Search results for: image clustering

Authors: Morteza Giti

Abstract:

As online shopping is becoming increasingly popular, computing customer lifetime value for better knowing the customers is also gaining more importance. Two distinct factors that can affect the value of a customer in the context of online shopping is the number of returned orders and payment method. Returned orders are those which have been shipped but not collected by the customer and are returned to the store. Payment method refers to the way that customers choose to pay for the price of the order which are usually two: Pre-pay and Cash-on-delivery. In this paper, a novel model called RFMSP is presented to calculated the customer lifetime value, taking these two parameters into account. The RFMSP model is based on the common RFM model while adding two extra parameter. The S represents the order status and the P indicates the payment method. As a case study for this model, the purchase history of customers in an online shop is used to compute the customer lifetime value over a period of twenty months.

Keywords: RFMSP model, AHP, customer lifetime value, k-means clustering, e-commerce

Procedia PDF Downloads 314

Authors: Shaira L. Kee, Michael Aaron G. Sy, Myles Joshua T. Tan, Hezerul Abdul Karim, Nouar AlDahoul

Abstract:

Skin diseases are considered the fourth most common disease, with melanoma and non-melanoma skin cancer as the most common type of cancer in Caucasians. The alarming increase in Skin Cancer cases shows an urgent need for further research to improve diagnostic methods, as early diagnosis can significantly improve the 5-year survival rate. Machine Learning algorithms for image pattern analysis in diagnosing skin lesions can dramatically increase the accuracy rate of detection and decrease possible human errors. Several studies have shown the diagnostic performance of computer algorithms outperformed dermatologists. However, existing methods still need improvements to reduce diagnostic errors and generate efficient and accurate results. Our paper proposes an ensemble method to classify dermoscopic images into benign and malignant skin lesions. The experiments were conducted using the International Skin Imaging Collaboration (ISIC) image samples. The dataset contains 3,297 dermoscopic images with benign and malignant categories. The results show improvement in performance with an accuracy of 88% and an F1 score of 87%, outperforming other existing models such as support vector machine (SVM), Residual network (ResNet50), EfficientNetB0, EfficientNetB4, and VGG16.

Keywords: deep learning - VGG16 - efficientNet - CNN – ensemble – dermoscopic images - melanoma

Procedia PDF Downloads 79

Authors: Lutful Karim, Mohammed S. Al-kahtani

Abstract:

Sensors are being used in various applications such as agriculture, health monitoring, air and water pollution monitoring, traffic monitoring and control and hence, play the vital role in the growth of big data. However, sensors collect redundant data. Thus, aggregating and filtering sensors data are significantly important to design an efficient big data framework. Current researches do not focus on aggregating and filtering data at multiple layers of sensor-based big data framework. Thus, this paper introduces (i) three layers data aggregation and framework for big data and (ii) a priority-based, dynamic data aggregation scheme (PDDA) for the lowest layer at sensors. Simulation results show that the PDDA outperforms existing tree and cluster-based data aggregation scheme in terms of overall network energy consumptions and end-to-end data transmission delay.

Keywords: big data, clustering, tree topology, data aggregation, sensor networks

Procedia PDF Downloads 343

Authors: Krishna Mohan Bathula, Fatou Bintou Loucoubar, FNU Kaleemunnisa, Christelle Scharff, Mark Anthony De Castro

Abstract:

Voice recognition algorithms such as automatic speech recognition and text-to-speech systems with African languages can play an important role in bridging the digital divide of Artificial Intelligence in Africa, contributing to the establishment of a fully inclusive information society. This paper proposes a Deep Learning model that can classify the user responses as inputs for an interactive voice response system. A dataset with Wolof language words ‘yes’ and ‘no’ is collected as audio recordings. A two stage Data Augmentation approach is adopted for enhancing the dataset size required by the deep neural network. Data preprocessing and feature engineering with Mel-Frequency Cepstral Coefficients are implemented. Convolutional Neural Networks (CNNs) have proven to be very powerful in image classification and are promising for audio processing when sounds are transformed into spectra. For performing voice response classification, the recordings are transformed into sound frequency feature spectra and then applied image classification methodology using a deep CNN model. The inference model of this trained and reusable Wolof voice response recognition system can be integrated with many applications associated with both web and mobile platforms.

