Search results for: machine vision

Authors: Jameela Ali, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

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Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-alaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively.

Keywords: K-nearest neighbors algorithm, radial basis function neural network, red blood cells, support vector machine

Procedia PDF Downloads 405

Authors: Rishabh Ambavanekar

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Expressive Aphasia (EA) is an oral disability, common among stroke victims, in which the Broca’s area of the brain is damaged, interfering with verbal communication abilities. EA currently has no technological solutions and its only current viable solutions are inefficient or only available to the affluent. This prompts the need for an affordable, innovative solution to facilitate recovery and assist in speech generation. This project proposes a novel concept: using a wearable low-cost electroencephalography (EEG) device-based brain-computer interface (BCI) to translate a user’s inner dialogue into words. A low-cost EEG device was developed and found to be 10 to 100 times less expensive than any current EEG device on the market. As part of the BCI, a machine learning (ML) model was developed and trained using the EEG data. Two stages of testing were conducted to analyze the effectiveness of the device: a proof-of-concept and a final solution test. The proof-of-concept test demonstrated an average accuracy of above 90% and the final solution test demonstrated an average accuracy of above 75%. These two successful tests were used as a basis to demonstrate the viability of BCI research in developing lower-cost verbal communication devices. Additionally, the device proved to not only enable users to verbally communicate but has the potential to also assist in accelerated recovery from the disorder.

Keywords: neurotechnology, brain-computer interface, neuroscience, human-machine interface, BCI, HMI, aphasia, verbal disability, stroke, low-cost, machine learning, ML, image recognition, EEG, signal analysis

Procedia PDF Downloads 116

Authors: Soumya Sajjan

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Liver cancer is the most common cancer disease worldwide in men and women, and is one of the few cancers still on the rise. Liver disease is the 4th leading cause of death. According to new NHS (National Health Service) figures, deaths from liver diseases have reached record levels, rising by 25% in less than a decade; heavy drinking, obesity, and hepatitis are believed to be behind the rise. In this study, we focus on Development of Diagnostic Classifier for Ultrasound liver lesion. Ultrasound (US) Sonography is an easy-to-use and widely popular imaging modality because of its ability to visualize many human soft tissues/organs without any harmful effect. This paper will provide an overview of underlying concepts, along with algorithms for processing of liver ultrasound images Naturaly, Ultrasound liver lesion images are having more spackle noise. Developing classifier for ultrasound liver lesion image is a challenging task. We approach fully automatic machine learning system for developing this classifier. First, we segment the liver image by calculating the textural features from co-occurrence matrix and run length method. For classification, Support Vector Machine is used based on the risk bounds of statistical learning theory. The textural features for different features methods are given as input to the SVM individually. Performance analysis train and test datasets carried out separately using SVM Model. Whenever an ultrasonic liver lesion image is given to the SVM classifier system, the features are calculated, classified, as normal and diseased liver lesion. We hope the result will be helpful to the physician to identify the liver cancer in non-invasive method.

Keywords: segmentation, Support Vector Machine, ultrasound liver lesion, co-occurance Matrix

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Authors: Emily Wood, James K. Westphal, Itamar Lerner

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Background: Polysomnography (PSG) recordings are regularly used in research and clinical settings to study sleep and sleep-related disorders. Typical PSG studies are conducted in professional laboratories and performed by qualified researchers. However, the number of sleep labs worldwide is disproportionate to the increasing number of individuals with sleep disorders like sleep apnea and insomnia. Consequently, there is a growing need to supply cheaper yet reliable means to measure sleep, preferably autonomously by subjects in their own home. Over the last decade, a variety of devices for self-monitoring of sleep became available in the market; however, very few have been directly validated against PSG to demonstrate their ability to perform reliable automatic sleep scoring. Two popular mobile EEG-based systems that have published validation results, the DREEM 3 headband and the Z-Machine, have never been directly compared one to the other by independent researchers. The current study aimed to compare the performance of DREEM 3 and the Z-Machine to help investigators and clinicians decide which of these devices may be more suitable for their studies. Methods: 26 participants have completed the study for credit or monetary compensation. Exclusion criteria included any history of sleep, neurological or psychiatric disorders. Eligible participants arrived at the lab in the afternoon and received the two devices. They then spent two consecutive nights monitoring their sleep at home. Participants were also asked to keep a sleep log, indicating the time they fell asleep, woke up, and the number of awakenings occurring during the night. Data from both devices, including detailed sleep hypnograms in 30-second epochs (differentiating Wake, combined N1/N2, N3; and Rapid Eye Movement sleep), were extracted and aligned upon retrieval. For analysis, the number of awakenings each night was defined as four or more consecutive wake epochs between sleep onset and termination. Total sleep time (TST) and the number of awakenings were compared to subjects’ sleep logs to measure consistency with the subjective reports. In addition, the sleep scores from each device were compared epoch-by-epoch to calculate the agreement between the two devices using Cohen’s Kappa. All analysis was performed using Matlab 2021b and SPSS 27. Results/Conclusion: Subjects consistently reported longer times spent asleep than the time reported by each device (M= 448 minutes for sleep logs compared to M= 406 and M= 345 minutes for the DREEM and Z-Machine, respectively; both ps<0.05). Linear correlations between the sleep log and each device were higher for the DREEM than the Z-Machine for both TST and the number of awakenings, and, likewise, the mean absolute bias between the sleep logs and each device was higher for the Z-Machine for both TST (p<0.001) and awakenings (p<0.04). There was some indication that these effects were stronger for the second night compared to the first night. Epoch-by-epoch comparisons showed that the main discrepancies between the devices were for detecting N2 and REM sleep, while N3 had a high agreement. Overall, the DREEM headband seems superior for reliably scoring sleep at home.

