Search results for: deep drawing process

Authors: Sajeeha Ansar, Asad Ali Safi, Sheikh Ziauddin, Ahmad R. Shahid, Faraz Ahsan

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Gliomas are most challenging and aggressive type of tumors which appear in different sizes, locations, and scattered boundaries. CNN is most efficient deep learning approach with outstanding capability of solving image analysis problems. A fully automatic deep learning based 2D-CNN model for brain tumor segmentation is presented in this paper. We used small convolution filters (3 x 3) to make architecture deeper. We increased convolutional layers for efficient learning of complex features from large dataset. We achieved better results by pushing convolutional layers up to 16 layers for HGG model. We achieved reliable and accurate results through fine-tuning among dataset and hyper-parameters. Pre-processing of this model includes generation of brain pipeline, intensity normalization, bias correction and data augmentation. We used the BRATS-2015, and Dice Similarity Coefficient (DSC) is used as performance measure for the evaluation of the proposed method. Our method achieved DSC score of 0.81 for complete, 0.79 for core, 0.80 for enhanced tumor regions. However, these results are comparable with methods already implemented 2D CNN architecture.

Keywords: brain tumor segmentation, convolutional neural networks, deep learning, HGG

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Authors: E. Karimi-Sibaki, A. Kharicha, M. Wu, A. Ludwig

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The standard electroslag remelting (ESR) process can ideally control the solidification of an ingot and produce homogeneous structure with minimum defects. However, the melt rate of electrode is rather low that makes the whole process uneconomical especially to produce small ingot sizes. In contrast, continuous casting is an economical process to produce small ingots such as billets at high casting speed. Unfortunately, deep liquid melt pool forms in the billet ingot of continuous casting that leads to center porosity and segregation. As such, continuous casting is not suitable to produce segregation prone alloys like tool steel or several super alloys. On the other hand, the electro slag rapid remelting (ESRR) process has advantages of both traditional ESR and continuous casting processes to produce billets. In the ESRR process, a T-shaped mold is used including a graphite ring that takes major amount of current through the mold. There are only a few reports available in the literature discussing about this topic. The research on the ESRR process is currently ongoing aiming to improve the design of the T-shaped mold, to decrease overall heat loss in the process, and to obtain a higher temperature at metal meniscus. In the present study, a 3D model is proposed to investigate the electromagnetic, thermal, and flow fields in the whole process as well as solidification of the billet ingot. We performed a fully coupled numerical simulation to explore the influence of the electromagnetically driven flow (MHD) on the thermal field in the slag and ingot. The main goal is to obtain some fundamental understanding of the formation of melt pool of the solidifying billet ingot in the ESRR process.

Keywords: billet ingot, magnetohydrodynamics (mhd), numerical simulation, remelting, solidification, t-shaped mold.

Procedia PDF Downloads 279

Authors: E. Karacabey, Ş. G. Özçelik, M. S. Turan, C. Baltacıoğlu, E. Küçüköner

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Changes in textural properties of zucchini slices under effects of frying conditions were investigated. Frying time and temperature were interested process variables like slice thickness. Slice thickness was studied at three levels (2, 3, and 4 mm). Frying process was performed at two temperature levels (160 and 180 °C) and each for five different process time periods (1, 2, 3, 5, 8 and 10 min). As frying oil sunflower oil was used. Before frying zucchini slices were thermally processes in boiling water for 90 seconds to inactivate at least 80% of plant’s enzymes. After thermal process, zucchini slices were fried in an industrial fryer at specified temperature and time pairs. Fried slices were subjected to textural profile analysis (TPA) to determine textural properties. In this extent hardness, elasticity, cohesion, chewiness, firmness values of slices were figured out. Statistical analysis indicated significant variations in the studied textural properties with process conditions (p < 0.05). Hardness and firmness were determined for fresh and thermally processes zucchini slices to compare each others. Differences in hardness and firmness of fresh, thermally processed and fried slices were found to be significant (p < 0.05). This project (113R015) has been supported by TUBITAK.