Keywords: automatic speech recognition, interactive voice response, voice response recognition, wolof word classification

Procedia PDF Downloads 115

Authors: Pradeep Kumar Singh Chauhan, Gayatri Devi, Zamir Ahmad, Komal Chauhan, Abha Mittal

Abstract:

The application of geophysical methods to study the subsurface profile for site investigation is becoming popular globally. These methods are non-destructive and provide the image of subsurface at shallow depths. Seismic refraction method is one of the most common and efficient method being used for civil engineering site investigations particularly for knowing the seismic velocity of the subsurface layers. Resistivity imaging technique is a geo-electrical method used to image the subsurface, water bearing zone, bedrock and layer thickness. Integrated approach combining seismic refraction and 2-D resistivity imaging will provide a better and reliable picture of the subsurface. These are economical and less time-consuming field survey which provide high resolution image of the subsurface. Geophysical surveys carried out in this study include seismic refraction and 2D resistivity imaging method for delineation of sub-surface strata in different parts of Srinagar, Garhwal Himalaya, India. The aim of this survey was to map the shallow subsurface in terms of geological and geophysical properties mainly P-wave velocity, resistivity, layer thickness, and lithology of the area. Both sides of the river, Alaknanda which flows through the centre of the city, have been covered by taking two profiles on each side using both methods. Seismic and electrical surveys were carried out at the same locations to complement the results of each other. The seismic refraction survey was carried out using ABEM TeraLoc 24 channel Seismograph and 2D resistivity imaging was performed using ABEM Terrameter LS equipment. The results show three distinct layers on both sides of the river up to the depth of 20 m. The subsurface is divided into three distinct layers namely, alluvium extending up to, 3 m depth, conglomerate zone lying between the depth of 3 m to 15 m, and compacted pebbles and cobbles beyond 15 m. P-wave velocity in top layer is found in the range of 400 – 600 m/s, in second layer it varies from 700 – 1100 m/s and in the third layer it is 1500 – 3300 m/s. The resistivity results also show similar pattern and were in good agreement with seismic refraction results. The results obtained in this study were validated with an available exposed river scar at one site. The study established the efficacy of geophysical methods for subsurface investigations.

Keywords: 2D resistivity imaging, P-wave velocity, seismic refraction survey, subsurface

Procedia PDF Downloads 255

Authors: Feng Yin

Abstract:

Clouds are inevitable contaminants in optical satellite imagery, and prevent the satellite imaging systems from acquiring clear view of the earth surface. The presence of clouds in satellite imagery bring negative influences for remote sensing land resources investigation. As a consequence, detecting the locations of clouds in satellite imagery is an essential preprocessing step, and further remove the existing clouds is crucial for the application of imagery. In this paper, a multi-temporal based satellite imagery cloud detection and removal method is proposed, which will be used for large-scale land resource investigation. The proposed method is mainly composed of four steps. First, cloud masks are generated for cloud contaminated images by single temporal cloud detection based on multiple spectral features. Then, a cloud-free reference image of target areas is synthesized by weighted averaging time-series images in which cloud pixels are ignored. Thirdly, the refined cloud detection results are acquired by multi-temporal analysis based on the reference image. Finally, detected clouds are removed via multi-temporal linear regression. The results of a case application in Hubei province indicate that the proposed multi-temporal cloud detection and removal method is effective and promising for large-scale land resource investigation.

Keywords: cloud detection, cloud remove, multi-temporal imagery, land resources investigation

Procedia PDF Downloads 276

Authors: Basiru Amuneni

Abstract:

Astronomy is one domain with a rise in data. Traditional tools for data management have been employed in the quest for knowledge discovery. However, these traditional tools become limited in the face of big. One means of maximizing knowledge discovery for big data is the use of scientific visualisation. The aim of the work is to explore the possibilities offered by emerging creative technologies of Virtual Reality (VR) systems and game engines to visualize multiwavelength datasets. Game Engines are primarily used for developing video games, however their advanced graphics could be exploited for scientific visualization which provides a means to graphically illustrate scientific data to ease human comprehension. Modern astronomy is now in the era of multiwavelength data where a single galaxy for example, is captured by the telescope several times and at different electromagnetic wavelength to have a more comprehensive picture of the physical characteristics of the galaxy. Visualising this in an immersive environment would be more intuitive and natural for an observer. This work presents a standalone VR application that accesses galaxy FITS files. The application was built using the Unity Game Engine for the graphics underpinning and the OpenXR API for the VR infrastructure. The work used a methodology known as Design Science Research (DSR) which entails the act of ‘using design as a research method or technique’. The key stages of the galaxy modelling pipeline are FITS data preparation, Galaxy Modelling, Unity 3D Visualisation and VR Display. The FITS data format cannot be read by the Unity Game Engine directly. A DLL (CSHARPFITS) which provides a native support for reading and writing FITS files was used. The Galaxy modeller uses an approach that integrates cleaned FITS image pixels into the graphics pipeline of the Unity3d game Engine. The cleaned FITS images are then input to the galaxy modeller pipeline phase, which has a pre-processing script that extracts, pixel, galaxy world position, and colour maps the FITS image pixels. The user can visualise image galaxies in different light bands, control the blend of the image with similar images from different sources or fuse images for a holistic view. The framework will allow users to build tools to realise complex workflows for public outreach and possibly scientific work with increased scalability, near real time interactivity with ease of access. The application is presented in an immersive environment and can use all commercially available headset built on the OpenXR API. The user can select galaxies in the scene, teleport to the galaxy, pan, zoom in/out, and change colour gradients of the galaxy. The findings and design lessons learnt in the implementation of different use cases will contribute to the development and design of game-based visualisation tools in immersive environment by enabling informed decisions to be made.