Keywords: DREEM, EEG, seep monitoring, Z-machine

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Authors: Maryam Khalilzadeh Poshtegal, Seyed Ahmad Mirbagheri, Mojtaba Noury

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In this paper, by application of the input yearly data of rainfall, temperature and flow to the Urmia Lake, the simulation of water level fluctuation were applied by means of three models. According to the climate change investigation the fluctuation of lakes water level are of high interest. This study investigate data-driven models, support vector machines (SVM), SVM method which is a new regression procedure in water resources are applied to the yearly level data of Lake Urmia that is the biggest and the hyper saline lake in Iran. The evaluated lake levels are found to be in good correlation with the observed values. The results of SVM simulation show better accuracy and implementation. The mean square errors, mean absolute relative errors and determination coefficient statistics are used as comparison criteria.

Keywords: simulation, water level fluctuation, urmia lake, support vector machine

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Authors: Alireza Rasekh, Peter Sergeant, Jan Vierendeels

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This paper copes with the numerical simulation for convective heat transfer in the stator disk of an axial flux permanent magnet (AFPM) electrical machine. Overheating is one of the main issues in the design of AFMPs, which mainly occurs in the stator disk, so that it needs to be prevented. A rotor-stator configuration with 16 magnets at the periphery of the rotor is considered. Air is allowed to flow through openings in the rotor disk and channels being formed between the magnets and in the gap region between the magnets and the stator surface. The rotating channels between the magnets act as a driving force for the air flow. The significant non-dimensional parameters are the rotational Reynolds number, the gap size ratio, the magnet thickness ratio, and the magnet angle ratio. The goal is to find correlations for the Nusselt number on the stator disk according to these non-dimensional numbers. Therefore, CFD simulations have been performed with the multiple reference frame (MRF) technique to model the rotary motion of the rotor and the flow around and inside the machine. A minimization method is introduced by a pattern-search algorithm to find the appropriate values of the reference temperature. It is found that the correlations are fast, robust and is capable of predicting the stator heat transfer with a good accuracy. The results reveal that the magnet angle ratio diminishes the stator heat transfer, whereas the rotational Reynolds number and the magnet thickness ratio improve the convective heat transfer. On the other hand, there a certain gap size ratio at which the stator heat transfer reaches a maximum.

Keywords: AFPM, CFD, magnet parameters, stator heat transfer

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Authors: Halimatu S. Abdullahi, Ray E. Sheriff, Fatima Mahieddine

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Agriculture is essential to the continuous existence of human life as they directly depend on it for the production of food. The exponential rise in population calls for a rapid increase in food with the application of technology to reduce the laborious work and maximize production. Technology can aid/improve agriculture in several ways through pre-planning and post-harvest by the use of computer vision technology through image processing to determine the soil nutrient composition, right amount, right time, right place application of farm input resources like fertilizers, herbicides, water, weed detection, early detection of pest and diseases etc. This is precision agriculture which is thought to be solution required to achieve our goals. There has been significant improvement in the area of image processing and data processing which has being a major challenge. A database of images is collected through remote sensing, analyzed and a model is developed to determine the right treatment plans for different crop types and different regions. Features of images from vegetations need to be extracted, classified, segmented and finally fed into the model. Different techniques have been applied to the processes from the use of neural network, support vector machine, fuzzy logic approach and recently, the most effective approach generating excellent results using the deep learning approach of convolution neural network for image classifications. Deep Convolution neural network is used to determine soil nutrients required in a plantation for maximum production. The experimental results on the developed model yielded results with an average accuracy of 99.58%.

