Search results for: high resolution SAR image

Authors: Berkan Ural, Arif Eser, Sinan Apaydin

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Medical image processing is generally become a challenging task nowadays. Indeed, processing of brain MRI images is one of the difficult parts of this area. This study proposes a hybrid well-defined approach which is consisted from tumor detection, extraction and analyzing steps. This approach is mainly consisted from a computer aided diagnostics system for identifying and detecting the tumor formation in any region of the brain and this system is commonly used for early prediction of brain tumor using advanced image processing and probabilistic neural network methods, respectively. For this approach, generally, some advanced noise removal functions, image processing methods such as automatic segmentation and morphological operations are used to detect the brain tumor boundaries and to obtain the important feature parameters of the tumor region. All stages of the approach are done specifically with using MATLAB software. Generally, for this approach, firstly tumor is successfully detected and the tumor area is contoured with a specific colored circle by the computer aided diagnostics program. Then, the tumor is segmented and some morphological processes are achieved to increase the visibility of the tumor area. Moreover, while this process continues, the tumor area and important shape based features are also calculated. Finally, with using the probabilistic neural network method and with using some advanced classification steps, tumor area and the type of the tumor are clearly obtained. Also, the future aim of this study is to detect the severity of lesions through classes of brain tumor which is achieved through advanced multi classification and neural network stages and creating a user friendly environment using GUI in MATLAB. In the experimental part of the study, generally, 100 images are used to train the diagnostics system and 100 out of sample images are also used to test and to check the whole results. The preliminary results demonstrate the high classification accuracy for the neural network structure. Finally, according to the results, this situation also motivates us to extend this framework to detect and localize the tumors in the other organs.

Keywords: image processing algorithms, magnetic resonance imaging, neural network, pattern recognition

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Authors: Jianhong Xiang, Hao Xiang, Linyu Wang

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Compressed sensing (CS) has a wide range of applications in sparse signal reconstruction. Aiming at the problems of low recovery accuracy and long reconstruction time of existing reconstruction algorithms in medical imaging, this paper proposes a corrected smoothing L0 algorithm based on compressed sensing (CSL0). First, an approximate hyperbolic tangent function (AHTF) that is more similar to the L0 norm is proposed to approximate the L0 norm. Secondly, in view of the "sawtooth phenomenon" in the steepest descent method and the problem of sensitivity to the initial value selection in the modified Newton method, the use of the steepest descent method and the modified Newton method are jointly optimized to improve the reconstruction accuracy. Finally, the CSL0 algorithm is simulated on various images. The results show that the algorithm proposed in this paper improves the reconstruction accuracy of the test image by 0-0. 98dB.

Keywords: smoothed L0, compressed sensing, image processing, sparse reconstruction

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Authors: Shelby Simpson, William Stanley, Namir Naba, Xiaodi Wang

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Both t-SNE and UMAP are brand new state of art tools to predominantly preserve the local structure that is to group neighboring data points together, which indeed provides a very informative visualization of heterogeneity in our data. In this research, we develop a t-SNE and UMAP base neural network image classification algorithm to embed the original dataset to a corresponding low dimensional dataset as a preprocessing step, then use this embedded database as input to our specially designed neural network classifier for image classification. We use the fashion MNIST data set, which is a labeled data set of images of clothing objects in our experiments. t-SNE and UMAP are used for dimensionality reduction of the data set and thus produce low dimensional embeddings. Furthermore, we use the embeddings from t-SNE and UMAP to feed into two neural networks. The accuracy of the models from the two neural networks is then compared to a dense neural network that does not use embedding as an input to show which model can classify the images of clothing objects more accurately.

Keywords: t-SNE, UMAP, fashion MNIST, neural networks

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Authors: Gizelle K. Vianna, Gabriel V. Cunha, Gustavo S. Oliveira

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Foliage diseases in plants can cause a reduction in both quality and quantity of agricultural production. Intelligent detection of plant diseases is an essential research topic as it may help monitoring large fields of crops by automatically detecting the symptoms of foliage diseases. This work investigates ways to recognize the late blight disease from the analysis of tomato digital images, collected directly from the field. A pair of multilayer perceptron neural network analyzes the digital images, using data from both RGB and HSL color models, and classifies each image pixel. One neural network is responsible for the identification of healthy regions of the tomato leaf, while the other identifies the injured regions. The outputs of both networks are combined to generate the final classification of each pixel from the image and the pixel classes are used to repaint the original tomato images by using a color representation that highlights the injuries on the plant. The new images will have only green, red or black pixels, if they came from healthy or injured portions of the leaf, or from the background of the image, respectively. The system presented an accuracy of 97% in detection and estimation of the level of damage on the tomato leaves caused by late blight.