Keywords: sunflower oil, hardness, firmness, slice thickness, frying temperature, frying time

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Authors: Guantao Bai

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Public space is an indispensable and important component of the urban built environment. How to more accurately evaluate the usage characteristics of public space can help improve its spatial quality. Compared to traditional survey methods, computer vision technology based on deep learning has advantages such as dynamic observation and low cost. This study takes the public space of Shaping Park as an example and, based on deep learning computer vision technology, processes and analyzes the image data of the public space to obtain the spatial usage characteristics and spatiotemporal characteristics of the public space. Research has found that the spontaneous activity time in public spaces is relatively random with a relatively short average activity time, while social activities have a relatively stable activity time with a longer average activity time. Computer vision technology based on deep learning can effectively describe the spatial usage characteristics of the research area, making up for the shortcomings of traditional research methods and providing relevant support for creating a good public space.

Keywords: computer vision, deep learning, public spaces, using features

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Authors: Nathenal Thomas Lambamo

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Based on the fact that more than 85% of the labor force and 90% of the export earnings are taken by agriculture in Ethiopia and it can be said that it is the backbone of the overall socio-economic activities in the country. Among the cereal crops that the agriculture sector provides for the country, wheat is the third-ranking one preceding teff and maize. In the present day, wheat is in higher demand related to the expansion of industries that use them as the main ingredient for their products. The local supply of wheat for these companies covers only 35 to 40% and the rest 60 to 65% percent is imported on behalf of potential customers that exhaust the country’s foreign currency reserves. The above facts show that the need for this crop in the country is too high and in reverse, the productivity of the crop is very less because of these reasons. Wheat disease is the most devastating disease that contributes a lot to this unbalance in the demand and supply status of the crop. It reduces both the yield and quality of the crop by 27% on average and up to 37% when it is severe. This study aims to detect the most frequent and degrading wheat diseases, Septoria and Leaf rust, using the most efficiently used subset of machine learning technology, deep learning. As a state of the art, a deep learning class classification technique called Convolutional Neural Network (CNN) has been used to detect diseases and has an accuracy of 99.01% is achieved.

Keywords: septoria, leaf rust, deep learning, CNN

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Authors: Carlos Roncero-Parra, Alfonso Parreño-Torres, Jorge Mateo Sotos, Alejandro L. Borja

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In this research, different methods based on machine/deep learning algorithms are presented for the classification and diagnosis of patients with mental disorders such as alzheimer. For this purpose, the signals obtained from 32 unipolar electrodes identified by non-invasive EEG were examined, and their basic properties were obtained. More specifically, different well-known machine learning based classifiers have been used, i.e., support vector machine (SVM), Bayesian linear discriminant analysis (BLDA), decision tree (DT), Gaussian Naïve Bayes (GNB), K-nearest neighbor (KNN) and Convolutional Neural Network (CNN). A total of 668 patients from five different hospitals have been studied in the period from 2011 to 2021. The best accuracy is obtained was around 93 % in both ADM and ADA classifications. It can be concluded that such a classification will enable the training of algorithms that can be used to identify and classify different mental disorders with high accuracy.

Keywords: alzheimer, machine learning, deep learning, EEG

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Authors: Mahdi Akhbardeh

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Synchronization of neuronal firing is a hallmark of several neurological diseases. Recently, stimulation techniques have been developed which make it possible to desynchronize oscillatory neuronal activity in a mild and effective way, without suppressing the neurons' firing. As yet, these techniques are being used to establish demand-controlled deep brain stimulation (DBS) techniques for the therapy of movement disorders like severe Parkinson's disease or essential tremor. We here present a first conceptualization suggesting that the nucleus accumbens is a promising target for the standard, that is, permanent high-frequency, DBS in patients with severe and chronic obsessive-compulsive disorder (OCD). In addition, we explain how demand-controlled DBS techniques may be applied to the therapy of OCD in those cases that are refractory to behavioral therapies and pharmacological treatment.