Keywords: convolution, feature extraction, image analysis, validation, precision agriculture

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Authors: Lizhi Ma, Chengcheng Hu, Fuxian Wong

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Partial reduce (P-Reduce) has set a state-of-the-art performance on distributed machine learning in the heterogeneous environment over the All-Reduce architecture. The dynamic P-Reduce based on the exponential moving average (EMA) approach predicts all the intermediate model parameters, which raises unreliability. It is noticed that the approximation trick leads the wrong way to obtaining model parameters in all the nodes. In this paper, SAP-Reduce is proposed, which is a variant of the All-Reduce distributed training model with staleness-aware dynamic P-Reduce. SAP-Reduce directly utilizes the EMA-like algorithm to generate the normalized weights. To demonstrate the effectiveness of the algorithm, the experiments are set based on a number of deep learning models, comparing the single-step training acceleration ratio and convergence time. It is found that SAP-Reduce simplifying dynamic P-Reduce outperforms the intermediate approximation one. The empirical results show SAP-Reduce is 1.3× −2.1× faster than existing baselines.

Keywords: collective communication, decentralized distributed training, machine learning, P-Reduce

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Authors: Yahia. Kourd, N. Guersi D. Lefebvre

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In this paper we propose to study the faults diagnosis in squirrel-cage induction motor using MLP neural networks. We use neural healthy and faulty models of the behavior in order to detect and isolate some faults in machine. In the first part of this work, we have created a neural model for the healthy state using Matlab and a motor located in LGEB by acquirins data inputs and outputs of this engine. Then we detected the faults in the machine by residual generation. These residuals are not sufficient to isolate the existing faults. For this reason, we proposed additive neural networks to represent the faulty behaviors. From the analysis of these residuals and the choice of a threshold we propose a method capable of performing the detection and diagnosis of some faults in asynchronous machines with squirrel cage rotor.

Keywords: faults diagnosis, neural networks, multi-models, squirrel-cage induction motor

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Authors: Forouzan Salehi Fergeni

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Converting the movement intents of a person into commands for action employing brain signals like electroencephalogram signals is a brain-computer interface (BCI) system. When left or right-hand motions are imagined, different patterns of brain activity appear, which can be employed as BCI signals for control. To make better the brain-computer interface (BCI) structures, effective and accurate techniques for increasing the classifying precision of motor imagery (MI) based on electroencephalography (EEG) are greatly needed. Subject dependency and non-stationary are two features of EEG signals. So, EEG signals must be effectively processed before being used in BCI applications. In the present study, after applying an 8 to 30 band-pass filter, a car spatial filter is rendered for the purpose of denoising, and then, a method of analysis of variance is used to select more appropriate and informative channels from a category of a large number of different channels. After ordering channels based on their efficiencies, a sequential forward channel selection is employed to choose just a few reliable ones. Features from two domains of time and wavelet are extracted and shortlisted with the help of a statistical technique, namely the t-test. Finally, the selected features are classified with different machine learning and neural network classifiers being k-nearest neighbor, Probabilistic neural network, support-vector-machine, Extreme learning machine, decision tree, Multi-layer perceptron, and linear discriminant analysis with the purpose of comparing their performance in this application. Utilizing a ten-fold cross-validation approach, tests are performed on a motor imagery dataset found in the BCI competition III. Outcomes demonstrated that the SVM classifier got the greatest classification precision of 97% when compared to the other available approaches. The entire investigative findings confirm that the suggested framework is reliable and computationally effective for the construction of BCI systems and surpasses the existing methods.

Keywords: brain-computer interface, channel selection, motor imagery, support-vector-machine

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Authors: Abdel Hadi N. Ebraheim, Eman Azomi, Nefisa A. Fahmy

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This paper designs and implements a computer-aided system (CAS) to help detect and diagnose liver cirrhosis in patients with Chronic Hepatitis C. Our system reduces the required features (tests) the patient is asked to do to tests to their minimal best most informative subset of tests, with a diagnostic accuracy above 99%, and hence saving both time and costs. We use the Support Vector Machine (SVM) with cross-validation, a Multilayer Perceptron Neural Network (MLP), and a Generalized Regression Neural Network (GRNN) that employs a base of radial functions for functional approximation, as classifiers. Our system is tested on 199 subjects, of them 99 Chronic Hepatitis C.The subjects were selected from among the outpatient clinic in National Herpetology and Tropical Medicine Research Institute (NHTMRI).