Keywords: artificial neural networks, digital image processing, pattern recognition, phytosanitary

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Authors: Elham Bagheri, Yalda Mohsenzadeh

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Image memorability refers to the phenomenon where certain images are more likely to be remembered by humans than others. It is a quantifiable and intrinsic attribute of an image. Understanding how visual perception and memory interact is important in both cognitive science and artificial intelligence. It reveals the complex processes that support human cognition and helps to improve machine learning algorithms by mimicking the brain's efficient data processing and storage mechanisms. To explore the computational underpinnings of image memorability, this study examines the relationship between an image's reconstruction error, distinctiveness in latent space, and its memorability score. A trained autoencoder is used to replicate human-like memorability assessment inspired by the visual memory game employed in memorability estimations. This study leverages a VGG-based autoencoder that is pre-trained on the vast ImageNet dataset, enabling it to recognize patterns and features that are common to a wide and diverse range of images. An empirical analysis is conducted using the MemCat dataset, which includes 10,000 images from five broad categories: animals, sports, food, landscapes, and vehicles, along with their corresponding memorability scores. The memorability score assigned to each image represents the probability of that image being remembered by participants after a single exposure. The autoencoder is finetuned for one epoch with a batch size of one, attempting to create a scenario similar to human memorability experiments where memorability is quantified by the likelihood of an image being remembered after being seen only once. The reconstruction error, which is quantified as the difference between the original and reconstructed images, serves as a measure of how well the autoencoder has learned to represent the data. The reconstruction error of each image, the error reduction, and its distinctiveness in latent space are calculated and correlated with the memorability score. Distinctiveness is measured as the Euclidean distance between each image's latent representation and its nearest neighbor within the autoencoder's latent space. Different structural and perceptual loss functions are considered to quantify the reconstruction error. The results indicate that there is a strong correlation between the reconstruction error and the distinctiveness of images and their memorability scores. This suggests that images with more unique distinct features that challenge the autoencoder's compressive capacities are inherently more memorable. There is also a negative correlation between the reduction in reconstruction error compared to the autoencoder pre-trained on ImageNet, which suggests that highly memorable images are harder to reconstruct, probably due to having features that are more difficult to learn by the autoencoder. These insights suggest a new pathway for evaluating image memorability, which could potentially impact industries reliant on visual content and mark a step forward in merging the fields of artificial intelligence and cognitive science. The current research opens avenues for utilizing neural representations as instruments for understanding and predicting visual memory.

Keywords: autoencoder, computational vision, image memorability, image reconstruction, memory retention, reconstruction error, visual perception

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Authors: M. Gomez, A. Molina

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The concept of brand equity in the wine tourism area is an interesting topic to explore the factors that determine it. The aim of this study is to address this gap by investigating wine tourism destinations brand equity, and understanding the impact that the denomination of origin (DO) brand image and the destination image have on brand equity. Managing and monitoring the branding of wine tourism destinations is crucial to attract tourist arrivals. The multiplicity of stakeholders involved in the branding process calls for research that, unlike previous studies, adopts a broader perspective and incorporates an internal and an external perspective. Therefore, this gap by comparing managers’ and visitors’ approaches to wine tourism destination brand equity has been addressed. A survey questionnaire for data collection purposes was used. The hypotheses were tested using winery managers and winery visitors, each leading a different position relative to the wine tourism destination brand equity. All the interviews were conducted face-to-face. The survey instrument included several scales related to DO brand image, destination image, and wine tourism destination brand equity. All items were measured on seven-point Likert scales. Partial least squares was used to analyze the accuracy of scales, the structural model, and multi-group analysis to identify the differences in the path coefficients and to test the hypotheses. The results show that the positive influence of DO brand image on wine tourism destination brand equity is stronger for wineries than for visitors, but there are no significant differences between the two groups. However, there are significant differences in the positive effect of destination brand image on both wine tourism destination brand equity and DO brand image. The results of this study are important for consultants, practitioners, and policy makers. The gap between managers and visitors calls for the development of a number of campaigns to enhance the image that visitors hold and, thus, increase tourist arrivals. Events such as wine gatherings and gastronomic symposiums held at universities and culinary schools and participation in business meetings can enhance the perceptions and in turn, the added value, brand equity of the wine tourism destinations. The images of destinations and DOs can help strengthen the brand equity of the wine tourism destinations, especially for visitors. Thus, the development and reinforcement of favorable, strong, and unique destination associations and DO associations are important to increase that value. Joint campaigns are advisable to enhance the images of destinations and DOs and, as a consequence, the value of the wine tourism destination brand.