Keywords: stereotactic neurosurgery, deep brain stimulation, obsessive-compulsive disorder, phase resetting

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Authors: Sai Maniveer Adapa, Sai Guptha Perla, Adithya Reddy P.

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Thyroid nodules can often be diagnosed with ultrasound imaging, although differentiating between benign and malignant nodules can be challenging for medical professionals. This work suggests a novel approach to increase the precision of thyroid nodule identification by combining machine learning and deep learning. The new approach first extracts information from the ultrasound pictures using a deep learning method known as a convolutional autoencoder. A support vector machine, a type of machine learning model, is then trained using these features. With an accuracy of 92.52%, the support vector machine can differentiate between benign and malignant nodules. This innovative technique may decrease the need for pointless biopsies and increase the accuracy of thyroid nodule detection.

Keywords: thyroid tumor diagnosis, ultrasound images, deep learning, machine learning, convolutional auto-encoder, support vector machine

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Authors: Fatemeh Saadat Ghareh Bagh, Srimanta Ray, Jerald Lalman

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Lignin, the largest inventory of organic carbon with a high caloric energy value is a major component in woody and non-woody biomass. In pulping mills, a large amount of the lignin is burned for energy. At the same time, the phenolic structure of lignin enables it to be converted to value-added compounds.This study has focused on extracting lignin from black liquor using deep eutectic solvents (DESs). Therefore, three choline chloride (ChCl)-DESs paired with lactic acid (LA) (1:11), oxalic acid.2H₂O (OX) (1:4), and malic acid (MA) (1:3) were synthesized at 90oC and atmospheric pressure. The kinetics of lignin recovery from black liquor using DES was investigated at three moderate temperatures (338, 353, and 368 K) at time intervals from 30 to 210 min. The extracted lignin (acid soluble lignin plus Klason lignin) was characterized by Fourier transform infrared spectroscopy (FTIR). The FTIR studies included comparing the extracted lignin with a model Kraft lignin. The extracted lignin was characterized spectrophotometrically to determine the acid soluble lignin (ASL) [TAPPI UM 250] fraction and Klason lignin was determined gravimetrically using TAPPI T 222 om02. The lignin extraction reaction using DESs was modeled by first-order reaction kinetics and the activation energy of the process was determined. The ChCl:LA-DES recovered lignin was 79.7±2.1% at 368K and a DES:BL ratio of 4:1 (v/v). The quantity of lignin extracted for the control solvent, [emim][OAc], was 77.5+2.2%. The activation energy measured for the LA-DES system was 22.7 KJ mol⁻¹, while the activation energy for the OX-DES and MA-DES systems were 7.16 KJ·mol⁻¹ and 8.66 KJ·mol⁻¹ when the total lignin recovery was 75.4 ±0.9% and 62.4 ±1.4, % respectively.

Keywords: black liquor, deep eutectic solvents, kinetics, lignin

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Authors: Xiaodong Li, Peng Gao, Chao-Jung Huang, Shiying Hao, Xuefeng B. Ling, Yongxia Han, Yaqi Zhang, Le Zheng, Chengyin Ye, Modi Liu, Minjie Xia, Changlin Fu, Bo Jin, Karl G. Sylvester, Eric Widen

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Predicting the risk of Pancreatic Adenocarcinoma (PA) in advance can benefit the quality of care and potentially reduce population mortality and morbidity. The aim of this study was to develop and prospectively validate a risk prediction model to identify patients at risk of new incident PA as early as 3 months before the onset of PA in a statewide, general population in Maine. The PA prediction model was developed using Deep Neural Networks, a deep learning algorithm, with a 2-year electronic-health-record (EHR) cohort. Prospective results showed that our model identified 54.35% of all inpatient episodes of PA, and 91.20% of all PA that required subsequent chemoradiotherapy, with a lead-time of up to 3 months and a true alert of 67.62%. The risk assessment tool has attained an improved discriminative ability. It can be immediately deployed to the health system to provide automatic early warnings to adults at risk of PA. It has potential to identify personalized risk factors to facilitate customized PA interventions.

