Search results for: satellite imagery classification

Authors: Dalwinder Singh, Amandeep Verma, Manpreet Kaur, Birmohan Singh

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The detection of pre-cancerous changes using a Pap smear test of cervical cell is the important step for the early diagnosis of cervical cancer. The Pap smear test consists of a sample of human cells taken from the cervix which are analysed to detect cancerous and pre-cancerous stage of the given subject. The manual analysis of these cells is labor intensive and time consuming process which relies on expert cytotechnologist. In this paper, a computer assisted system for the automated analysis of the cervical cells has been proposed. We propose a morphology based approach to the nucleus detection and segmentation of the cytoplasmic region of the given single or multiple overlapped cell. Further, various texture and region based features are calculated from these cells to classify these into normal and abnormal cell. Experimental results on public available dataset show that our system has achieved satisfactory success rate.

Keywords: cervical cancer, cervical tissue, mathematical morphology, texture features

Procedia PDF Downloads 508

Authors: Levent Basayigit, Mert Dedeoglu, Fadime Ozogul

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In this study, an application was carried out to determine the Volcanic Soils by using remote sensing.  The study area was located on the Golcuk formation in Isparta-Turkey. The thermal bands of Landsat 7 image were used for processing. The implementation of the climate model that was based on the water index was used in ERDAS Imagine software together with pixel based image classification. Soil Moisture Index (SMI) was modeled by using the surface temperature (Ts) which was obtained from thermal bands and vegetation index (NDVI) derived from Landsat 7. Surface moisture values were grouped and classified by using scoring system. Thematic layers were compared together with the field studies. Consequently, different moisture levels for volcanic soils were indicator for determination and separation. Those thermal wavelengths are preferable bands for separation of volcanic soils using moisture and temperature models.

Keywords: Landsat 7, soil moisture index, temperature models, volcanic soils

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Authors: Wahab Ali, Oyebade K. Oyedotun, Adnan Khashman

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Breast cancer remains a threat to the woman’s world in view of survival rates, it early diagnosis and mortality statistics. So far, research has shown that many survivors of breast cancer cases are in the ones with early diagnosis. Breast cancer is usually categorized into stages which indicates its severity and corresponding survival rates for patients. Investigations show that the farther into the stages before diagnosis the lesser the chance of survival; hence the early diagnosis of breast cancer becomes imperative, and consequently the application of novel technologies to achieving this. Over the year, mammograms have used in the diagnosis of breast cancer, but the inconclusive deductions made from such scans lead to either false negative cases where cancer patients may be left untreated or false positive where unnecessary biopsies are carried out. This paper presents the application of artificial neural networks in the prediction of severity of breast tumour (whether benign or malignant) using mammography reports and other factors that are related to breast cancer.

Keywords: breast cancer, intelligent classification, neural networks, mammography

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Authors: Max Sardou, Patricia Sardou

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Now that weight saving is mandatory, to author best knowledge composite springs, that we have invented, are best choice for automotive suspensions, against steel. So, we have created a Joint Ventures called S.ARA, in order to mass produce composite coils springs. Start of Production of composite coils springs was in 2014 for AUDI. As we have demonstrated, on the road, that composite springs are not a sweet dream. The present paper describes all the benefits of ‘C’ springs and ‘S’ springs for high performance vehicles suspension, for rocket stage separation, and for satellite injection into orbit. Developing rocket stage separation, we have developed for CNES (Centre National d’Etudes Spatiales) the following concept. If we call ‘line of action’ a line going from one end of a spring to the other. Our concept is to use for instance two springs inclined. In such a way that their line of action cross together and create at this crossing point a virtual center well above the springs. This virtual center, is pulling from above the top stage and is offering a guidance, perfectly stable and straight. About buildings, our solution is to transfer this rocket technology, creating a ‘virtual center’ of pendulation positioned above the building center of gravity. This is achieved by using tilted composite springs benches oriented in such a way that their line of action converges creating the ‘virtual center’. Thanks to the ‘virtual center’ position, the building behaves as a pendulum, hanged from above. When earthquake happen then the building will oscillate around its ‘virtual center’ and will go back safely to equilibrium after the tremor. ‘C’ springs, offering anti-rust, anti-settlement, fail-safe suspension, plus virtual center solution is the must for long-lasting, perfect protection of buildings against earthquakes.