Keywords: brand equity, managers, visitors, wine tourism

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Authors: Erfan Niazi, Marianne Fenech

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Red Blood Cells (RBCs) or erythrocytes tend to form chain-like aggregates under low shear rate called rouleaux. This is a reversible process and rouleaux disaggregate in high shear rates. Therefore, RBCs aggregation occurs in the microcirculation where low shear rates are present but does not occur under normal physiological conditions in large arteries. Numerical modeling of RBCs interactions is fundamental in analytical models of a blood flow in microcirculation. Population Balance Modeling (PBM) is particularly useful for studying problems where particles agglomerate and break in a two phase flow systems to find flow characteristics. In this method, the elementary particles lose their individual identity due to continuous destructions and recreations by break-up and agglomeration. The aim of this study is to find RBCs aggregation in a dynamic situation. Simplified PBM was used previously to find the aggregation rate on a static observation of the RBCs aggregation in a drop of blood under the microscope. To find aggregation rate in a dynamic situation we propose an experimental set up testing RBCs sedimentation. In this test, RBCs interact and aggregate to form rouleaux. In this configuration, disaggregation can be neglected due to low shear stress. A high-speed camera is used to acquire video-microscopic pictures of the process. The sizes of the aggregates and velocity of sedimentation are extracted using an image processing techniques. Based on the data collection from 5 healthy human blood samples, the aggregation rate was estimated as 2.7x10³(±0.3 x10³) 1/s.

Keywords: red blood cell, rouleaux, microfluidics, image processing, population balance modeling

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Authors: Vahid Anari, Leila Shahmohammadi

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Diagnosing and interpreting manually from a large cohort dataset of immunohistochemically stained tissue of tumors using an optical microscope involves subjectivity and also is tedious for pathologist specialists. Moreover, digital pathology today represents more of an evolution than a revolution in pathology. In this paper, we develop and test an unsupervised algorithm that can automatically enhance the IHC image of a meningioma tumor and classify cells into positive (proliferative) and negative (normal) cells. A dataset including 150 images is used to test the scheme. In addition, a new adaptive color image enhancement method is proposed based on a vector directional filter (VDF) and statistical properties of filtering the window. Since the cells are distinguishable by the human eye, the accuracy and stability of the algorithm are quantitatively compared through application to a wide variety of real images.

Keywords: digital pathology, cell segmentation, immunohistochemically, noise reduction

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Authors: Abdellatif Baba

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In order to facilitate medical images analysis by improving their quality and readability, we present in this paper a new dynamic band pass filter as a general and suitable operator for different types of medical images. Our objective is to enrich the details of any treated medical image to make it sufficiently clear enough to give an understood and simplified meaning even for unspecialized people in the medical domain.

Keywords: medical image enhancement, dynamic band pass filter, analysis improvement

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Authors: Srushti R. Joshi, Ujjwal Rakesh, Spoorthi Sripad

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The rapid advancement of remote sensing technologies has revolutionized plant health monitoring, offering valuable insights for precision agriculture and environmental management. This paper presents a comprehensive comparative analysis between the widely employed normalized difference vegetation index (NDVI) and state-of-the-art hyperspectral sensors in the context of plant health assessment. The study aims to elucidate the weigh ups of spectral resolution. Employing a diverse range of vegetative environments, the research utilizes simulated datasets to evaluate the performance of NDVI and hyperspectral sensors in detecting subtle variations indicative of plant stress, disease, and overall vitality. Through meticulous data analysis and statistical validation, this study highlights the superior performance of hyperspectral sensors across the parameters used.

Keywords: normalized difference vegetation index, hyperspectral sensor, spectral resolution, infrared

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Authors: Chih-Yi Ho, Tzu-Fang Chang, Chih-Chia Huang, Chia-Yen Lee

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In dental clinics, the dentists use the periapical radiography image to assess the effectiveness of endodontic treatment of teeth with chronic apical periodontitis. Periapical radiography images are taken at different times to assess alveolar bone variation before and after the root canal treatment, and furthermore to judge whether the treatment was successful. Current clinical assessment of apical tissue recovery relies only on dentist personal experience. It is difficult to have the same standard and objective interpretations due to the dentist or radiologist personal background and knowledge. If periapical radiography images at the different time could be registered well, the endodontic treatment could be evaluated. In the image registration area, it is necessary to assign representative control points to the transformation model for good performances of registration results. However, detection of representative control points (feature points) on periapical radiography images is generally very difficult. Regardless of which traditional detection methods are practiced, sufficient feature points may not be detected due to the low-contrast characteristics of the x-ray image. Barnard detector is an algorithm for feature point detection based on grayscale value gradients, which can obtain sufficient feature points in the case of gray-scale contrast is not obvious. However, the Barnard detector would detect too many feature points, and they would be too clustered. This study uses the local extrema of clustering feature points and the suppression radius to overcome the problem, and compared different feature point detection methods. In the preliminary result, the feature points could be detected as representative control points by the proposed method.