Keywords: astronomy, visualisation, multiwavelenght dataset, virtual reality

Procedia PDF Downloads 88

Authors: Oumaima Ben Abderrahim, Mohamed Houcine Elhedhili, Leila Saidane

Abstract:

In this paper, we propose a trust management system based on clustering architecture for the social internet of things called TMBCO-SIOT. The proposed model integrates numerous factors such as direct and indirect trust; transaction factor; precaution factor; and social modeling of trust. The novelty of our approach can be summed up in two aspects. The first aspect concerns the architecture based on the community of interest (CoT) where each community is headed by an administrator (admin). However, the second aspect is the trust management system that tries to prevent On-Off attacks and mitigates dishonest recommendations using the k-means algorithm and guarantor things. The effectiveness of the proposed system is proved by simulation against malicious nodes.

Keywords: IoT, trust management system, attacks, trust, dishonest recommendations, K-means algorithm

Procedia PDF Downloads 210

Authors: Inigo Beckett

Abstract:

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

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

Procedia PDF Downloads 49

Authors: Ali Hamza

Abstract:

Accurate segmentation of breast ultrasound images is of paramount importance in enhancing the diagnostic capabilities of breast cancer detection. This study presents an approach utilizing the U-Net architecture for segmenting breast ultrasound images aimed at improving the accuracy and reliability of mass identification within the breast tissue. The proposed method encompasses a multi-stage process. Initially, preprocessing techniques are employed to refine image quality and diminish noise interference. Subsequently, the U-Net architecture, a deep learning convolutional neural network (CNN), is employed for pixel-wise segmentation of regions of interest corresponding to potential breast masses. The U-Net's distinctive architecture, characterized by a contracting and expansive pathway, enables accurate boundary delineation and detailed feature extraction. To evaluate the effectiveness of the proposed approach, an extensive dataset of breast ultrasound images is employed, encompassing diverse cases. Quantitative performance metrics such as the Dice coefficient, Jaccard index, sensitivity, specificity, and Hausdorff distance are employed to comprehensively assess the segmentation accuracy. Comparative analyses against traditional segmentation methods showcase the superiority of the U-Net architecture in capturing intricate details and accurately segmenting breast masses. The outcomes of this study emphasize the potential of the U-Net-based segmentation approach in bolstering breast ultrasound image analysis. The method's ability to reliably pinpoint mass boundaries holds promise for aiding radiologists in precise diagnosis and treatment planning. However, further validation and integration within clinical workflows are necessary to ascertain their practical clinical utility and facilitate seamless adoption by healthcare professionals. In conclusion, leveraging the U-Net architecture for breast ultrasound image segmentation showcases a robust framework that can significantly enhance diagnostic accuracy and advance the field of breast cancer detection. This approach represents a pivotal step towards empowering medical professionals with a more potent tool for early and accurate breast cancer diagnosis.

Keywords: mage segmentation, U-Net, deep learning, breast cancer detection, diagnostic accuracy, mass identification, convolutional neural network

Procedia PDF Downloads 82

Authors: Lauren Schuldt Wilson

Abstract:

Felicia Hemans’ memorializing of the unwritten lives of women and the consequent allowance for marginalized voices to remember and be remembered has been considered by many critics in terms of ekphrasis and elegy, terms which privilege the question of whether Hemans’ poeticizing can represent lost voices of history or only her poetic expression. Amy Gates, Brian Elliott, and others point out Hemans’ acknowledgement of the self-projection necessary for imaginatively filling the absences of unrecorded histories. Yet, few have examined the complex temporal positioning Hemans inscribes in these moments of self-projection and imaginative historicizing. In poems like ‘The Image in Lava,’ Hemans maps not only a lost past, but also a lost potential future onto the image of a dead infant in its mother’s arms, the discovery and consideration of which moves the imagined viewer to recover and incorporate the ‘hope’ encapsulated in the figure of the infant into a reevaluation of national time embodied by the ‘relics / Left by the pomps of old.’ By examining Hemans’ acknowledgement and response to Percy Bysshe Shelley’s ‘Ozymandias,’ this essay explores how Hemans’ depictions of imaginative historicizing open new horizons of possibility and reevaluate temporal value structures by imagining previously undiscovered or unexplored potentialities of the past. Where Shelley’s poem mocks the futility of national power and time, this essay outlines Hemans’ suggestion of alternative threads of identity and temporal meaning-making which, regardless of historical veracity, exist outside of and against the structures Shelley challenges. Counter to previous readings of Hemans’ poem as celebration of either recovered or poetically constructed maternal love, this essay argues that Hemans offers a meditation on sites of reproduction—both of personal reproductive futurity and of national reproduction of power. This meditation culminates in Hemans’ gesturing towards a method of historicism by which the imagined viewer reinvigorates the sterile, ‘shattered visage’ of national time by forming temporal identity through the imagining of trans-historical hope inscribed on the infant body of the universal, individual subject rather than the broken monument of the king.

Keywords: futurity, national temporalities, reproduction, revisionary histories

Procedia PDF Downloads 164

Authors: Guanqiao Wang, Hongyang Yu

Abstract:

There is a lot of repetitive work in the traditional construction industry. These repetitive tasks can significantly improve production efficiency by replacing manual tasks with robots. There- fore, robots appear more and more frequently in the construction industry. Navigation and positioning are very important tasks for construction robots, and the requirements for accuracy of positioning are very high. Traditional indoor robots mainly use radiofrequency or vision methods for positioning. Compared with ordinary robots, the indoor plastering robot needs to be positioned closer to the wall for wall plastering, so the requirements for construction positioning accuracy are higher, and the traditional navigation positioning method has a large error, which will cause the robot to move. Without the exact position, the wall cannot be plastered, or the error of plastering the wall is large. A new positioning method is proposed, which is assisted by line lasers and uses image processing-based positioning to perform more accurate positioning on the traditional positioning work. In actual work, filter, edge detection, Hough transform and other operations are performed on the images captured by the camera. Each time the position of the laser line is found, it is compared with the standard value, and the position of the robot is moved or rotated to complete the positioning work. The experimental results show that the actual positioning error is reduced to less than 0.5 mm by this accurate positioning method.

Keywords: indoor plastering robot, navigation, precise positioning, line laser, image processing

Procedia PDF Downloads 146

Authors: Urooj Saeed, Sajid Rashid Ahmad, Iqra Khalid, Sahar Mirza, Imtiaz Younas

Abstract:

Pakistan, a flood-prone country, has experienced worst floods in the recent past which have caused extensive damage to the urban and rural areas by loss of lives, damage to infrastructure and agricultural fields. Poor flood management system in the country has projected the risks of damages as the increasing frequency and magnitude of floods are felt as a consequence of climate change; affecting national economy directly or indirectly. To combat the needs of flood emergency, this paper focuses on remotely sensed data based approach for rapid mapping and monitoring of flood extent and its damages so that fast dissemination of information can be done, from local to national level. In this research study, spatial extent of the flooding caused by heavy rains of 2014 has been mapped by using space borne data to assess the crop damages and affected population in sixteen districts of Punjab. For this purpose, moderate resolution imaging spectroradiometer (MODIS) was used to daily mark the flood extent by using Normalised Difference Water Index (NDWI). The highest flood value data was integrated with the LandScan 2014, 1km x 1km grid based population, to calculate the affected population in flood hazard zone. It was estimated that the floods covered an area of 16,870 square kilometers, with 3.0 million population affected. Moreover, to assess the flood damages, Object Based Image Analysis (OBIA) aided with spectral signatures was applied on Landsat image to attain the thematic layers of healthy (0.54 million acre) and damaged crops (0.43 million acre). The study yields that the population of Jhang district (28% of 2.5 million population) was affected the most. Whereas, in terms of crops, Jhang and Muzzafargarh are the ‘highest damaged’ ranked district of floods 2014 in Punjab. This study was completed within 24 hours of the peak flood time, and proves to be an effective methodology for rapid assessment of damages due to flood hazard

Keywords: flood hazard, space borne data, object based image analysis, rapid damage assessment

Procedia PDF Downloads 324

Authors: Yuan-lin Liu, Ye Li, Tian Xia

Abstract:

There are some shortcomings in Chinese existing taxi management modes. This paper suggests to establish the third-party comprehensive information management platform and put forward an evaluation model based on credit. Four indicators are used to evaluate the drivers’ credit, they are passengers’ evaluation score, driving behavior evaluation, drivers’ average bad record number, and personal credit score. A weighted clustering method is used to achieve credit level evaluation for taxi drivers. The management of taxi industry is based on the credit level, while the grade of the drivers is accorded to their credit rating. Credit rating determines the cost, income levels, the market access, useful period of license and the level of wage and bonus, as well as violation fine. These methods can make the credit evaluation effective. In conclusion, more credit data will help to set up a more accurate and detailed classification standard library.