Keywords: cancer prediction, deep learning, electronic health records, pancreatic adenocarcinoma

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Authors: Praveen S. Muthukumarana, Achala C. Aponso

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A large percentage of global crop harvest is lost due to crop diseases. Timely identification and treatment of crop diseases is difficult in many developing nations due to insufficient trained professionals in the field of agriculture. Many crop diseases can be accurately diagnosed by visual symptoms. In the past decade, deep learning has been successfully utilized in domains such as healthcare but adoption in agriculture for plant disease detection is rare. The literature shows that models trained with popular datasets such as PlantVillage does not generalize well on real world images. This paper attempts to find out how to make plant disease identification models that generalize well with real world images.

Keywords: agriculture, convolutional neural network, deep learning, plant disease classification, plant disease detection, plant disease diagnosis

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Authors: Ayyaz Hussain, Tariq Sadad

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Breast cancer (BC) is one of the widespread ailments among females globally. The early prognosis of BC can decrease the mortality rate. Exact findings of benign tumors can avoid unnecessary biopsies and further treatments of patients under investigation. However, due to variations in images, it is a tough job to isolate cancerous cases from normal and benign ones. The machine learning technique is widely employed in the classification of BC pattern and prognosis. In this research, a deep convolution neural network (DCNN) called AlexNet architecture is employed to get more discriminative features from breast tissues. To achieve higher accuracy, K-nearest neighbor (KNN) classifiers are employed as a substitute for the softmax layer in deep learning. The proposed model is tested on a widely used breast image database called MIAS dataset for experimental purposes and achieved 99% accuracy.

Keywords: breast cancer, DCNN, KNN, mammography

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Authors: Ali Reza Sattarzadeh, Ronny J. Kutadinata, Pubudu N. Pathirana, Van Thanh Huynh

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Traffic congestion has become a severe worldwide problem, affecting everyday life, fuel consumption, time, and air pollution. The primary causes of these issues are inadequate transportation infrastructure, poor traffic signal management, and rising population. Traffic flow forecasting is one of the essential and effective methods in urban congestion and traffic management, which has attracted the attention of researchers. With the development of technology, undeniable progress has been achieved in existing methods. However, there is a possibility of improvement in the extraction of temporal and spatial features to determine the importance of traffic flow sequences and extraction features. In the proposed model, we implement the convolutional neural network (CNN) and long short-term memory (LSTM) deep learning models for mining nonlinear correlations and their effectiveness in increasing the accuracy of traffic flow prediction in the real dataset. According to the experiments, the results indicate that implementing Conv-LSTM networks increases the productivity and accuracy of deep learning models for traffic flow prediction.

Keywords: deep learning algorithms, intelligent transportation systems, spatiotemporal features, traffic flow prediction

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Authors: Bin Feng, Zhulin Zong

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Radar systems are critical in the modern military, aviation, and maritime operations, and their proper functioning is essential for the success of these operations. However, due to the complexity and sensitivity of radar systems, they are susceptible to various faults that can significantly affect their performance. Traditional radar fault diagnosis strategies rely on expert knowledge and rule-based approaches, which are often limited in effectiveness and require a lot of time and resources. Deep learning has recently emerged as a promising approach for fault diagnosis due to its ability to learn features and patterns from large amounts of data automatically. In this paper, we propose a radar fault diagnosis strategy based on deep learning that can accurately identify and classify faults in radar systems. Our approach uses convolutional neural networks (CNN) to extract features from radar signals and fault classify the features. The proposed strategy is trained and validated on a dataset of measured radar signals with various types of faults. The results show that it achieves high accuracy in fault diagnosis. To further evaluate the effectiveness of the proposed strategy, we compare it with traditional rule-based approaches and other machine learning-based methods, including decision trees, support vector machines (SVMs), and random forests. The results demonstrate that our deep learning-based approach outperforms the traditional approaches in terms of accuracy and efficiency. Finally, we discuss the potential applications and limitations of the proposed strategy, as well as future research directions. Our study highlights the importance and potential of deep learning for radar fault diagnosis. It suggests that it can be a valuable tool for improving the performance and reliability of radar systems. In summary, this paper presents a radar fault diagnosis strategy based on deep learning that achieves high accuracy and efficiency in identifying and classifying faults in radar systems. The proposed strategy has significant potential for practical applications and can pave the way for further research.