Keywords: virtual center of tilt, composite springs, fail safe springs, antiseismic suspention

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Authors: E. Ghoneim, C. Narron, I. Iqbal, I. Hassan, E. Hammam

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The scarcity of water is becoming a more prominent threat, especially in areas that are already arid in nature. Although the Kingdom of Saudi Arabia (KSA) is an arid country, its southwestern region offers a high variety of botanical landscapes, many of which are wooded forests, while the eastern and northern regions offer large areas of groundwater irrigated farmlands. At present, some parts of KSA, including forests and farmlands, have witnessed protracted and severe drought due to change in rainfall pattern as a result of global climate change. Such prolonged drought that last for several consecutive years is expected to cause deterioration of forested and pastured lands as well as cause crop failure in the KSA (e.g., wheat yield). An analysis to determine vegetation drought vulnerability and severity during the growing season (September-April) over a fourteen year period (2000-2014) in KSA was conducted using MODIS Terra imagery. The Vegetation Condition Index (VCI), derived from the Normalized Difference Vegetation Index (NDVI), and the Temperature Condition Index (TCI), derived from the Land Surface Temperature (LST) data was extracted from MODIS Terra Images. The VCI and TCI were then combined to compute the Vegetation Health Index (VHI). The VHI revealed the overall vegetation health for the area under investigation. A preliminary outcome of the modeled VHI over KSA, using averaged monthly vegetation data over a 14-year period, revealed that the vegetation health condition is deteriorating over time in both naturally vegetated areas and irrigated farmlands. The derived drought map for KSA indicates that both extreme and severe drought occurrences have considerably increased over the same study period. Moreover, based on the cumulative average of drought frequency in each governorate of KSA it was determined that Makkah and Jizan governorates to the east and southwest, witness the most frequency of extreme drought, whereas Tabuk to the northwest, exhibits the less extreme drought frequency. Areas where drought is extreme or severe would most likely have negative influences on agriculture, ecosystems, tourism, and even human welfare. With the drought risk map the kingdom could make informed land management decisions including were to continue with agricultural endeavors and protect forested areas and even where to develop new settlements.

Keywords: drought, vegetation health condition, TCI, Saudi Arabia

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Authors: Mayowa Adeyeye, Kehinde Adeyemo

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The direct free kick is considered a great opportunity to score a goal but this is not always the case amidst Nigerian and other elite footballers. This study, therefore, examined the extent to which an 8 weeks mental skill training programme is effective for improving accuracy in direct free kick in football. Sixty (n-60) students of Pepsi Football Academy participated in the study. They were randomly distributed into two groups of positive self-talk group (intervention n-30) and control group (n-30). The instrument used in the collection of data include a standard football goal post while the research materials include a dummy soccer wall, a cord, an improvised vanishing spray, a clipboard, writing materials, a recording sheet, a self-talk log book, six standard 5 football, cones, an audiotape and a compact disc. The Weinberge and Gould (2011) mental skills training manual was used. The reliability coefficient of the apparatus following a pilot study stood at 0.72. Before the commencement of the mental skills training programme, the participants were asked to take six simulated direct free kick. At the end of each physical skills training session after the pre-test, the researcher spent at least 15 minutes with the groups exposing them to the intervention. The mental skills training programme alongside physical skills training took place in two different locations for the different groups under study, these included Agege Stadium Main bowl Football Pitch (Imagery Group), and Ogba Ijaye (Control Group). The mental skills training programme lasted for eight weeks. After the completion of the mental skills training programme, all the participants were asked to take another six simulated direct free kick attempts using the same field used for the pre-test to determine the efficacy of the treatments. The pre-test and post-test data were analysed using inferential statistics of t-test, while the alpha level was set at 0.05. The result revealed significant differences in t-test for positive self-talk and control group. Based on the findings, it is recommended that athletes should be exposed to positive self-talk alongside their normal physical skills training for quality delivery of accurate direct free kick during training and competition.

Keywords: accuracy, direct free kick, pepsi football academy, positive self-talk

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Authors: Aleksandra Jonas, Aleksandra Gronowska, Maciej Ścigacz, Szymon Jadczak

