The international firm that carried out the application operates in a free region in Turkey and its core business area is producing original equipment technologies, vehicle electrical construction, electronics, safety and thermal systems for the world's leading light and heavy vehicle manufacturers. In the firm primarily, a project team has been established. The team dealt with the current maintenance process again, and it has been revised again by the process management techniques. Repair process which is sub-process of maintenance process has been discussed again. In the improved processes, the ABC equipment classification technique was used to decide which machine or machines will be given priority in case of failure. This technique is a prioritization method of malfunctioned machine based on the effect of the production, product quality, maintenance costs and job security. Improved maintenance and repair processes have been implemented in the company for three months, and the obtained data were compared with the previous year data. In conclusion, breakdown maintenance was found to occur in a shorter time, with lower cost and lower spare parts inventory.

Keywords: ABC equipment classification, business process management (BPM), maintenance, repair performance

Procedia PDF Downloads 195

Authors: Samir Kittaniy

Abstract:

The “Mirrors of Princes” is considered one of the most important literary types in Arabic and Islamic heritage. The term can be found in various types of “Adab”. The paper deals with the phrase: “Mirrors of princes” itself, showing its nature and the extent of its spread among researchers. Thus, the article relates to one of the main cultural pillars of the literary heritage. Creative individuals within the framework of this type of “Adab” have viewed the rulers as the ultimate goal they try to reach in their classification efforts, with the aim of educating, entertaining and amusing. Most literary classifications were submitted as a gift to the rulers, in an attempt to get closer to them. Pragmatic moral and political advices were among the most prominent issues to gain the approval of rulers.

Keywords: Islam, Arabic, literature, Middle East, mirrors of princes

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Authors: Tesnim Charrad, Kaouther Nouira, Ahmed Ferchichi

Abstract:

In order to reduce the number of deaths due to heart problems, we propose the use of Hierarchical Temporal Memory Algorithm (HTM) which is a real time anomaly detection algorithm. HTM is a cortical learning algorithm based on neocortex used for anomaly detection. In other words, it is based on a conceptual theory of how the human brain can work. It is powerful in predicting unusual patterns, anomaly detection and classification. In this paper, HTM have been implemented and tested on ECG datasets in order to detect cardiac anomalies. Experiments showed good performance in terms of specificity, sensitivity and execution time.

Keywords: cardiac anomalies, ECG, HTM, real time anomaly detection

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Authors: Midhun Paul, T. Radha Ramanan

Abstract:

The objective of this work is to present a state-of-the-art literature review highlighting the challenges in the research of the scheduling of assembly job shop problem and providing an insight on how the future directions of the research would be. The number of work has been substantial that it requires a review to enable one to understand the origin of the research and how it is getting evolved. This review paper presents a comprehensive review of the literature dealing with various studies carried on assembly job shop scheduling. The review details the evolution of the AJS from the perspective of other scheduling problems and also presents a classification scheme. The work also identifies the potential directions for future research, which we believe to be worthwhile considering.

Keywords: assembly job shop, future directions, manufacturing, scheduling

Procedia PDF Downloads 413