Keywords: liver cirrhosis, artificial neural network, support vector machine, multi-layer perceptron, classification, accuracy

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Authors: Annapurna Basavaraju, Andreas Marn, Franz Heitmeir

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The Advisory Council for Aeronautics Research in Europe (ACARE) is cognizant for the overall reduction of NOx emissions by 80% in its vision 2020. Moreover small turbo engines have higher fuel specific emissions compared to large engines due to their limited combustion chamber size. In order to fulfill these requirements, novel combustion concepts are essential. This motivates to carry out the research on the current state of art, catalytic stabilized combustion chamber using hydrogen in small jet engines which are designed and investigated both numerically and experimentally during this project. Catalytic combustion concepts can also be adopted for low caloric fuels and are therefore not constrained to only hydrogen. However, hydrogen has high heating value and has the major advantage of producing only the nitrogen oxides as pollutants during the combustion, thus eliminating the interest on other emissions such as Carbon monoxides etc. In the present work, the combustion chamber is designed based on the ‘Rich catalytic Lean burn’ concept. The experiments are conducted for the characteristic operating range of an existing engine. This engine has been tested successfully at Institute of Thermal Turbomachinery and Machine Dynamics (ITTM), Technical University Graz. One of the facts that the efficient combustion is a result of proper mixing of fuel-air mixture, considerable significance is given to the selection of appropriate mixer. This led to the design of three diverse configurations of mixers and is investigated experimentally and numerically. Subsequently the best mixer would be equipped in the main combustion chamber and used throughout the experimentation. Furthermore, temperatures and pressures would be recorded at various locations inside the combustion chamber and the exhaust emissions will also be analyzed. The instrumented combustion chamber would be inspected at the engine relevant inlet conditions for nine different sets of catalysts at the Hot Flow Test Facility (HFTF) of the institute.

Keywords: catalytic combustion, gas turbine, hydrogen, mixer, NOx emissions

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Authors: Doseong Eom, Jeongmin Kim, Kwang Ryel Ryu

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Large exhibition halls require a lot of energy to maintain comfortable atmosphere for the visitors viewing inside. One way of reducing the energy cost is to have thermal energy storage systems installed so that the thermal energy can be stored in the middle of night when the energy price is low and then used later when the price is high. To minimize the overall energy cost, however, we should be able to decide how much energy to save during which time period exactly. If we can foresee future energy load and the corresponding cost, we will be able to make such decisions reasonably. In this paper, we use machine learning technique to obtain models for predicting weather conditions and the number of visitors on hourly basis for the next day. Based on the energy load thus predicted, we build a cost-optimal daily operation plan for the thermal energy storage systems and cooling and heating facilities through simulation-based optimization.

Keywords: building energy management, machine learning, operation planning, simulation-based optimization

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Authors: Pinel Sebastien, Bourrin François, De Madron Du Rieu Xavier, Ludwig Wolfgang, Arnau Pedro

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Heavy precipitation constitutes a major meteorological threat in the western Mediterranean. Research has investigated the relationship between the states of the Mediterranean Sea and the atmosphere with the precipitation for short temporal windows. However, at a larger temporal scale, the precursor signals of heavy rainfall in the sea and atmosphere have drawn little attention. Moreover, despite ongoing improvements in numerical weather prediction, the medium-term forecasting of rainfall events remains a difficult task. Here, we aim to investigate the influence of early-spring environmental parameters on the following autumnal heavy precipitations. Hence, we develop a machine learning model to predict extreme autumnal rainfall with a 6-month lead time over the Spanish Catalan coastal area, based on i) the sea pattern (main current-LPC and Sea Surface Temperature-SST) at the mesoscale scale, ii) 4 European weather teleconnection patterns (NAO, WeMo, SCAND, MO) at synoptic scale, and iii) the hydrological regime of the main local river (Rhône River). The accuracy of the developed model classifier is evaluated via statistical analysis based on classification accuracy, logarithmic and confusion matrix by comparing with rainfall estimates from rain gauges and satellite observations (CHIRPS-2.0). Sensitivity tests are carried out by changing the model configuration, such as sea SST, sea LPC, river regime, and synoptic atmosphere configuration. The sensitivity analysis suggests a negligible influence from the hydrological regime, unlike SST, LPC, and specific teleconnection weather patterns. At last, this study illustrates how public datasets can be integrated into a machine learning model for heavy rainfall prediction and can interest local policies for management purposes.

Keywords: extreme hazards, sensitivity analysis, heavy rainfall, machine learning, sea-atmosphere modeling, precipitation forecasting

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Authors: Yang-Kuei Lin, Yun-Xi Zhang

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This research focuses on the bicriteria correlated parallel machine scheduling problem. The two objective functions considered in this problem are to minimize maximum lateness and total weighted completion time. We first present a mixed integer programming (MIP) model that can find the entire efficient frontier for the studied problem. Next, we have proposed a bicriteria heuristic that can find non-dominated solutions for the studied problem. The performance of the proposed bicriteria heuristic is compared with the efficient frontier generated by solving the MIP model. Computational results indicate that the proposed bicriteria heuristic can solve the problem efficiently and find a set of diverse solutions that are uniformly distributed along the efficient frontier.