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Exact Data is a smart tool which helps with meaningful marketing content creation. It helps marketers achieve this by analyzing the text of an advertisement before and after its publication on social media sites like Facebook or Instagram. In our research we focus on four areas of natural language processing (NLP): grammar correction, sentiment analysis, irony detection and advertisement interpretation. Our research has identified a considerable lack of NLP tools for the Polish language, which specifically aid online marketers. In light of this, our research team has set out to create a robust and versatile NLP tool for the Polish language. The primary objective of our research is to develop a tool that can perform a range of language processing tasks in this language, such as sentiment analysis, text classification, text correction and text interpretation. Our team has been working diligently to create a tool that is accurate, reliable, and adaptable to the specific linguistic features of Polish, and that can provide valuable insights for a wide range of marketers needs. In addition to the Polish language version, we are also developing an English version of the tool, which will enable us to expand the reach and impact of our research to a wider audience. Another area of focus in our research involves tackling the challenge of the limited availability of linguistically diverse corpora for non-English languages, which presents a significant barrier in the development of NLP applications. One approach we have been pursuing is the translation of existing English corpora, which would enable us to use the wealth of linguistic resources available in English for other languages. Furthermore, we are looking into other methods, such as gathering language samples from social media platforms. By analyzing the language used in social media posts, we can collect a wide range of data that reflects the unique linguistic characteristics of specific regions and communities, which can then be used to enhance the accuracy and performance of NLP algorithms for non-English languages. In doing so, we hope to broaden the scope and capabilities of NLP applications. Our research focuses on several key NLP techniques including sentiment analysis, text classification, text interpretation and text correction. To ensure that we can achieve the best possible performance for these techniques, we are evaluating and comparing different approaches and strategies for implementing them. We are exploring a range of different methods, including transformers and convolutional neural networks (CNNs), to determine which ones are most effective for different types of NLP tasks. By analyzing the strengths and weaknesses of each approach, we can identify the most effective techniques for specific use cases, and further enhance the performance of our tool. Our research aims to create a tool, which can provide a comprehensive analysis of advertising effectiveness, allowing marketers to identify areas for improvement and optimize their advertising strategies. The results of this study suggest that a smart tool for advertisement analysis can provide valuable insights for businesses seeking to create effective advertising campaigns.

Keywords: NLP, AI, IT, language, marketing, analysis

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Authors: David Ming Liu, Benjamin Hirsch, Bashir Aden

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Syndromic surveillance is to detect or predict disease outbreaks through the analysis of medical sources of data. Using social media data like tweets to do syndromic surveillance becomes more and more popular with the aid of open platform to collect data and the advantage of microblogging text and mobile geographic location features. In this paper, a Syndromic Surveillance Framework is presented with machine learning kernel using tweets data analytics. Influenza and the three cities Abu Dhabi, Al Ain and Dubai of United Arabic Emirates are used as the test disease and trial areas. Hospital cases data provided by the Health Authority of Abu Dhabi (HAAD) are used for the correlation purpose. In our model, Latent Dirichlet allocation (LDA) engine is adapted to do supervised learning classification and N-Fold cross validation confusion matrix are given as the simulation results with overall system recall 85.595% performance achieved.

Keywords: Syndromic surveillance, Tweets, Machine Learning, data mining, Latent Dirichlet allocation (LDA), Influenza

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Authors: Haojie Wang, Xun Li, Rui Yin

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In order to solve the memorization overfitting in the model-agnostic meta-learning MAML algorithm, a method of generating mutually exclusive tasks based on data augmentation is proposed. This method generates a mutex task by corresponding one feature of the data to multiple labels so that the generated mutex task is inconsistent with the data distribution in the initial dataset. Because generating mutex tasks for all data will produce a large number of invalid data and, in the worst case, lead to an exponential growth of computation, this paper also proposes a key data extraction method that only extract part of the data to generate the mutex task. The experiments show that the method of generating mutually exclusive tasks can effectively solve the memorization overfitting in the meta-learning MAML algorithm.

Keywords: mutex task generation, data augmentation, meta-learning, text classification.

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Authors: Yao-Hong Tsai

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This paper presents a weighted approach to unconstrained iris recognition. Nowadays, commercial systems are usually characterized by strong acquisition constraints based on the subject’s cooperation. However, it is not always achievable for real scenarios in our daily life. Researchers have been focused on reducing these constraints and maintaining the performance of the system by new techniques at the same time. With large variation in the environment, there are two main improvements to develop the proposed iris recognition system. For solving extremely uneven lighting condition, statistic based illumination normalization is first used on eye region to increase the accuracy of iris feature. The detection of the iris image is based on Adaboost algorithm. Secondly, the weighted approach is designed by Gaussian functions according to the distance to the center of the iris. Furthermore, local binary pattern (LBP) histogram is then applied to texture classification with the weight. Experiment showed that the proposed system provided users a more flexible and feasible way to interact with the verification system through iris recognition.

Keywords: authentication, iris recognition, adaboost, local binary pattern

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Authors: Omar Nouri, Wafa Mnejja, Fatma Dhouib, Syrine Zouari, Wicem Siala, Ilhem Charfeddine, Afef Khanfir, Leila Farhat, Nejla Fourati, Jamel Daoud