Keywords: feature detection, Barnard detector, registration, periapical radiography image, endodontic treatment

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Authors: M. L. Mesquita, V. H. Ferreira, C. M. Cebola

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Mediation is an increasingly important mechanism, particularly in the European context, as demonstrated, for example, by the publication by the European Union of the Directive 2008/52/EC on certain aspects of mediation in civil and mercantile matters. Developments in international trade and globalization in this new century have led to an increase of the number of litigations, often cross-border, and the courts have failed to respond adequately. From the economic point of view, competitive negotiation can generate negative external effects in social terms. Not always the solution found in court is the most efficient solution taking into account all elements of society. On the other hand, the administration of justice adds in economic terms transaction costs that can be mitigated by the application of other forms of conflict resolution, such as mediation. In this paper, the economic benefits of mediation will be analysed in the light of various studies on the functioning of justice. Several theoretical arguments will be confronted with empirical studies to demonstrate that mediation has significant positive economic effects. In the Portuguese legal system, legislative frameworks for mediation display a state committed to creating a new architecture for the administration of justice, based on the construction of a multi-faceted legal system for dispute resolution mechanisms. Understanding the way in which the system of mediation in Portugal was introduced, allows us to point out that our internal ordering is creating the legal instruments which can assist citizens in the effective protection of their rights. However, data on the use of mediation in concrete proceedings and the consequent effectiveness of mediation in settling disputes, reveal a mechanism that is still far from the ideal results that were initially sought.

Keywords: access to justice, alternative dispute resolution, mediation, litigation

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Authors: Masoud Nasiri Sarvi, Yunhua Luo

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Low-trauma hip fracture, usually caused by fall from standing height, has become a main source of morbidity and mortality for the elderly. Factors affecting hip fracture include sex, race, age, body weight, height, body mass distribution, etc., and thus, hip fracture risk in fall differs widely from subject to subject. It is therefore necessary to develop a subject-specific biomechanical model to predict hip fracture risk. The objective of this study is to develop a two-level, image-based, subject-specific biomechanical model consisting of a whole-body dynamics model and a proximal-femur finite element (FE) model for more accurately assessing the risk of hip fracture in lateral falls. Required information for constructing the model is extracted from a whole-body and a hip DXA (Dual Energy X-ray Absorptiometry) image of the subject. The proposed model considers all parameters subject-specifically, which will provide a fast, accurate, and non-expensive method for predicting hip fracture risk.

Keywords: bone mineral density, hip fracture risk, impact force, sideways falls

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Authors: Thomas Njuguna Kibutu

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The ability to access justice is an important facet of securing peaceful, just, and inclusive societies, as recognized by Goal 16 of the 2030 Agenda for Sustainable Development. Goal 16 calls for peace, justice, and strong institutions to promote the rule of law and access to justice at a global level. More specifically, Target 16.3 of the Goal aims to promote the rule of law at the national and international levels and ensure equal access to justice for all. On the other hand, it is now widely recognized that Alternative Dispute Resolution (hereafter, ADR) represents an efficient mechanism for resolving disputes outside the adversarial conventional court system of litigation or prosecution. ADR processes include but are not limited to negotiation, reconciliation, mediation, arbitration, and traditional conflict resolution. ADR has a number of advantages, including being flexible, cost-efficient, time-effective, and confidential, and giving the parties more control over the process and the results, thus promoting restorative justice. The methodology of this paper is a desktop review of books, journal articles, reports and government documents., among others. The paper recognizes that ADR represents a cornerstone of Africa’s, and more specifically, Kenya’s, efforts to promote inclusive, accountable, and effective institutions and achieve the objectives of goal 16. In Kenya, and not unlike many African countries, there has been an outcry over the backlog of cases that are yet to be resolved in the courts and the statistics have shown that the numbers keep on rising. While ADR mechanisms have played a major role in reducing these numbers, access to justice in the country remains a big challenge, especially to the subaltern. There is, therefore, a need to analyze the opportunities and challenges facing the application of ADR mechanisms as tools for accessing justice in Kenya and further discuss various ways in which we can overcome these challenges to make ADR an effective alternative to dispute resolution. The paper argues that by embracing ADR across various sectors and addressing existing shortcomings, Kenya can, over time, realize its vision of a more just and equitable society. This paper discusses the opportunities and challenges of the application of ADR in Kenya with a view to sharing the lessons and challenges with the wider African continent. The paper concludes that ADR mechanisms can provide critical pathways to justice in Kenya and the African continent in general but come with distinct challenges. The paper thus calls for concerted efforts of respective stakeholders to overcome these challenges.

Keywords: mediation, arbitration, negotiation, reconsiliation, Traditional conflict resolution, sustainable development

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Authors: Walaa M.Abd-Elhafiez, Wajeb Gharibi

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In this paper a novel color image compression technique for efficient storage and delivery of data is proposed. The proposed compression technique started by RGB to YCbCr color transformation process. Secondly, the canny edge detection method is used to classify the blocks into edge and non-edge blocks. Each color component Y, Cb, and Cr compressed by discrete cosine transform (DCT) process, quantizing and coding step by step using adaptive arithmetic coding. Our technique is concerned with the compression ratio, bits per pixel and peak signal to noise ratio, and produce better results than JPEG and more recent published schemes (like, CBDCT-CABS and MHC). The provided experimental results illustrate the proposed technique which is efficient and feasible in terms of compression ratio, bits per pixel and peak signal to noise ratio.