Keywords: radar system, fault diagnosis, deep learning, radar fault

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Authors: Lorenzo Gontrani, Marilena Carbone, Domenica Tommasa Donia, Elvira Maria Bauer, Pietro Tagliatesta

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One of the fields where DES shows remarkable added values is the synthesis Of inorganic materials, in particular nanoparticles. In this field, the higher- ent and highly-tunable nano-homogeneities of DES structure give origin to a marked templating effect, a precious role that has led to the recent bloom of a vast number of studies exploiting these new synthesis media to prepare Nanomaterials and composite structures of various kinds. In this contribution, the most recent developments in the field will be reviewed, and some ex-citing examples of novel metal oxide nanoparticles syntheses using non-toxic type-IV Deep Eutectic Solvents will be described. The prepared materials possess nanometric dimensions and show flower-like shapes. The use of the pre- pared nanoparticles as fluorescent materials for the detection of various contaminants is under development.

Keywords: metal deep eutectic solvents, nanoparticles, inorganic synthesis, type IV DES, lamellar

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Authors: Y. Gholipour, E. Rezazadeh

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Delay is one of the most serious and common problems of construction project that can affect project delivery unfavorably. This research presents the most important causes of delay in large dam projects based on a survey on some executed dam construction in Iran. In this survey a randomly selected samples of owners, consultants and contractors have been involved. The outcome of this survey revealed that scheduled payments, site management, shop drawing review process, unforeseen ground conditions and contractor experience as the most important factors affecting on delay in dam construction projects.

Keywords: delay, dam construction, project management, Iran

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Authors: Fatma Hsain Ali Suliman

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Personality plays an important role in acquiring a second language. For second language learners to make maximum progress with their own learning styles, their individual differences must be recognized and attended to. Personality is considered to be a pattern of unique characteristics that give a person’s behavior a kind of consistency and individuality. Therefore, the enclosed study, which is entitled “The Role of Personality in Second language Acquisition: Extroversion and Introversion”, tends to shed light on the relationship between learners’ personalities and second language acquisition process. In other words, it aims at drawing attention to how individual differences of students as being extroverts or introverts could affect the language acquisition process. As a literature review, this paper discusses the results of some studies concerning this issue as well as the point views of researchers and scholars who have focused on the effect of extrovert and introvert personality on acquiring a second language. To accomplish the goals of this study, which is divided into 5 chapters including introduction, review of related literature, research method and design, results and discussions and conclusions and recommendations, 20 students of English Department, Faculty of Arts, Misurata University, Libya were handed out a questionnaire to figure out the effect of their personalities on the learning process. Finally, to be more sure about the role of personality in a second language acquisition process, the same students who were given the questionnaire were observed in their ESL classes.

Keywords: second language acquisition, personality, extroversion, introversion, individual differences, language learning strategy, personality factors, psycho linguistics

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Authors: Ming-Jen Huang, Chun-Fang Huang, Chiching Wei

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With the recent advance of the deep neural network, we observe new applications of NLP (natural language processing) and CV (computer vision) powered by deep neural networks for processing business documents. However, creating a real-world document processing system needs to integrate several NLP and CV tasks, rather than treating them separately. There is a need to have a unified approach for processing documents containing textual and graphical elements with rich formats, diverse layout arrangement, and distinct semantics. In this paper, a framework that fulfills this unified approach is presented. The framework includes a representation model definition for holding the information generated by various tasks and specifications defining the coordination between these tasks. The framework is a blueprint for building a system that can process documents with rich formats, styles, and multiple types of elements. The flexible and lightweight design of the framework can help build a system for diverse business scenarios, such as contract monitoring and reviewing.