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Purpose and Objective: Intensity modulated radiation (IMRT) technique, associated with induction chemotherapy (IC) and/or concomitant chemotherapy (CC), is actually the recommended treatment modality for nasopharyngeal carcinomas (NPC). The aim of this study was to evaluate the therapeutic results and the patterns of relapse with this treatment protocol. Material and methods: A retrospective monocentric study of 145 patients with NPC treated between June 2016 and July 2021. All patients received IMRT with integrated simultaneous boost (SIB) of 33 daily fractions at a dose of 69.96 Gy for high-risk volume, 60 Gy for intermediate risk volume and 54 Gy for low-risk volume. The high-risk volume dose was 66.5 Gy in children. Survival analysis was performed according to the Kaplan-Meier method, and the Log-rank test was used to compare factors that may influence survival. Results: Median age was 48 years (11-80) with a sex ratio of 2.9. One hundred-twenty tumors (82.7%) were classified as stages III-IV according to the 2017 UICC TNM classification. Ten patients (6.9%) were metastatic at diagnosis. One hundred-thirty-five patient (93.1%) received IC, 104 of which (77%) were TPF-based (taxanes, cisplatin and 5 fluoro-uracil). One hundred-thirty-eight patient (95.2%) received CC, mostly cisplatin in 134 cases (97%). After a median follow-up of 50 months [22-82], 46 patients (31.7%) had a relapse: 12 (8.2%) experienced local and/or regional relapse after a median of 18 months [6-43], 29 (20%) experienced distant relapse after a median of 9 months [2-24] and 5 patients (3.4%) had both. Thirty-five patients (24.1%) died, including 5 (3.4%) from a cause other than their cancer. Three-year overall survival (OS), cancer specific survival, disease free survival, metastasis free survival and loco-regional free survival were respectively 78.1%, 81.3%, 67.8%, 74.5% and 88.1%. Anatomo-clinic factors predicting OS were age > 50 years (88.7 vs. 70.5%; p=0.004), diabetes history (81.2 vs. 66.7%; p=0.027), UICC N classification (100 vs. 95 vs. 77.5 vs. 68.8% respectively for N0, N1, N2 and N3; p=0.008), the practice of a lymph node biopsy (84.2 vs. 57%; p=0.05), and UICC TNM stages III-IV (93.8 vs. 73.6% respectively for stage I-II vs. III-IV; p=0.044). Therapeutic factors predicting OS were a number of CC courses (less than 4 courses: 65.8 vs. 86%; p=0.03, less than 5 courses: 71.5 vs. 89%; p=0.041), a weight loss > 10% during treatment (84.1 vs. 60.9%; p=0.021) and a total cumulative cisplatin dose, including IC and CC, < 380 mg/m² (64.4 vs. 87.6%; p=0.003). Radiotherapy delay and total duration did not significantly affect OS. No grade 3-4 late side effects were noted in the evaluable 127 patients (87.6%). The most common toxicity was dry mouth which was grade 2 in 47 cases (37%) and grade 1 in 55 cases (43.3%).Conclusion: IMRT for nasopharyngeal carcinoma granted a high loco-regional control rate for patients during the last five years. However, distant relapses remain frequent and conditionate the prognosis. We identified many anatomo-clinic and therapeutic prognosis factors. Therefore, high-risk patients require a more aggressive therapeutic approach, such as radiotherapy dose escalation or adding adjuvant chemotherapy.

Keywords: therapeutic results, prognostic factors, intensity-modulated radiotherapy, nasopharyngeal carcinoma

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Authors: Revalin Herdianto

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Recent climate changes increase uncertainties in vegetation conditions such as health and biomass globally and locally. The detection is, however, difficult due to the spatial and temporal scale of vegetation coverage. Due to unique vegetation response to its environmental conditions such as water availability, the interplay between vegetation dynamics and hydrologic conditions leave a signature in their feedback relationship. Vegetation indices (VI) depict vegetation biomass and photosynthetic capacity that indicate vegetation dynamics as a response to variables including hydrologic conditions and microclimate factors such as rainfall characteristics and land surface temperature (LST). It is hypothesized that the signature may be depicted by VI in its relationship with other variables. To study this signature, several catchments in Asia, Australia, and Indonesia were analysed to assess the variations in hydrologic characteristics with vegetation types. Methods used in this study includes geographic identification and pixel marking for studied catchments, analysing time series of VI and LST of the marked pixels, smoothing technique using Savitzky-Golay filter, which is effective for large area and extensive data. Time series of VI, LST, and rainfall from satellite and ground stations coupled with digital elevation models were analysed and presented. This study found that the hydrologic response of vegetation to rainfall variations may be shown in one hydrologic year, in which a drought event can be detected a year later as a suppressed growth. However, an annual rainfall of above average do not promote growth above average as shown by VI. This technique is found to be a robust and tractable approach for assessing catchment dynamics in changing climates.

Keywords: vegetation indices, land surface temperature, vegetation dynamics, catchment

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Authors: Sheldon Liu, Gordon Wang, Lei Liu, Xuefeng Liu

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Anomaly detection, also referred to as one-class classification, plays a crucial role in identifying product images that deviate from the expected distribution. This study introduces Data-centric Anomaly Detection with Diffusion Models (DCADDM), presenting a systematic strategy for data collection and further diversifying the data with image generation via diffusion models. The algorithm addresses data collection challenges in real-world scenarios and points toward data augmentation with the integration of generative AI capabilities. The paper explores the generation of normal images using diffusion models. The experiments demonstrate that with 30% of the original normal image size, modeling in an unsupervised setting with state-of-the-art approaches can achieve equivalent performances. With the addition of generated images via diffusion models (10% equivalence of the original dataset size), the proposed algorithm achieves better or equivalent anomaly localization performance.