Keywords: image compression, color image, q-coder, quantization, edge-detection

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Authors: Morteza Khashehchi, Kamel Hooman

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Thermal effects on the dynamics and stability of the flow past a circular cylinder operating in the mixed convection regime is studied experimentally for Reynolds number (ReD) between 1000 and 4000, and different cylinder wall temperatures (Tw) between 25 and 75°C by means of Particle Image Velocimetry (PIV). The experiments were conducted in a horizontal wind tunnel with the heated cylinder placed horizontally. With such assumptions, the direction of the thermally induced buoyancy force acting on the fluid surrounding the heated cylinder would be perpendicular to the flow direction. In each experiment, to acquire 3000 PIV image pairs, the temperature and Reynolds number of the approach flow were held constant. By adjusting different temperatures in different Reynolds numbers, the corresponding Richardson number (RiD = Gr/Re^2) was varied between 0:0 (unheated) and 10, resulting in a change in the heat transfer process from forced convection to mixed convection. With increasing temperature of the wall cylinder, significant modifications of the wake flow pattern and wake vortex shedding process were clearly revealed. For cylinder at low wall temperature, the size of the wake and the vortex shedding process are found to be quite similar to those of an unheated cylinder. With high wall temperature, however, the high temperature gradient in the wake shear layer creates a type of vorticity with opposite sign to that of the shear layer vorticity. This temperature gradient vorticity weakens the strength of the shear layer vorticity, causing delay in reaching the recreation point. In addition to the wake characteristics, the shedding frequency for the heated cylinder is determined for all aforementioned cases. It is found that, as the cylinder wall is heated, the organization of the vortex shedding is altered and the relative position of the first detached vortices with respect to the second one is changed. This movement of the first detached vortex toward the second one increases the frequency of the shedding process. It is also found that the wake closure length decreases with increasing the Richardson number.

Keywords: heated cylinder, PIV, wake, Reynolds number

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Authors: Aaron J. Small, Craig A. Fletcher

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This study investigates the effect of closed circuit television (CCTV) image patch layout on performance of a simulated train-platform departure task. The within-subjects experimental design measures target detection rate and response latency during a CCTV visual search task conducted as part of the procedure for safe train dispatch. Three interface designs were developed by manipulating CCTV image patch layout. Eye movements, perceived workload and system usability were measured across experimental conditions. Task performance was compared to identify significant differences between conditions. The results of this study have not been determined.

Keywords: rail human factors, workload, closed circuit television, platform departure, attention, information processing, interface design

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Authors: Emiko Suzuki

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Although some claim that we are in a post-feminist society, the word “postfeminism” still raises questions to many. In postfeminist media, as a British sociologist Rosalind Gill points out, on the one hand, it seems to promote an empowering image of women who are active, positively sexually motivated, has free will to make market choices, and have surveillance and discipline for their personality and body, yet on the other hand, such beautiful and attractive feminist image imposes stronger surveillance of their mind and body for women. Similar representation, which is that femininity is described in a contradictive way, is seen in Japanese media as well. This study tries to capture how post-feminist Japanese media is, contrary to its ostensible messages, encouraging women to join the obedience to the labor system by affirming the traditional image of attractive women using sexual objectification and promoting values of neoliberalism. The result shows an interesting insight into how Japanese media is creating a conflicting ideal representation of women through repeatedly exposing such images.

Keywords: postfeminism, Japanese media, sexual objectification, embodiment

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Authors: Jack Nash, Chris Simpson, Holly Hurn, Ronel Terblanche, Alan Mistlin

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There is an increasing incidence of mild traumatic brain injury (mTBI) cases throughout sport and with this, a growing interest from governing bodies to ensure these are managed appropriately and player welfare is prioritised. The Berlin consensus statement on concussion in sport recommends a multidisciplinary approach when managing those patients who do not have full resolution of mTBI symptoms. There are as of yet no standardised guideline to follow in the treatment of complex cases mTBI in athletes. The aim of this project was to analyse the outcomes, both clinical and vocational, of all patients admitted to the mild Traumatic Brain Injury (mTBI) service at the UK’s Defence Military Rehabilitation Centre Headley Court between 1st June 2008 and 1st February 2017, as a result of a sport induced injury, and evaluate potential predictive indicators of outcome. Patients were identified from a database maintained by the mTBI service. Clinical and occupational outcomes were ascertained from medical and occupational employment records, recorded prospectively, at time of discharge from the mTBI service. Outcomes were graded based on the vocational independence scale (VIS) and clinical documentation at discharge. Predictive indicators including referral time, age at time of injury, previous mental health diagnosis and a financial claim in place at time of entry to service were assessed using logistic regression. 45 Patients were treated for sport-related mTBI during this time frame. Clinically 96% of patients had full resolution of their mTBI symptoms after input from the mTBI service. 51% of patients returned to work at their previous vocational level, 4% had ongoing mTBI symptoms, 22% had ongoing physical rehabilitation needs, 11% required mental health input and 11% required further vestibular rehabilitation. Neither age, time to referral, pre-existing mental health condition nor compensation seeking had a significant impact on either vocational or clinical outcome in this population. The vast majority of patients reviewed in the mTBI clinic had persistent symptoms which could not be managed in primary care. A consultant-led, multidisciplinary approach to the diagnosis and management of mTBI has resulted in excellent clinical outcomes in these complex cases. High levels of symptom resolution suggest that this referral and treatment pathway is successful and is a model which could be replicated in other organisations with consultant led input. Further understanding of both predictive and individual factors would allow clinicians to focus treatments on those who are most likely to develop long-term complications following mTBI. A consultant-led, multidisciplinary service ensures a large number of patients will have complete resolution of mTBI symptoms after sport-related mTBI. Further research is now required to ascertain the key predictive indicators of outcome following sport-related mTBI.