Keywords: document processing, framework, formal definition, machine learning

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Authors: Koray U. Erbas

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Neural networks are appealing for many applications since they are able to learn complex non-linear relationships between input and output data. As the number of neurons and layers in a neural network increase, it is possible to represent more complex relationships with automatically extracted features. Nowadays Deep Neural Networks (DNNs) are widely used in Computer Vision problems such as; classification, object detection, segmentation image editing etc. In this work, Facial Emotion Recognition task is performed by proposed Convolutional Neural Network (CNN)-based DNN architecture using FER2013 Dataset. Moreover, the effects of different hyperparameters (activation function, kernel size, initializer, batch size and network size) are investigated and ablation study results for Pooling Layer, Dropout and Batch Normalization are presented.

Keywords: convolutional neural network, deep learning, deep learning based FER, facial emotion recognition

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Authors: Yitao Lei, Xingxiang Zhai, Burra Venkata Durga Kumar

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As the quantity and complexity of computing in large-scale software systems increase, distributed system computing becomes increasingly important. The distributed system realizes high-performance computing by collaboration between different computing resources. If there are no efficient resource scheduling resources, the abuse of distributed computing may cause resource waste and high costs. However, resource scheduling is usually an NP-hard problem, so we cannot find a general solution. However, some optimization algorithms exist like genetic algorithm, ant colony optimization, etc. The large scale of distributed systems makes this traditional optimization algorithm challenging to work with. Heuristic and machine learning algorithms are usually applied in this situation to ease the computing load. As a result, we do a review of traditional resource scheduling optimization algorithms and try to introduce a deep reinforcement learning method that utilizes the perceptual ability of neural networks and the decision-making ability of reinforcement learning. Using the machine learning method, we try to find important factors that influence the performance of distributed system computing and help the distributed system do an efficient computing resource scheduling. This paper surveys the application of deep reinforcement learning on distributed system computing resource scheduling proposes a deep reinforcement learning method that uses a recurrent neural network to optimize the resource scheduling, and proposes the challenges and improvement directions for DRL-based resource scheduling algorithms.

Keywords: resource scheduling, deep reinforcement learning, distributed system, artificial intelligence

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Authors: Adamu Zubairu Mohammed, Fransis Oforum Arimoro, Salihu Maikudi Ibrahim, Y. I. Auta, T. I. Arowosegbe, Y. Abdullahi

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The present study was conducted to evaluate the effect of depth on the distribution of zooplankton and some physicochemical parameters in Tungan Kawo Lake (Wushishi dam). Water and zooplankton samples were collected from the surface, 3.0 meters deep and 6.0 meters deep, for a period of 24 hours for six months. Standard procedures were adopted for the determination of physicochemical parameters. Results have shown significant differences in the pH, DO, BOD Hardness, Na, and Mg. A total of 1764 zooplankton were recorded, comprising 35 species, with cladocera having 18 species (58%), 14 species of copepoda (41%), 3 species of diptera (1.0%). Results show that more of the zooplankton were recorded in the 3.0 meters-deep region compared to the two other depts and a significant difference was observed in the distribution of Ceriodaphnia dubia, Daphnia laevis, and Leptodiaptomus coloradensis. Though the most abundant zooplankton was recorded in the 3.0 meters deep, Leptodiaptomus coloradesnsis, which was observed in the 6.0 meters deep as the most individual observed, this was followed by Daphnia laevis. Canonical correspondence analysis between physicochemical parameters and the zooplankton indicated a good relationship in the Lake. Ceriodaphnia dubia was found to have a good association with oxygen, sodium, and potassium, while Daphnia laevis and Leptodiaptomus coloradensis are in good relationship with magnesium and phosphorus. It was generally observed that this depth does not have much influence on the distribution of zooplankton in Wushishi Lake.