Keywords: diffusion models, anomaly detection, data-centric, generative AI

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Authors: H. J. Wattimanela, U. S. Passaribu, N. T. Puspito, S. W. Indratno

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Banda Sea collision zone (BSCZ) of is the result of the interaction and convergence of Indo-Australian plate, Eurasian plate and Pacific plate. This location in the eastern part of Indonesia. This zone has a very high seismic activity. In this research, we will be calculated rate (λ) and Mean Square Eror (MSE). By this result, we will identification of Poisson distribution of earthquakes in the BSCZ with the point process approach. Chi-square test approach and test Anscombe made in the process of identifying a Poisson distribution in the partition area. The data used are earthquakes with Magnitude ≥ 6 SR and its period 1964-2013 and sourced from BMKG Jakarta. This research is expected to contribute to the Moluccas Province and surrounding local governments in performing spatial plan document related to disaster management.

Keywords: molluca banda sea collision zone, earthquakes, mean square error, poisson distribution, chi-square test, anscombe test

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Authors: Joy Aworookoroh

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Writing is one of the active skills in language learning. Students of English as a foreign language are expected to write efficiently and proficiently in the language; however, there are usually challenges to optimal performance and competence in writing. Errors, on the other hand, in a foreign language learning situation are more positive than negative as they provide the basis for solving the limitations of the students. This paper investigates the situation in the Department of English, Taraba State University Jalingo. Students are administered a descriptive writing test across different levels of study. The target students are multilingual with an L1 of either Kuteb, Hausa or Junkun languages. The essays are accessed to identify the different kinds of errors in them alongside the classification of the order. Errors of correctness, clarity, engagement, and delivery were identified. However, the study identified that the degree of errors reduces alongside the experience and exposure of the students to an EFL classroom.

Keywords: errors, writings, descriptive essay, multilingual

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Authors: Donya Ashtiani Haghighi, Amirali Baniasadi

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Coronavirus (COVID-19) disease has spread abruptly all over the world since the end of 2019. Computed tomography (CT) scans and X-ray images are used to detect this disease. Different Deep Neural Network (DNN)-based diagnosis solutions have been developed, mainly based on Convolutional Neural Networks (CNNs), to accelerate the identification of COVID-19 cases. However, CNNs lose important information in intermediate layers and require large datasets. In this paper, Capsule Network (CapsNet) is used. Capsule Network performs better than CNNs for small datasets. Accuracy of 0.9885, f1-score of 0.9883, precision of 0.9859, recall of 0.9908, and Area Under the Curve (AUC) of 0.9948 are achieved on the Capsule-based framework with hyperparameter tuning. Moreover, different dropout rates are investigated to decrease overfitting. Accordingly, a dropout rate of 0.1 shows the best results. Finally, we remove one convolution layer and decrease the number of trainable parameters to 146,752, which is a promising result.

Keywords: capsule network, dropout, hyperparameter tuning, classification

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Authors: Inigo Beckett

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In forensic investigations, it is often the case that the most potentially useful recorded evidence derives from coincidental imagery, recorded immediately before or during an incident, and that during the incident (e.g. a ‘failure’ or fire event), the evidence is changed or destroyed. To an image analysis expert involved in photogrammetric analysis for Civil or Criminal Proceedings, traditional computer vision methods involving calibrated cameras is often not appropriate because image metadata cannot be relied upon. This paper presents an approach for resolving this problem, considering in particular and by way of a case study, the angle of a simple ladder shown in a photograph. The UK Health and Safety Executive (HSE) guidance document published in 2014 (INDG455) advises that a leaning ladder should be erected at 75 degrees to the horizontal axis. Personal injury cases can arise in the construction industry because a ladder is too steep or too shallow. Ad-hoc photographs of such ladders in their incident position provide a basis for analysis of their angle. This paper presents a direct approach for ascertaining the position of the camera and the angle of the ladder simultaneously from the photograph(s) by way of a workflow that encompasses a novel application of projective geometry and matrix analysis. Mathematical analysis shows that for a given pixel ratio of directly measured collinear points (i.e. features that lie on the same line segment) from the 2D digital photograph with respect to a given viewing point, we can constrain the 3D camera position to a surface of a sphere in the scene. Depending on what we know about the ladder, we can enforce another independent constraint on the possible camera positions which enables us to constrain the possible positions even further. Experiments were conducted using synthetic and real-world data. The synthetic data modeled a vertical plane with a ladder on a horizontally flat plane resting against a vertical wall. The real-world data was captured using an Apple iPhone 13 Pro and 3D laser scan survey data whereby a ladder was placed in a known location and angle to the vertical axis. For each case, we calculated camera positions and the ladder angles using this method and cross-compared them against their respective ‘true’ values.