Keywords: credit, mobile internet, e-hailing taxi, management mode, weighted cluster

Procedia PDF Downloads 320

Authors: L. Belkhiri, L. Mouni, A. Tiri, T.S. Narany

Abstract:

In the present study, the self-organizing map (SOM) clustering technique was applied to identify homogeneous clusters of hydrochemical parameters in El Milia plain, Algeria, to assess the quality of groundwater for potable and agricultural purposes. The visualization of SOM-analysis indicated that 35 groundwater samples collected in the study area were classified into three clusters, which showed progressive increase in electrical conductivity from cluster one to cluster three. Samples belonging to cluster one are mostly located in the recharge zone showing hard fresh water type, however, water type gradually changed to hard-brackish type in the discharge zone, including clusters two and three. Ionic ratio studies indicated the role of carbonate rock dissolution in increases on groundwater hardness, especially in cluster one. However, evaporation and evapotranspiration are the main processes increasing salinity in cluster two and three.

Keywords: groundwater quality, self-organizing maps, drinking water, irrigation water

Procedia PDF Downloads 254

Authors: Chen-Chang Lee, Po-Chou Chen, Jo-Chi Jao, Chun-Chung Lui, Leung-Chit Tsang, Lain-Chyr Hwang

Abstract:

Fetal Magnetic Resonance Imaging (MRI) is a challenge task because the fetal movements could cause motion artifact in MR images. The remedy to overcome this problem is to use fast scanning pulse sequences. The Single-Shot Fast Spin-Echo (SSFSE) T2-weighted imaging technique is routinely performed and often used as a gold standard in clinical examinations. Fast spoiled gradient-echo (FSPGR) T1-Weighted Imaging (T1WI) is often used to identify fat, calcification and hemorrhage. Fast Imaging Employing Steady-State Acquisition (FIESTA) is commonly used to identify fetal structures as well as the heart and vessels. The contrast of FIESTA image is related to T1/T2 and is different from that of SSFSE. The advantages and disadvantages of these two scanning sequences for fetal imaging have not been clearly demonstrated yet. This study aimed to compare these three rapid MRI techniques (SSFSE, FIESTA, and FSPGR) for fetal MRI examinations. The image qualities and influencing factors among these three techniques were explored. A 1.5T GE Discovery 450 clinical MR scanner with an eight-channel high-resolution abdominal coil was used in this study. Twenty-five pregnant women were recruited to enroll fetal MRI examination with SSFSE, FIESTA and FSPGR scanning. Multi-oriented and multi-slice images were acquired. Afterwards, MR images were interpreted and scored by two senior radiologists. The results showed that both SSFSE and T2W-FIESTA can provide good image quality among these three rapid imaging techniques. Vessel signals on FIESTA images are higher than those on SSFSE images. The Specific Absorption Rate (SAR) of FIESTA is lower than that of the others two techniques, but it is prone to cause banding artifacts. FSPGR-T1WI renders lower Signal-to-Noise Ratio (SNR) because it severely suffers from the impact of maternal and fetal movements. The scan times for these three scanning sequences were 25 sec (T2W-SSFSE), 20 sec (FIESTA) and 18 sec (FSPGR). In conclusion, all these three rapid MR scanning sequences can produce high contrast and high spatial resolution images. The scan time can be shortened by incorporating parallel imaging techniques so that the motion artifacts caused by fetal movements can be reduced. Having good understanding of the characteristics of these three rapid MRI techniques is helpful for technologists to obtain reproducible fetal anatomy images with high quality for prenatal diagnosis.

Keywords: fetal MRI, FIESTA, FSPGR, motion artifact, SSFSE

Procedia PDF Downloads 528

Authors: Delara Kazempour, Mashallah Abasi Dezfuli, Reza Javidan

Abstract:

Video compression used for channels with limited bandwidth and storage devices has limited storage capabilities. One of the most popular approaches in video compression is the usage of different transforms. Discrete cosine transform is one of the video compression methods that have some problems such as blocking, noising and high distortion inappropriate effect in compression ratio. wavelet transform is another approach is better than cosine transforms in balancing of compression and quality but the recognizing of curve curvature is so limit. Because of the importance of the compression and problems of the cosine and wavelet transforms, the contourlet transform is most popular in video compression. In the new proposed method, we used contourlet transform in video image compression. Contourlet transform can save details of the image better than the previous transforms because this transform is multi-scale and oriented. This transform can recognize discontinuity such as edges. In this approach we lost data less than previous approaches. Contourlet transform finds discrete space structure. This transform is useful for represented of two dimension smooth images. This transform, produces compressed images with high compression ratio along with texture and edge preservation. Finally, the results show that the majority of the images, the parameters of the mean square error and maximum signal-to-noise ratio of the new method based contourlet transform compared to wavelet transform are improved but in most of the images, the parameters of the mean square error and maximum signal-to-noise ratio in the cosine transform is better than the method based on contourlet transform.

Keywords: video compression, contourlet transform, discrete cosine transform, wavelet transform

Procedia PDF Downloads 441

Authors: Riaan Kleyn

Abstract:

Mass wine production companies across the globe are provided with grapes from winegrowers that predominantly utilize mechanical harvesting machines to harvest wine grapes. Mechanical harvesting accelerates the rate at which grapes are harvested, allowing grapes to be delivered faster to meet the demands of wine cellars. The disadvantage of the mechanical harvesting method is the inclusion of material-other-than-grapes (MOG) in the harvested wine grape loads arriving at the cellar which degrades the quality of wine that can be produced. Currently, wine cellars do not have a method to determine the amount of MOG present within wine grape loads. This paper seeks to find an optimal computer vision method capable of detecting the amount of MOG within a wine grape load. A MOG detection method will encourage winegrowers to deliver MOG-free wine grape loads to avoid penalties which will indirectly enhance the quality of the wine to be produced. Traditional image segmentation methods were compared to deep learning segmentation methods based on images of wine grape loads that were captured at a wine cellar. The Mask R-CNN model with a ResNet-50 convolutional neural network backbone emerged as the optimal method for this study to determine the amount of MOG in an image of a wine grape load. Furthermore, a statistical analysis was conducted to determine how the MOG on the surface of a grape load relates to the mass of MOG within the corresponding grape load.

Keywords: computer vision, wine grapes, machine learning, machine harvested grapes

Procedia PDF Downloads 93

Authors: Tomas Trainys, Algimantas Venckauskas

Abstract:

Application of biometric features to the cryptography for human identification and authentication is widely studied and promising area of the development of high-reliability cryptosystems. Biometric cryptosystems typically are designed for patterns recognition, which allows biometric data acquisition from an individual, extracts feature sets, compares the feature set against the set stored in the vault and gives a result of the comparison. Preprocessing and fusion of biometric data are the most important phases in generating a feature vector for key generation or authentication. Fusion of biometric features is critical for achieving a higher level of security and prevents from possible spoofing attacks. The paper focuses on the tasks of initial processing and fusion of multiple representations of finger vein modality patterns. These tasks are solved by applying conventional image preprocessing methods and machine learning techniques, Convolutional Neural Network (SVM) method for image segmentation and feature extraction. An article presents a method for generating sets of biometric features from a finger vein network using several instances of the same modality. Extracted features sets were fused at the feature level. The proposed method was tested and compared with the performance and accuracy results of other authors.

Keywords: bio-cryptography, biometrics, cryptographic key generation, data fusion, information security, SVM, pattern recognition, finger vein method.

Procedia PDF Downloads 150

Authors: Musaab Riyadh, Norwati Mustapha, Dina Riyadh

Abstract:

Trajectory similarity can be defined as the cost of transforming one trajectory into another based on certain similarity method. It is the core of numerous mining tasks such as clustering, classification, and indexing. Various approaches have been suggested to measure similarity based on the geometric and dynamic properties of trajectory, the overlapping between trajectory segments, and the confined area between entire trajectories. In this article, an evaluation of these approaches has been done based on computational cost, usage memory, accuracy, and the amount of data which is needed in advance to determine its suitability to stream mining applications. The evaluation results show that the stream mining applications support similarity methods which have low computational cost and memory, single scan on data, and free of mathematical complexity due to the high-speed generation of data.