Keywords: zooplankton, standard procedures, canonical correspondence analysis, Wushishi, canonical, physicochemical parameter

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Authors: A. Kavya Sri, K. Sai Vineela, R. Vanitha, S. Rohith

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Large pieces of ice that break from the glaciers are known as icebergs. The threat that icebergs pose to navigation, production of offshore oil and gas services, and underwater pipelines makes their detection crucial. In this project, an automated iceberg tracking method using deep learning techniques and satellite images of icebergs is to be developed. With a temporal resolution of 12 days and a spatial resolution of 20 m, Sentinel-1 (SAR) images can be used to track iceberg drift over the Southern Ocean. In contrast to multispectral images, SAR images are used for analysis in meteorological conditions. This project develops a web-based graphical user interface to detect and track icebergs using sentinel-1 images. To track the movement of the icebergs by using temporal images based on their latitude and longitude values and by comparing the center and area of all detected icebergs. Testing the accuracy is done by precision and recall measures.

Keywords: synthetic aperture radar (SAR), icebergs, deep learning, spatial resolution, temporal resolution

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Authors: Shimaa Holail, Tamer Saleh, Xiongwu Xiao

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Building change detection (BCD) from satellite imagery is an essential topic in urbanization monitoring, agricultural land management, and updating geospatial databases. Recently, methods for detecting changes based on deep learning have made significant progress and impressive results. However, it has the problem of being insensitive to changes in buildings with complex spectral differences, and the features being extracted are not discriminatory enough, resulting in incomplete buildings and irregular boundaries. To overcome these problems, we propose a dual Siamese network based on the Unet model with the addition of a deep supervision strategy (DS) in this paper. This network consists of a backbone (encoder) based on ImageNet pre-training, a fusion block, and feature pyramid networks (FPN) to enhance the step-by-step information of the changing regions and obtain a more accurate BCD map. To train the proposed method, we created a new dataset (EGY-BCD) of high-resolution and multi-temporal aerial images captured over New Cairo in Egypt to detect building changes for this purpose. The experimental results showed that the proposed method is effective and performs well with the EGY-BCD dataset regarding the overall accuracy, F1-score, and mIoU, which were 91.6 %, 80.1 %, and 73.5 %, respectively.

Keywords: building change detection, deep supervision, semantic segmentation, EGY-BCD dataset

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Authors: Ali Berkan Ural

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This paper presents the analysis, evaluation, and pre-diagnosis of early stage breast based diagnostic problems (breast cancer, nodulesorlumps) by Computer Aided Diagnosing (CAD) system from mammogram radiological images. According to the statistics, the time factor is crucial to discover the disease in the patient (especially in women) as possible as early and fast. In the study, a new algorithm is developed using advanced image processing and deep learning method to detect and classify the problem at earlystagewithmoreaccuracy. This system first works with image processing methods (Image acquisition, Noiseremoval, Region Growing Segmentation, Morphological Operations, Breast BorderExtraction, Advanced Segmentation, ObtainingRegion Of Interests (ROIs), etc.) and segments the area of interest of the breast and then analyzes these partly obtained area for cancer detection/lumps in order to diagnosis the disease. After segmentation, with using the Spectrogramimages, 5 different deep learning based methods (specified Convolutional Neural Network (CNN) basedAlexNet, ResNet50, VGG16, DenseNet, Xception) are applied to classify the breast based problems.

Keywords: computer aided diagnosis, breast cancer, region growing, segmentation, deep learning

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Authors: Chad Brown

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This paper establishes general conditions for the convergence rates of nonparametric sieve estimators with dependent data. We present two key results: one for nonstationary data and another for stationary mixing data. Previous theoretical results often lack practical applicability to deep neural networks (DNNs). Using these conditions, we derive convergence rates for DNN sieve estimators in nonparametric regression settings with both nonstationary and stationary mixing data. The DNN architectures considered adhere to current industry standards, featuring fully connected feedforward networks with rectified linear unit activation functions, unbounded weights, and a width and depth that grows with sample size.