Keywords: image analysis, projective geometry, homography, photogrammetry, ladders, Forensics, Mathematical modeling, planar geometry, matrix analysis, collinear, cameras, photographs

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Authors: Yao-Hong Tsai

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This paper presents an efficient fusion algorithm for iris images to generate stable feature for recognition in unconstrained environment. Recently, iris recognition systems are focused on real scenarios in our daily life without the subject’s cooperation. Under large variation in the environment, the objective of this paper is to combine information from multiple images of the same iris. The result of image fusion is a new image which is more stable for further iris recognition than each original noise iris image. A wavelet-based approach for multi-resolution image fusion is applied in the fusion process. The detection of the iris image is based on Adaboost algorithm and then local binary pattern (LBP) histogram is then applied to texture classification with the weighting scheme. Experiment showed that the generated features from the proposed fusion algorithm can improve the performance for verification system through iris recognition.

Keywords: image fusion, iris recognition, local binary pattern, wavelet

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Authors: Sadia Nasrin Tisha, Mushfika Sharmin Rahman, Javier Orduz

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Machine learning is applied in a variety of fields throughout the world. The healthcare sector has benefited enormously from it. One of the most effective approaches for predicting human heart diseases is to use machine learning applications to classify data and predict the outcome as a classification. However, with the rapid advancement of quantum technology, quantum computing has emerged as a potential game-changer for many applications. Quantum algorithms have the potential to execute substantially faster than their classical equivalents, which can lead to significant improvements in computational performance and efficiency. In this study, we applied quantum machine learning concepts to predict coronary heart diseases from text data. We experimented thrice with three different features; and three feature sets. The data set consisted of 100 data points. We pursue to do a comparative analysis of the two approaches, highlighting the potential benefits of quantum machine learning for predicting heart diseases.

Keywords: quantum machine learning, SVM, QSVM, matrix product state

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Authors: Mladen Dragičević, Mirjana Pejić Bach, Vanja Šimičević

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The purpose of this paper is to develop models that would enable predicting student success. These models could improve allocation of students among colleges and optimize the newly introduced model of government subsidies for higher education. For the purpose of collecting data, an anonymous survey was carried out in the last year of undergraduate degree student population using random sampling method. Decision trees were created of which two have been chosen that were most successful in predicting student success based on two criteria: Grade Point Average (GPA) and time that a student needs to finish the undergraduate program (time-to-degree). Decision trees have been shown as a good method of classification student success and they could be even more improved by increasing survey sample and developing specialized decision trees for each type of college. These types of methods have a big potential for use in decision support systems.

Keywords: data mining, knowledge discovery in databases, prediction models, student success

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Authors: Sumaira Hafeez, Saira Akram

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Albeit the populace in metropolitan regions on the planet develops each year, the urban communities battling to adapt to the expanded metropolitan movement grow at different rates. Land Surface Temperature and other atmospheric parameters of the area of not really settled using Landsat pictures more than 10 years isolated. The LULC types were moreover arranged using managed gathering techniques. Quick urbanization is changing the current examples of Land Use Land Cover (LULC) all around the world, which is thusly expanding the Land Surface Temperature (LST) other atmospheric parameters in numerous districts. Present review was centered around assessing the current and recreating the future LULC and Land Surface Temperature patterns in the elevated climate of lower Himalayan district of Pakistan. Past examples of LULC and Land Surface Temperature were distinguished through the multi-unearthly Landsat satellite pictures during the 1995–2019 information period. The future forecasts were made for the year 2030 to work out LULC and LST changes separately, utilizing their previous examples. The review presumes that the reliably extending encroachment of the city's as of late advanced provincial regions over the totally open have went with an overall warming of the district's typical. Meteorological parameters over the earlier ten years and that permitting the land to lie void for a significant long time resulting to clearing the country fields for future metropolitan improvement is a preparation that has lamentable natural effects.

Keywords: surface urban heat island, land surface temperature, urban climate change, spatial analysis of meterological and atmospheric science

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Authors: C. Rana, S. K. Jain

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The enormous amount of Web data has challenged its usage in efficient manner in the past few years. As such, a range of techniques are applied to tackle this problem; prominent among them is personalization and recommender system. In fact, these are the tools that assist user in finding relevant information of web. Most of the e-commerce websites are applying such tools in one way or the other. In the past decade, a large number of recommendation algorithms have been proposed to tackle such problems. However, there have not been much research in the evaluation criteria for these algorithms. As such, the traditional accuracy and classification metrics are still used for the evaluation purpose that provides a static view. This paper studies how the evolution of user preference over a period of time can be mapped in a recommender system using a new evaluation methodology that explicitly using time dimension. We have also presented different types of experimental set up that are generally used for recommender system evaluation. Furthermore, an overview of major accuracy metrics and metrics that go beyond the scope of accuracy as researched in the past few years is also discussed in detail.