Keywords: bicriteria, correlated parallel machines, heuristic, scheduling

Procedia PDF Downloads 138

Authors: Adamu Abdullahi, On Francisca, Saidu Isah Rambo, G. N. Obunadike, D. T. Chinyio

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The paper discusses the potential threat of Denial of Service (DoS) attacks in the Internet of Things (IoT) networks on constrained application protocols (CoAP). As billions of IoT devices are expected to be connected to the internet in the coming years, the security of these devices is vulnerable to attacks, disrupting their functioning. This research aims to tackle this issue by applying mixed methods of qualitative and quantitative for feature selection, extraction, and cluster algorithms to detect DoS attacks in the Constrained Application Protocol (CoAP) using the Machine Learning Algorithm (MLA). The main objective of the research is to enhance the security scheme for CoAP in the IoT environment by analyzing the nature of DoS attacks and identifying a new set of features for detecting them in the IoT network environment. The aim is to demonstrate the effectiveness of the MLA in detecting DoS attacks and compare it with conventional intrusion detection systems for securing the CoAP in the IoT environment. Findings: The research identifies the appropriate node to detect DoS attacks in the IoT network environment and demonstrates how to detect the attacks through the MLA. The accuracy detection in both classification and network simulation environments shows that the k-means algorithm scored the highest percentage in the training and testing of the evaluation. The network simulation platform also achieved the highest percentage of 99.93% in overall accuracy. This work reviews conventional intrusion detection systems for securing the CoAP in the IoT environment. The DoS security issues associated with the CoAP are discussed.

Keywords: algorithm, CoAP, DoS, IoT, machine learning

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Authors: Shoji Matsumoto

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Friction Stir Welding (FSW) has been widely used in the automotive, aerospace, and high-tech industries due to its superior mechanical properties after welding. However, when it becomes a matter to perform a high-quality joint using FSW, it is necessary to secure an advanced tilt angle (usually 1 to 5 degrees) using a dedicated FSW machine and to use a joint structure and a restraining jig that can withstand the tool pressure applied during the jointing process using a highly rigid processing machine. One issue that has become a challenge in this process is ‘productivity and versatility’. To solve this problem, we have conducted research and development of multi-functioning machines and robotics with FSW tools, which combine cutting/milling and FSW functions as one in recent years. However, the narrow process window makes it prone to welding defects and lacks repeatability, which makes a limitation for FSW its use in the fields where precisions required. Another reason why FSW machines are not widely used in the world is because of the matter of very high cost of ownership.

Keywords: synchronized, stir, welding, friction, traveling speed, synchronized stir welding, friction stir welding

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Authors: Albert Acheampong, Tamer Elshandidy

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We investigate the extent to which sustainability disclosure from the narrative section of European banks’ annual reports improves analyst forecast accuracy. We capture sustainability disclosure using a machine learning approach and use forecast error to proxy analyst forecast accuracy. Our results suggest that sustainability disclosure significantly improves analyst forecast accuracy by reducing the forecast error. In a further analysis, we also find that the induction of Directive 2014/95/European Union (EU) is associated with increased disclosure content, which then reduces forecast error. Collectively, our results suggest that sustainability disclosure improves forecast accuracy, and the induction of the new EU directive strengthens this improvement. These results hold after several further and robustness analyses. Our findings have implications for market participants and policymakers.

Keywords: sustainability disclosure, machine learning, analyst forecast accuracy, forecast error, European banks, EU directive

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Authors: Uswah Ahmad Khan, Muhammad Moazam Fraz, Humayoon Shafique Satti, Qasim Aziz

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Approximately 10-14% of the global population experiences a functional disorder known as irritable bowel syndrome (IBS). The disorder is defined by persistent abdominal pain and an irregular bowel pattern. IBS significantly impairs work productivity and disrupts patients' daily lives and activities. Although IBS is widespread, there is still an incomplete understanding of its underlying pathophysiology. This study aims to help characterize the phenotype of IBS patients by differentiating the comorbidities found in IBS patients from those in non-IBS patients using machine learning algorithms. In this study, we extracted samples coding for IBS from the UK BioBank cohort and randomly selected patients without a code for IBS to create a total sample size of 18,000. We selected the codes for comorbidities of these cases from 2 years before and after their IBS diagnosis and compared them to the comorbidities in the non-IBS cohort. Machine learning models, including Decision Trees, Gradient Boosting, Support Vector Machine (SVM), AdaBoost, Logistic Regression, and XGBoost, were employed to assess their accuracy in predicting IBS. The most accurate model was then chosen to identify the features associated with IBS. In our case, we used XGBoost feature importance as a feature selection method. We applied different models to the top 10% of features, which numbered 50. Gradient Boosting, Logistic Regression and XGBoost algorithms yielded a diagnosis of IBS with an optimal accuracy of 71.08%, 71.427%, and 71.53%, respectively. Among the comorbidities most closely associated with IBS included gut diseases (Haemorrhoids, diverticular diseases), atopic conditions(asthma), and psychiatric comorbidities (depressive episodes or disorder, anxiety). This finding emphasizes the need for a comprehensive approach when evaluating the phenotype of IBS, suggesting the possibility of identifying new subsets of IBS rather than relying solely on the conventional classification based on stool type. Additionally, our study demonstrates the potential of machine learning algorithms in predicting the development of IBS based on comorbidities, which may enhance diagnosis and facilitate better management of modifiable risk factors for IBS. Further research is necessary to confirm our findings and establish cause and effect. Alternative feature selection methods and even larger and more diverse datasets may lead to more accurate classification models. Despite these limitations, our findings highlight the effectiveness of Logistic Regression and XGBoost in predicting IBS diagnosis.