Keywords: sieve extremum estimates, nonparametric estimation, deep learning, neural networks, rectified linear unit, nonstationary processes

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Authors: Rahou Mohamed , Sebaa Fethi, Cheikh Abdelmadjid

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Manufacturing tolerancing is intended to determine the intermediate geometrical and dimensional states of the part during its manufacturing process. These manufacturing dimensions also serve to satisfy not only the functional requirements given in the definition drawing but also the manufacturing constraints, for example geometrical defects of the machine, vibration, and the wear of the cutting tool. The choice of positioning has an important influence on the cost and quality of manufacture. To avoid this problem, a two-step approach have been developed. The first step is dedicated to the determination of the optimum position. As for the second step, a study was carried out for the tightening effect on the tolerance interval.

Keywords: dispersion, tolerance, manufacturing, position

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Authors: P. Fernández-Morales, D. Peláez, C. Isaza, J. M. Meza, E. Mendoza

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Mg AZ31B alloy sheets were processed through equal-channel angular sheet drawing (ECASD) process, following the route A and C at room temperature and varying the processing speed. SEM was used to analyze the microstructure. The grain size was refined and presence of twins was observed. Vickers microhardness and tensile testing were carried out to evaluate the mechanical properties, showing in general; a remarkable increase in the first pass and slight increases during subsequent passes and, that the route C produces better uniform properties distribution through the thickness of the samples.

Keywords: ECASD, Mg Alloy, mechanical properties, microstructure

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Authors: Anahita Farhang Ghahfarokhi, Alessandro Berti, Wil M.P. van der Aalst

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Process mining provides ways to analyze business processes. Common process mining techniques consider the process as a whole. However, in real-life business processes, different behaviors exist that make the overall process too complex to interpret. Process comparison is a branch of process mining that isolates different behaviors of the process from each other by using process cubes. Process cubes organize event data using different dimensions. Each cell contains a set of events that can be used as an input to apply process mining techniques. Existing work on process cubes assume single case notions. However, in real processes, several case notions (e.g., order, item, package, etc.) are intertwined. Object-centric process mining is a new branch of process mining addressing multiple case notions in a process. To make a bridge between object-centric process mining and process comparison, we propose a process cube framework, which supports process cube operations such as slice and dice on object-centric event logs. To facilitate the comparison, the framework is integrated with several object-centric process discovery approaches.

Keywords: multidimensional process mining, mMulti-perspective business processes, OLAP, process cubes, process discovery, process mining

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Authors: Vijaya Yuvaram Singh V M, Kameshwar Rao J V

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The challenge with development of neural network based methods for medical is the availability of data. Anatomical landmark detection in the medical domain is a process to find points on the x-ray scan report of the patient. Most of the time this task is done manually by trained professionals as it requires precision and domain knowledge. Traditionally object detection based methods are used for landmark detection. Here, we utilize reinforcement learning and query based method to train a single agent capable of detecting multiple landmarks. A deep Q network agent is trained to detect single and multiple landmarks present on hip and shoulder from x-ray scan of a patient. Here a single agent is trained to find multiple landmark making it superior to having individual agents per landmark. For the initial study, five images of different patients are used as the environment and tested the agents performance on two unseen images.

Keywords: reinforcement learning, medical landmark detection, multi target detection, deep neural network

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Authors: Walid El Kamash

Abstract:

Sand columns (or granular piles) can be employed as soil strengthening for flexible constructions such as road embankments, oil storage tanks in addition to multistory structures. The challenge of embedding the sand columns in soft soil is that the surrounding soft soil cannot avail the enough confinement stress in order to keep the form of the sand column. Therefore, the sand columns which were installed in such soil will lose their ability to perform needed load-bearing capacity. The encasement, besides increasing the strength and stiffness of the sand column, prevents the lateral squeezing of sands when the column is installed even in extremely soft soils, thus enabling quicker and more economical installation. This paper investigates the improvement in load capacity of the sand column by encasement through a comprehensive parametric study using the 3-D finite difference analysis for the soft clay of soil in Egypt. Moreover, the study was extended to include a comparison study between encased sand column and Deep Mixed columns (DM). The study showed that confining the sand by geosynthetic resulted in an increment of shear strength. That result paid the attention to use encased sand stone rather than deep mixed columns due to relative high permeability of the first material.

Keywords: encased sand column, Deep mixed column, numerical analysis, improving soft soil

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