Keywords: global distance measure, local distance measure, semantic trajectory, spatial dimension, stream data mining

Procedia PDF Downloads 391

Authors: Marine Kobalava

Abstract:

Introduction. The paper substantiates the crucial role of anti-crisis PR in managing the image and reputation of companies. The critical situation caused by the Covid-19 virus in various countries of the world and the actions taken have had a significant negative impact on the image of companies and public groups. The mentioned circumstance has caused some problems for companies’ products in terms of customer demand. Accordingly, the main goal of PR has become to achieve the optimal relationship between companies and society with effective management. It should also be taken into account that the range of action of PR in crisis situations is much wider than that of advertising. In the paper, Public Relations is evaluated as a determining factor of the companies' prestige, its reliability, which has a decisive effect on the goodwill, trust, and general reputation of the public towards the company. The purpose of the study is to reveal the challenges of anti-crisis PR in Georgian companies and to develop recommendations on effective management mechanisms. Methodologies. Analysis, induction, synthesis, and other methods are used in the paper; Matrix and SWOT analysis are constructed. Ways of establishing and implementing an anti-crisis PR system in companies are proposed. The main aspects of anti-crisis management are identified by using the matrix of the choice of diversification strategy of the companies' activities, the possibilities of making adequate decisions using PR are studied according to the characteristics of the companies' activities and priority directions. Conclusion. The paper draws conclusions on modern problems of anti-crisis PR, offers recommendations on ways to solve it through PR strategies.

Keywords: anti-crisis PR, effective management, company, PR strategy

Procedia PDF Downloads 79

Authors: Bakhtiar Ul Haq, R. Ahmed, A. Shaari, Mazmira Binti Mohamed, Nisar Ali

Abstract:

The capability of exploiting the electronic charge and spin properties simultaneously in a single material has made diluted magnetic semiconductors (DMS) remarkable in the field of spintronics. We report the designing of DMS based on zinc-blend ZnO doped with Cr impurity. The full potential linearized augmented plane wave plus local orbital FP-L(APW+lo) method in density functional theory (DFT) has been adapted to carry out these investigations. For treatment of exchange and correlation energy, generalized gradient approximations have been used. Introducing Cr atoms in the matrix of ZnO has induced strong magnetic moment with ferromagnetic ordering at stable ground state. Cr:ZnO was found to favor the short range magnetic interaction that reflect the tendency of Cr clustering. The electronic structure of ZnO is strongly influenced in the presence of Cr impurity atoms where impurity bands appear in the band gap.

Keywords: ZnO, density functional theory, diluted agnetic semiconductors, ferromagnetic materials, FP-L(APW+lo)

Procedia PDF Downloads 424

Authors: Mais Nijim, Rama Devi Chennuboyina, Waseem Al Aqqad

Abstract:

Advances in spatial and spectral resolution of satellite images have led to tremendous growth in large image databases. The data we acquire through satellites, radars and sensors consists of important geographical information that can be used for remote sensing applications such as region planning, disaster management. Spatial data classification and object recognition are important tasks for many applications. However, classifying objects and identifying them manually from images is a difficult task. Object recognition is often considered as a classification problem, this task can be performed using machine-learning techniques. Despite of many machine-learning algorithms, the classification is done using supervised classifiers such as Support Vector Machines (SVM) as the area of interest is known. We proposed a classification method, which considers neighboring pixels in a region for feature extraction and it evaluates classifications precisely according to neighboring classes for semantic interpretation of region of interest (ROI). A dataset has been created for training and testing purpose; we generated the attributes by considering pixel intensity values and mean values of reflectance. We demonstrated the benefits of using knowledge discovery and data-mining techniques, which can be on image data for accurate information extraction and classification from high spatial resolution remote sensing imagery.

Keywords: remote sensing, object recognition, classification, data mining, waterbody identification, feature extraction

Procedia PDF Downloads 334

Authors: Sardorbek Numonov, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Dongeun Choi, Byung-Woo Hong

Abstract:

The detection of curvilinear structures often plays an important role in the analysis of images. In particular, it is considered as a crucial step for the diagnosis of chronic respiratory diseases to localize the fissures in chest CT imagery where the lung is divided into five lobes by the fissures that are characterized by linear features in appearance. However, the characteristic linear features for the fissures are often shown to be subtle due to the high intensity variability, pathological deformation or image noise involved in the imaging procedure, which leads to the uncertainty in the quantification of anatomical or functional properties of the lung. Thus, it is desired to enhance the linear features present in the chest CT images so that the distinctiveness in the delineation of the lobe is improved. We propose a recursive diffusion process that prefers coherent features based on the analysis of structure tensor in an anisotropic manner. The local image features associated with certain scales and directions can be characterized by the eigenanalysis of the structure tensor that is often regularized via isotropic diffusion filters. However, the isotropic diffusion filters involved in the computation of the structure tensor generally blur geometrically significant structure of the features leading to the degradation of the characteristic power in the feature space. Thus, it is required to take into consideration of local structure of the feature in scale and direction when computing the structure tensor. We apply an anisotropic diffusion in consideration of scale and direction of the features in the computation of the structure tensor that subsequently provides the geometrical structure of the features by its eigenanalysis that determines the shape of the anisotropic diffusion kernel. The recursive application of the anisotropic diffusion with the kernel the shape of which is derived from the structure tensor leading to the anisotropic scale-space where the geometrical features are preserved via the eigenanalysis of the structure tensor computed from the diffused image. The recursive interaction between the anisotropic diffusion based on the geometry-driven kernels and the computation of the structure tensor that determines the shape of the diffusion kernels yields a scale-space where geometrical properties of the image structure are effectively characterized. We apply our recursive anisotropic diffusion algorithm to the detection of curvilinear structure in the chest CT imagery where the fissures present curvilinear features and define the boundary of lobes. It is shown that our algorithm yields precise detection of the fissures while overcoming the subtlety in defining the characteristic linear features. The quantitative evaluation demonstrates the robustness and effectiveness of the proposed algorithm for the detection of fissures in the chest CT in terms of the false positive and the true positive measures. The receiver operating characteristic curves indicate the potential of our algorithm as a segmentation tool in the clinical environment. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: anisotropic diffusion, chest CT imagery, chronic respiratory disease, curvilinear structure, fissure detection, structure tensor