Keywords: comorbidities, disease association, irritable bowel syndrome (IBS), predictive analytics

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Authors: Jameela Ali, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

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Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifyig the red blood cells as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-Malaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively

Keywords: red blood cells, classification, radial basis function neural networks, suport vector machine, k-nearest neighbors algorithm

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Authors: Said Alshukri, Mazhar Hussain Malik

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Green hydrogen is hydrogen produced using renewable energy sources. In the last few years, Oman aimed to reduce its dependency on fossil fuels. Recently, the hydrogen economy has become a global trend, and many countries have started to investigate the feasibility of implementing this sector. Oman created an alliance to establish the policy and rules for this sector. With motivation coming from both global and local interest in green hydrogen, this paper investigates the potential of producing hydrogen from wind and solar energies in three different locations in Oman, namely Duqm, Salalah, and Sohar. By using machine learning-based software “WEKA” and local metrological data, the project was designed to figure out which location has the highest wind and solar energy potential. First, various supervised models were tested to obtain their prediction accuracy, and it was found that the Random Forest (RF) model has the best prediction performance. The RF model was applied to 2021 metrological data for each location, and the results indicated that Duqm has the highest wind and solar energy potential. The system of one wind turbine in Duqm can produce 8335 MWh/year, which could be utilized in the water electrolysis process to produce 88847 kg of hydrogen mass, while a solar system consisting of 2820 solar cells is estimated to produce 1666.223 MWh/ year which is capable of producing 177591 kg of hydrogen mass.

Keywords: green hydrogen, machine learning, wind and solar energies, WEKA, supervised models, random forest

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Authors: Omar Elharrouss, Youssef Hmamouche, Assia Kamal Idrissi, Btissam El Khamlichi, Amal El Fallah-Seghrouchni

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Edge detection is represented as one of the most challenging tasks in computer vision, due to the complexity of detecting the edges or boundaries in real-world images that contains objects of different types and scales like trees, building as well as various backgrounds. Edge detection is represented also as a key task for many computer vision applications. Using a set of backbones as well as attention modules, deep-learning-based methods improved the detection of edges compared with the traditional methods like Sobel and Canny. However, images of complex scenes still represent a challenge for these methods. Also, the detected edges using the existing approaches suffer from non-refined results while the image output contains many erroneous edges. To overcome this, n this paper, by using the mechanism of residual learning, a refined edge detection network is proposed (RED-Net). By maintaining the high resolution of edges during the training process, and conserving the resolution of the edge image during the network stage, we make the pooling outputs at each stage connected with the output of the previous layer. Also, after each layer, we use an affined batch normalization layer as an erosion operation for the homogeneous region in the image. The proposed methods are evaluated using the most challenging datasets including BSDS500, NYUD, and Multicue. The obtained results outperform the designed edge detection networks in terms of performance metrics and quality of output images.

Keywords: edge detection, convolutional neural networks, deep learning, scale-representation, backbone

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Authors: Mouhammd Alkasassbeh, Alaa Lasasmeh

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We live in an era of rapid development as a result of significant scientific growth. Like other technologies, wireless sensor networks (WSNs) are playing one of the main roles. Based on WSNs, ZigBee adds many features to devices, such as minimum cost and power consumption, and increasing the range and connect ability of sensor nodes. ZigBee technology has come to be used in various fields, including science, engineering, and networks, and even in medicinal aspects of intelligence building. In this work, we generated two main datasets, the first being based on tree topology and the second on star topology. The datasets were evaluated by three machine learning (ML) algorithms: J48, meta.j48 and multilayer perceptron (MLP). Each topology was classified into normal and abnormal (attack) network traffic. The dataset used in our work contained simulated data from network simulation 2 (NS2). In each database, the Bayesian network meta.j48 classifier achieved the highest accuracy level among other classifiers, of 99.7% and 99.2% respectively.