Keywords: collaborative filtering, data mining, evolutionary, clustering, algorithm, recommender systems

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Authors: Navya Saira George, Patroba Achola Odera

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This study aims to determine spatio-temporal expansion of Kochi City, situated on the west coast of Kerala State in India. Remote sensing and GIS techniques have been used to determine land use/cover and urban expansion of the City. Classification of Landsat images of the years 1973, 1988, 2002 and 2018 have been used to reproduce a visual story of the growth of the City over a period of 45 years. Accuracy range of 0.79 ~ 0.86 is achieved with kappa coefficient range of 0.69 ~ 0.80. Results show that the areas covered by vegetation and water bodies decreased progressively from 53.0 ~ 30.1% and 34.1 ~ 26.2% respectively, while built-up areas increased steadily from 12.5 to 42.2% over the entire study period (1973 ~ 2018). The shift in land use from agriculture to non-agriculture may be attributed to the land reforms since 1980s.

Keywords: Geographical Information Systems, Kochi City, Land use/cover, Remote Sensing, Urban Sprawl

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Authors: Ehsan Mehryaar, Seyed Armin Motahari Tabari

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Without a doubt, soil shear strength is the most important property of the soil. The majority of fatal and catastrophic geological accidents are related to shear strength failure of the soil. Therefore, its prediction is a matter of high importance. However, acquiring the shear strength is usually a cumbersome task that might need complicated laboratory testing. Therefore, prediction of it based on common and easy to get soil properties can simplify the projects substantially. In this paper, A hybrid model based on the classification and regression tree algorithm and logistic regression is proposed where each leaf of the tree is an independent regression model. A database of 189 points for clay soil, including Moisture content, liquid limit, plastic limit, clay content, and shear strength, is collected. The performance of the developed model compared to the existing models and equations using root mean squared error and coefficient of correlation.

Keywords: model tree, CART, logistic regression, soil shear strength

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Authors: Ting Lu, Ya-Ru Diao, Fei Fan, Ye Xue, Lei Shi, Xian-e Tang, Meng-jun Zhan, Zhen-hua Deng

Abstract:

Accurate adult age estimation (AAE) is a significant and challenging task in forensic and archeology fields. Attempts have been made to explore optimal adult age metrics, and the rib is considered a potential age marker. The traditional way is to extract age-related features designed by experts from macroscopic or radiological images followed by classification or regression analysis. Those results still have not met the high-level requirements for practice, and the limitation of using feature design and manual extraction methods is loss of information since the features are likely not designed explicitly for extracting information relevant to age. Deep learning (DL) has recently garnered much interest in imaging learning and computer vision. It enables learning features that are important without a prior bias or hypothesis and could be supportive of AAE. This study aimed to develop DL models for AAE based on CT images and compare their performance to the manual visual scoring method. Chest CT data were reconstructed using volume rendering (VR). Retrospective data of 2500 patients aged 20.00-69.99 years were obtained between December 2019 and September 2021. Five-fold cross-validation was performed, and datasets were randomly split into training and validation sets in a 4:1 ratio for each fold. Before feeding the inputs into networks, all images were augmented with random rotation and vertical flip, normalized, and resized to 224×224 pixels. ResNeXt was chosen as the DL baseline due to its advantages of higher efficiency and accuracy in image classification. Mean absolute error (MAE) was the primary parameter. Independent data from 100 patients acquired between March and April 2022 were used as a test set. The manual method completely followed the prior study, which reported the lowest MAEs (5.31 in males and 6.72 in females) among similar studies. CT data and VR images were used. The radiation density of the first costal cartilage was recorded using CT data on the workstation. The osseous and calcified projections of the 1 to 7 costal cartilages were scored based on VR images using an eight-stage staging technique. According to the results of the prior study, the optimal models were the decision tree regression model in males and the stepwise multiple linear regression equation in females. Predicted ages of the test set were calculated separately using different models by sex. A total of 2600 patients (training and validation sets, mean age=45.19 years±14.20 [SD]; test set, mean age=46.57±9.66) were evaluated in this study. Of ResNeXt model training, MAEs were obtained with 3.95 in males and 3.65 in females. Based on the test set, DL achieved MAEs of 4.05 in males and 4.54 in females, which were far better than the MAEs of 8.90 and 6.42 respectively, for the manual method. Those results showed that the DL of the ResNeXt model outperformed the manual method in AAE based on CT reconstruction of the costal cartilage and the developed system may be a supportive tool for AAE.