Keywords: brain injury, concussion, neurology, rehabilitation, sports injury

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Authors: Nomfundo Ngombane, Topside E. Mathonsi

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Remote sensing and satellite imaging have been widely used to detect wildfires; nevertheless, the technologies present some limitations in terms of early wildfire detection as the technologies are greatly influenced by weather conditions and can miss small fires. The fires need to have spread a few kilometers for the technologies to provide accurate detection. The South African Advanced Fire Information System uses MODIS (Moderate Resolution Imaging Spectroradiometer) as satellite imaging. MODIS has limitations as it can exclude small fires and can fall short in validating fire vulnerability. Thus in the future, a Machine Learning algorithm will be designed and implemented for the early detection of wildfires. A simulator will be used to evaluate the effectiveness of the proposed solution, and the results of the simulation will be presented.

Keywords: moderate resolution imaging spectroradiometer, advanced fire information system, machine learning algorithm, detection of wildfires

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Authors: H. Emari, Z. Emari

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The main objective of this research was to determine the dimensions of service quality in the banking industry of Iran. For this purpose, the study empirically examined the European perspective suggesting that service quality consists of three dimensions, technical, functional and image. This research is an applied research and its strategy is casual strategy. A standard questionnaire was used for collecting the data. 287 customers of Melli Bank of Northwest were selected through cluster sampling and were studied. The results from a banking service sample revealed that the overall service quality is influenced more by a consumer’s perception of technical quality than functional quality. Accordingly, the Gronroos model is a more appropriate representation of service quality than the American perspective with its limited concentration on the dimension of functional quality in the banking industry of Iran. So, knowing the key dimensions of the quality of services in this industry and planning for their improvement can increase the satisfaction of customers and productivity of this industry.

Keywords: technical quality, functional quality, banking, image, mediating role

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Authors: Nancy Elizabeth Naranjo Garcia

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Canoa (1976) by Felipe Cazals is a film that exposes the consequences of power that the Mexican State exercised over the 1968 student movement. The film, in this particular way, approaches the Tlatelolco Massacre from a point of view that takes into consideration the events that led up to it. Nonetheless, the reference to the political tension in Canoa remains ambiguous. Thus, the cinematographic representation refers to an event that leaves space for reflection, and as a consequence leaves evidence of an image that signals the notion of survival as Georges Didi-Huberman points out. In addition to denouncing the oppressive force by the Mexican State, the images in Canoa also emphasize what did not happen in Tlatelolco and its condensation with the student activists. To observe the images that Canoa offers in a new light, this work proposes further exploration with the following questions; How do the images in Canoa narrate? How are the images inserted in the film? In this fashion, a more profound comprehension of the objective and the essence of the images becomes feasible. As a result, it is possible to analyze the images of Canoa with the real killing at San Miguel Canoa in literature. The film visualizes a testimony of the event that once seemed unimaginable, an image that anticipates and structures the proceeding event. Therefore, this study takes a second look at how Canoa considers not only the killing at San Miguel Canoa and the Tlatlelolco Massacre, but goes further on contextualize an unimaginable image.

Keywords: cinematographic representation, student movement, Tlatelolco Massacre, unimaginable image

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Authors: Shuge Lei, Haonan Hu, Dasheng Sun, Huabin Zhang, Kehong Yuan, Jian Dai, Yan Tong

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It remains challenging to measure reliability from classification results from different machine learning models. This paper proposes a reliable soup optimization algorithm based on the model weight fusion algorithm Model Soup, aiming to improve reliability by using dual-channel reliability as the objective function to fuse a series of weights in the breast ultrasound classification models. Experimental results on breast ultrasound clinical datasets demonstrate that reliable soup significantly enhances the reliability of breast ultrasound image classification tasks. The effectiveness of the proposed approach was verified via multicenter trials. The results from five centers indicate that the reliability optimization algorithm can enhance the reliability of the breast ultrasound image classification model and exhibit low multicenter correlation.