Keywords: IDS, Machine learning, WSN, ZigBee technology

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Authors: Bhaveek Maini, Sanjay Dhanka, Surita Maini

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Electroencephalography (EEG) is used to classify several epileptic seizures worldwide. It is a very crucial task for the neurologist to identify the epileptic seizure with manual EEG analysis, as it takes lots of effort and time. Human error is always at high risk in EEG, as acquiring signals needs manual intervention. Disease diagnosis using machine learning (ML) has continuously been explored since its inception. Moreover, where a large number of datasets have to be analyzed, ML is acting as a boon for doctors. In this research paper, authors proposed two different ML models, i.e., logistic regression (LR) and Naïve Bayes (NB), to predict epileptic seizures based on general parameters. These two techniques are applied to the epileptic seizures recognition dataset, available on the UCI ML repository. The algorithms are implemented on an 80:20 train test ratio (80% for training and 20% for testing), and the performance of the model was validated by 10-fold cross-validation. The proposed study has claimed accuracy of 81.87% and 95.49% for LR and NB, respectively.

Keywords: epileptic seizure recognition, logistic regression, Naïve Bayes, machine learning

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Authors: Devendra Kr Tayal, Vrinda Gupta, Aastha Bansal, Khushi Singh, Sristi Sharma, Hunny Gaur

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In today's world, suicide is a serious problem. In order to save lives, early suicide attempt detection and prevention should be addressed. A good number of at-risk people utilize social media platforms to talk about their issues or find knowledge on related chores. Twitter and Reddit are two of the most common platforms that are used for expressing oneself. Extensive research has already been done in this field. Through supervised classification techniques like Nave Bayes, Bernoulli Nave Bayes, and Multiple Layer Perceptron on a Reddit dataset, we demonstrate the early recognition of suicidal ideation. We also performed comparative analysis on these approaches and used accuracy, recall score, F1 score, and precision score for analysis.

Keywords: machine learning, suicide ideation detection, supervised classification, natural language processing

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Authors: G. Krishna Mohana Rao, P. Ravi Kumar

Abstract:

Milling is the process of removing unwanted material with suitable tool. Even though the milling process is having wider application, the vibration of machine tool and work piece during the process produces chatter on the products. Various methods of preventing the chatter have been incorporated into machine tool systems. Damper is cut into equal number of parts. Each part is called as finger. Multiple fingers were inserted in the hollow portion of the shank to reduce tool vibrations. In the present work, nonlinear static and dynamic analysis of the damper inserted end milling cutter used to reduce the chatter was done. A comparison is made for the milling cutter with multiple dampers. Surface roughness was determined by machining with multiple finger inserted milling cutters.

Keywords: damping inserts, end milling, vibrations, nonlinear dynamic analysis, number of fingers

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Authors: Saba Gachpaz, Hamid Reza Heidari

Abstract:

The sharp increase in population growth leads to more pressure on agricultural areas to satisfy the food supply. To achieve this, more resources should be consumed and, besides other environmental concerns, highlight sustainable agricultural development. Land-use management is a crucial factor in obtaining optimum productivity. Machine learning is a widely used technique in the agricultural sector, from yield prediction to customer behavior. This method focuses on learning and provides patterns and correlations from our data set. In this study, nine physical control factors, namely, soil classification, electrical conductivity, normalized difference water index (NDWI), groundwater level, elevation, annual precipitation, pH of water, annual mean temperature, and slope in the alluvial plain in Khuzestan (an agricultural hotspot in Iran) are used to decide the best agricultural land use for both rainfed and irrigated agriculture for ten different crops. For this purpose, each variable was imported into Arc GIS, and a raster layer was obtained. In the next level, by using training samples, all layers were imported into the python environment. A random forest model was applied, and the weight of each variable was specified. In the final step, results were visualized using a digital elevation model, and the importance of all factors for each one of the crops was obtained. Our results show that despite 62% of the study area being allocated to agricultural purposes, only 42.9% of these areas can be defined as a suitable class for cultivation purposes.

Keywords: land suitability, machine learning, random forest, sustainable agriculture

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Authors: Alexandre Faure, Yoko Mizokami, éRic Dinet

Abstract:

These recent years, the potential of light in assisting visually impaired people in their indoor mobility has been demonstrated by different studies. Implementing smart lighting systems for selective visual enhancement, especially designed for low-vision people, is an approach that breaks with the existing visual aids. The appearance of the surface of an object is significantly influenced by the lighting conditions and the constituent materials of the objects. Appearance of objects may appear to be different from expectation. Therefore, lighting conditions lead to an important part of accurate material recognition. The main objective of this work was to investigate the effect of the spatial distribution of light on object identification in the context of low vision. The purpose was to determine whether and what specific lighting approaches should be preferred for visually impaired people. A psychophysical experiment was designed to study the ability of individuals to identify the smallest cube of a pair under different lighting diffusion conditions. Participants were divided into two distinct groups: a reference group of observers with normal or corrected-to-normal visual acuity and a test group, in which observers were required to wear visual impairment simulation glasses. All participants were presented with pairs of cubes in a "miniature room" and were instructed to estimate the relative size of the two cubes. The miniature room replicates real-life settings, adorned with decorations and separated from external light sources by black curtains. The correlated color temperature was set to 6000 K, and the horizontal illuminance at the object level at approximately 240 lux. The objects presented for comparison consisted of 11 white cubes and 11 black cubes of different sizes manufactured with a 3D printer. Participants were seated 60 cm away from the objects. Two different levels of light diffuseness were implemented. After receiving instructions, participants were asked to judge whether the two presented cubes were the same size or if one was smaller. They provided one of five possible answers: "Left one is smaller," "Left one is smaller but unsure," "Same size," "Right one is smaller," or "Right one is smaller but unsure.". The method of constant stimuli was used, presenting stimulus pairs in a random order to prevent learning and expectation biases. Each pair consisted of a comparison stimulus and a reference cube. A psychometric function was constructed to link stimulus value with the frequency of correct detection, aiming to determine the 50% correct detection threshold. Collected data were analyzed through graphs illustrating participants' responses to stimuli, with accuracy increasing as the size difference between cubes grew. Statistical analyses, including 2-way ANOVA tests, showed that light diffuseness had no significant impact on the difference threshold, whereas object color had a significant influence in low vision scenarios. The first results and trends derived from this pilot experiment clearly and strongly suggest that future investigations could explore extreme diffusion conditions to comprehensively assess the impact of diffusion on object identification. For example, the first findings related to light diffuseness may be attributed to the range of manipulation, emphasizing the need to explore how other lighting-related factors interact with diffuseness.

Keywords: Lighting, Low Vision, Visual Aid, Object Identification, Psychophysical Experiment

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Authors: Javed Mallick

Abstract:

In metropolitan areas, rapid changes in land use and land cover (LULC) have ecological and environmental consequences. Saudi Arabia's cities have experienced tremendous urban growth since the 1990s, resulting in urban heat islands, groundwater depletion, air pollution, loss of ecosystem services, and so on. From 1990 to 2020, this study examines the variance and heterogeneity in land surface temperature (LST) caused by LULC changes in Abha-Khamis Mushyet, Saudi Arabia. LULC was mapped using the support vector machine (SVM). The mono-window algorithm was used to calculate the land surface temperature (LST). To identify LST clusters, the local indicator of spatial associations (LISA) model was applied to spatiotemporal LST maps. In addition, the parallel coordinate (PCP) method was used to investigate the relationship between LST clusters and urban biophysical variables as a proxy for LULC. According to LULC maps, urban areas increased by more than 330% between 1990 and 2018. Between 1990 and 2018, built-up areas had an 83.6% transitional probability. Furthermore, between 1990 and 2020, vegetation and agricultural land were converted into built-up areas at a rate of 17.9% and 21.8%, respectively. Uneven LULC changes in built-up areas result in more LST hotspots. LST hotspots were associated with high NDBI but not NDWI or NDVI. This study could assist policymakers in developing mitigation strategies for urban heat islands

Keywords: land use land cover mapping, land surface temperature, support vector machine, LISA model, parallel coordinate plot

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Authors: Shishir Lamichhane, Saurav Dulal, Bibek Gautam, Madan Thapa Magar, Indraman Tamrakar

Abstract:

Renewable energies such as wind turbines and solar photovoltaic have gained significance as a result of global environmental pollution and energy crises. These sources of energy are converted into electrical energy and delivered to end-users through the utility system. As a result of the widespread use of power electronics-based grid-interfacing technologies to accommodate renewable sources of energy, the prevalence of converters has expanded as well. As a result, the power system's rotating inertia is decreasing, endangering the utility grid's stability. The use of Virtual Synchronous Machine (VSM) technology has been proposed to overcome the grid stability problem due to low rotating inertia. The grid-connected inverter used in VSM can be controlled to emulate inertia, which replicates the external features of a synchronous generator. As a result, the rotating inertia is increased to support the power system's stability. A power angle control strategy is proposed in this paper and its model is simulated in MATLAB/Simulink to study the effects of parameter disturbances on the active power and frequency for a VSM. The system consists of a synchronous generator, which is modeled in such a way that the frequency drops to an unacceptable region during transient conditions due to a lack of inertia when VSM is not used. Then, the suggested model incorporating VSM emulates rotating inertia, injecting a controllable amount of energy into the grid during frequency transients to enhance transient stability.

Keywords: damping constant, inertia–constant, ROCOF, transient stability, distributed sources

Procedia PDF Downloads 203