Keywords: forensic anthropology, age determination by the skeleton, costal cartilage, CT, deep learning

Procedia PDF Downloads 58

Authors: Stefanie Rukavina, Sascha Gruss, Steffen Walter, Holger Hoffmann, Harald C. Traue

Abstract:

OPEN-EmoRecII is an open multimodal corpus with experimentally induced emotions. In the first half of the experiment, emotions were induced with standardized picture material and in the second half during a human-computer interaction (HCI), realized with a wizard-of-oz design. The induced emotions are based on the dimensional theory of emotions (valence, arousal and dominance). These emotional sequences - recorded with multimodal data (mimic reactions, speech, audio and physiological reactions) during a naturalistic-like HCI-environment one can improve classification methods on a multimodal level. This database is the result of an HCI-experiment, for which 30 subjects in total agreed to a publication of their data including the video material for research purposes. The now available open corpus contains sensory signal of: video, audio, physiology (SCL, respiration, BVP, EMG Corrugator supercilii, EMG Zygomaticus Major) and mimic annotations.

Keywords: open multimodal emotion corpus, annotated labels, intelligent interaction

Procedia PDF Downloads 399

Authors: Matthieu-P. Schapranow

Abstract:

Recruitment of participants for clinical trials requires the screening of a big number of potential candidates, i.e. the testing for trial-specific inclusion and exclusion criteria, which is a time-consuming and complex task. Today, a significant amount of time is spent on identification of adequate trial participants as their selection may affect the overall study results. We introduce a unique patient eligibility metric, which allows systematic ranking and classification of candidates based on trial-specific filter criteria. Our web application enables real-time analysis of patient data and assessment of candidates using freely definable inclusion and exclusion criteria. As a result, the overall time required for identifying eligible candidates is tremendously reduced whilst additional degrees of freedom for evaluating the relevance of individual candidates are introduced by our contribution.

Keywords: in-memory technology, clinical trials, screening, eligibility metric, data analysis, clustering

Procedia PDF Downloads 476

Authors: Birmohan Singh, V.K.Jain

Abstract:

Computer aided diagnosis systems provide vital opinion to radiologists in the detection of early signs of breast cancer from mammogram images. Masses and microcalcifications, architectural distortions are the major abnormalities. In this paper, a computer aided diagnosis system has been proposed for distinguishing abnormal mammograms with architectural distortion from normal mammogram. Four types of texture features GLCM texture, GLRLM texture, fractal texture and spectral texture features for the regions of suspicion are extracted. Support Vector Machine has been used as classifier in this study. The proposed system yielded an overall sensitivity of 96.47% and accuracy of 96% for the detection of abnormalities with mammogram images collected from Digital Database for Screening Mammography (DDSM) database.

Keywords: architecture distortion, mammograms, GLCM texture features, GLRLM texture features, support vector machine classifier

Procedia PDF Downloads 472

Authors: Antoine F. Dedewanou, Eric N. Ekpinda

Abstract:

We investigate whether and how deposit insurance program affects savings decisions in the Economic Community of Central African States (ECCAS). Specifically, using the World Bank’s 2014 and 2011 Global Financial Inclusion (Global Findex) databases, we apply special regressor approach. We find that the deposit insurance program increases significantly, everything else equal, the probability that people save their money at a financial institution by 11 percentage points in Gabon, by 22.2 percentage points in DR Congo and by 15.1 percentage points in Chad. These effects are matched with positive effects of age and education level. But in Cameroon, the effect of deposit insurance is not significant. The policies aimed at fostering financial inclusion will be more effective if there is a deposit insurance scheme in place, along with awareness among young people, and education programs. JEL Classification: G21, O12, O16

Keywords: deposit insurance, savings, special regressor, ECCAS countries

Procedia PDF Downloads 175

Authors: Maamar Ali Saud Al Tobi, Geraint Bevan, K. P. Ramachandran, Peter Wallace, David Harrison

Abstract:

Centrifugal pumps are complex machines which can experience different types of fault. Condition monitoring can be used in centrifugal pump fault detection through vibration analysis for mechanical and hydraulic forces. Vibration analysis methods have the potential to be combined with artificial intelligence systems where an automatic diagnostic method can be approached. An automatic fault diagnosis approach could be a good option to minimize human error and to provide a precise machine fault classification. This work aims to introduce an approach to centrifugal pump fault diagnosis based on artificial intelligence and genetic algorithm systems. An overview of the future works, research methodology and proposed experimental setup is presented and discussed. The expected results and outcomes based on the experimental work are illustrated.

Keywords: centrifugal pump setup, vibration analysis, artificial intelligence, genetic algorithm

Procedia PDF Downloads 398