Keywords: breast ultrasound image classification, feature attribution, reliability assessment, reliability optimization

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Authors: Norzalina Othman, Nurul Na’imy Wan, Azliza Mohd Rusli, Wan Noor Syahirah Meor Idris

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Iris detection is a process where the position of the eyes is extracted from the face images. It is a current method used for many applications such as for security purpose and drowsiness detection. This paper proposes the use of eyes detection in controlling side mirror of motor vehicles. The eyes detection method aims to make driver easy to adjust the side mirrors automatically. The system will determine the midpoint coordinate of eyes detection on RGB (color) image and the input signal from y-coordinate will send it to controller in order to rotate the angle of side mirror on vehicle. The eye position was cropped and the coordinate of midpoint was successfully detected from the circle of iris detection using Viola Jones detection and circular Hough transform methods on RGB image. The coordinate of midpoint from the experiment are tested using controller to determine the angle of rotation on the side mirrors.

Keywords: iris detection, midpoint coordinates, RGB images, side mirror

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Authors: Stella C. Okenu, Stephen O. Adikwu, Martins E. Okoro

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The geothermal energy resource potential of the Lower Benue Trough (LBT) in Nigeria was evaluated in this study using spectral analysis of high-resolution aeromagnetic (HRAM) data. The reduced to the equator aeromagnetic data was divided into sixteen (16) overlapping blocks, and each of the blocks was analyzed to obtain the radial averaged power spectrum which enabled the computation of the top and centroid depths to magnetic sources. The values were then used to assess the Curie Point Depth (CPD), geothermal gradients, and heat flow variations in the study area. Results showed that CPD varies from 7.03 to 18.23 km, with an average of 12.26 km; geothermal gradient values vary between 31.82 and 82.50°C/km, with an average of 51.21°C/km, while heat flow variations range from 79.54 to 206.26 mW/m², with an average of 128.02 mW/m². Shallow CPD zones that run from the eastern through the western and southwestern parts of the study area correspond to zones of high geothermal gradient values and high subsurface heat flow distributions. These areas signify zones associated with anomalous subsurface thermal conditions and are therefore recommended for detailed geothermal energy exploration studies.

Keywords: geothermal energy, curie-point depth, geothermal gradient, heat flow, aeromagnetic data, LBT

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Authors: Maryam Fallahpoor, Biswajeet Pradhan

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Background: Artificial intelligence (AI) serves as a valuable tool in mitigating the scarcity of human resources required for the evaluation and categorization of vast quantities of medical imaging data. When AI operates with optimal precision, it minimizes the demand for human interpretations and, thereby, reduces the burden on radiologists. Among various AI approaches, deep learning (DL) stands out as it obviates the need for feature extraction, a process that can impede classification, especially with intricate datasets. The advent of DL models has ushered in a new era in medical imaging, particularly in the context of COVID-19 detection. Traditional 2D imaging techniques exhibit limitations when applied to volumetric data, such as Computed Tomography (CT) scans. Medical images predominantly exist in one of two formats: neuroimaging informatics technology initiative (NIfTI) and digital imaging and communications in medicine (DICOM). Purpose: This study aims to employ DL for the classification of COVID-19-infected pulmonary patients and normal cases based on 3D CT scans while investigating the impact of image format. Material and Methods: The dataset used for model training and testing consisted of 1245 patients from IranMehr Hospital. All scans shared a matrix size of 512 × 512, although they exhibited varying slice numbers. Consequently, after loading the DICOM CT scans, image resampling and interpolation were performed to standardize the slice count. All images underwent cropping and resampling, resulting in uniform dimensions of 128 × 128 × 60. Resolution uniformity was achieved through resampling to 1 mm × 1 mm × 1 mm, and image intensities were confined to the range of (−1000, 400) Hounsfield units (HU). For classification purposes, positive pulmonary COVID-19 involvement was designated as 1, while normal images were assigned a value of 0. Subsequently, a U-net-based lung segmentation module was applied to obtain 3D segmented lung regions. The pre-processing stage included normalization, zero-centering, and shuffling. Four distinct 3D CNN models (ResNet152, ResNet50, DensNet169, and DensNet201) were employed in this study. Results: The findings revealed that the segmentation technique yielded superior results for DICOM images, which could be attributed to the potential loss of information during the conversion of original DICOM images to NIFTI format. Notably, ResNet152 and ResNet50 exhibited the highest accuracy at 90.0%, and the same models achieved the best F1 score at 87%. ResNet152 also secured the highest Area under the Curve (AUC) at 0.932. Regarding sensitivity and specificity, DensNet201 achieved the highest values at 93% and 96%, respectively. Conclusion: This study underscores the capacity of deep learning to classify COVID-19 pulmonary involvement using real 3D hospital data. The results underscore the significance of employing DICOM format 3D CT images alongside appropriate pre-processing techniques when training DL models for COVID-19 detection. This approach enhances the accuracy and reliability of diagnostic systems for COVID-19 detection.

Keywords: deep learning, COVID-19 detection, NIFTI format, DICOM format

Procedia PDF Downloads 85

Authors: Robert Lenzie

Abstract:

Transcranial electrical stimulation (tES) is significant in the research literature. However, the effects of tES on brain activity are still poorly understood at the surface level, the Brodmann Area level, and the impact on neural networks. Using a method like electroencephalography (EEG) in conjunction with tES might make it possible to comprehend the brain response and mechanisms behind published observed alterations in more depth. Using a method to directly see the effect of tES on EEG may offer high temporal resolution data on the brain activity changes/modulations brought on by tES that correlate to various processing stages within the brain. This paper provides unpublished information on a cutting-edge methodology that may reveal details about the dynamics of how the human brain works beyond what is now achievable with existing methods.

Keywords: tACS, frequency, EEG, optimal

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Authors: Subodh Chandra Shakya, Badal Shrestha, Suni Thapa, Ashutosh Chauhan, Saugat Adhikari

Abstract:

To tackle our problem of clothes identification, we used different architectures of Convolutional Neural Networks. Among different architectures, the outcome from Inception ResNet V2 and MobileNet V2 seemed promising. On comparison of the metrices, we observed that the Inception ResNet V2 slightly outperforms MobileNet V2 for this purpose. So this paper of ours proposes the cloth identifier using Inception ResNet V2 and also contains the comparison between the outcome of ResNet V2 and MobileNet V2. The document here contains the results and findings of the research that we performed on the DeepFashion Dataset. To improve the dataset, we used different image preprocessing techniques like image shearing, image rotation, and denoising. The whole experiment was conducted with the intention of testing the efficiency of convolutional neural networks on cloth identification so that we could develop a reliable system that is good enough in identifying the clothes worn by the users. The whole system can be integrated with some kind of recommendation system.

Keywords: inception ResNet, convolutional neural net, deep learning, confusion matrix, data augmentation, data preprocessing

Procedia PDF Downloads 186

Authors: Saba Rahimi, Ozan Oktay, Javier Alvarez-Valle, Sujeeth Bharadwaj

Abstract:

Successful application of deep learning in medical image analysis necessitates unprecedented amounts of labeled training data. Unlike conventional 2D applications, radiological images can be three-dimensional (e.g., CT, MRI), consisting of many instances within each image. The problem is exacerbated when expert annotations are required for effective pixel-wise labeling, which incurs exorbitant labeling effort and cost. Active learning is an established research domain that aims to reduce labeling workload by prioritizing a subset of informative unlabeled examples to annotate. Our contribution is a cost-effective approach for U-Net 3D models that uses Monte Carlo sampling to analyze pixel-wise uncertainty. Experiments on the AAPM 2017 lung CT segmentation challenge dataset show that our proposed framework can achieve promising segmentation results by using only 42% of the training data.

Keywords: image segmentation, active learning, convolutional neural network, 3D U-Net

Procedia PDF Downloads 153

Authors: Ananya Ananya, Karthik Rao

Abstract:

Accurate segmentation of knee cartilage in 3-D magnetic resonance (MR) images for quantitative assessment of volume is crucial for studying and diagnosing osteoarthritis (OA) of the knee, one of the major causes of disability in elderly people. Radiologists generally perform this task in slice-by-slice manner taking 15-20 minutes per 3D image, and lead to high inter and intra observer variability. Hence automatic methods for knee cartilage segmentation are desirable and are an active field of research. This paper presents design and experimental evaluation of 2D convolutional neural networks based fully automated methods for knee cartilage segmentation in 3D MRI. The architectures are validated based on 40 test images and 60 training images from SKI10 dataset. The proposed methods segment 2D slices one by one, which are then combined to give segmentation for whole 3D images. Proposed methods are modified versions of U-net and dilated convolutions, consisting of a single step that segments the given image to 5 labels: background, femoral cartilage, tibia cartilage, femoral bone and tibia bone; cartilages being the primary components of interest. U-net consists of a contracting path and an expanding path, to capture context and localization respectively. Dilated convolutions lead to an exponential expansion of receptive field with only a linear increase in a number of parameters. A combination of modified U-net and dilated convolutions has also been explored. These architectures segment one 3D image in 8 – 10 seconds giving average volumetric Dice Score Coefficients (DSC) of 0.950 - 0.962 for femoral cartilage and 0.951 - 0.966 for tibia cartilage, reference being the manual segmentation.

Keywords: convolutional neural networks, dilated convolutions, 3 dimensional, fully automated, knee cartilage, MRI, segmentation, U-net

Procedia PDF Downloads 259