Procedia PDF Downloads 231

Authors: Soiduate Ogoye-Atanga

Abstract:

During funeral ceremonies, it has become common for attendees to jostle for burial programs in some southern Nigerian towns. Beginning from ordinary typewritten text only sheets of paper in the 1980s to their current digitally formatted multicolor magazine style, burial programs continue to be collected and kept in homes where they remain as archival documents of family photo histories and as a veritable form of leveraging family status and visibility in a social economy through the inclusion of lots of choreographically arranged photographs and text. The biographical texts speak of idealized and often lofty and aestheticized accomplishments of deceased peoples, which are often corroborated by an accompanying section of tributes from first the immediate family members, and then from affiliations as well as organizations deceased people belonged, in the form of scanned letterheaded corporate tributes. Others speak of modest biographical texts when the deceased accomplished little. Usually, in majority of the cases, the display of photographs and text in these programs follow a trajectory of historical compartmentalization of the deceased, beginning from parentage to the period of youth, occupation, retirement, and old age as the case may be, which usually drives from black and white historical photographs to the color photography of today. This compartmentalization follows varied models but is designed to show the deceased in varying activities during his lifetime. The production of these programs ranges from the extremely expensive and luscious full colors of near fifty-eighty pages to bland and very simplified low-quality few-page editions in a single color and no photographs, except on the cover. Cost and quality, therefore, become determinants of varying family status and social visibility. By a critical selection of photographs and text, family members construct an idealized image of deceased people and themselves, concentrating on mutuality based on appropriate sartorial selections, socioeconomic grade, and social temperaments that are framed to corroborate the public’s perception of them. Burial magazines, therefore, serve purposes beyond their primary use; they symbolize an orchestrated social site for image-making and the validation of the social status of families, shaped by prior family histories.

Keywords: biographical texts, burial programs, compartmentalization, magazine, multicolor, photo-histories, social status

Procedia PDF Downloads 188

Authors: A. M. John-Otumu, M. M. Rahman, M. C. Onuoha, E. P. Ojonugwa

Abstract:

A Convolutional Neural Network (CNN) model for predicting Lassa fever was built using Python 3.8.0 programming language, alongside Keras 2.2.4 and TensorFlow 2.6.1 libraries as the development environment in order to reduce the current high risk of Lassa fever in West Africa, particularly in Nigeria. The study was prompted by some major flaws in existing conventional laboratory equipment for diagnosing Lassa fever (RT-PCR), as well as flaws in AI-based techniques that have been used for probing and prognosis of Lassa fever based on literature. There were 15,679 blood smear microscopic image datasets collected in total. The proposed model was trained on 70% of the dataset and tested on 30% of the microscopic images in avoid overfitting. A 3x3x3 convolution filter was also used in the proposed system to extract features from microscopic images. The proposed CNN-based model had a recall value of 96%, a precision value of 93%, an F1 score of 95%, and an accuracy of 94% in predicting and accurately classifying the images into clean or infected samples. Based on empirical evidence from the results of the literature consulted, the proposed model outperformed other existing AI-based techniques evaluated. If properly deployed, the model will assist physicians, medical laboratory scientists, and patients in making accurate diagnoses for Lassa fever cases, allowing the mortality rate due to the Lassa fever virus to be reduced through sound decision-making.

Keywords: artificial intelligence, ANN, blood smear, CNN, deep learning, Lassa fever

Procedia PDF Downloads 118

Authors: Khalid. M. O. Elmabrok, M. L. Burby, G. G. Nasr

Abstract:

Flow instability during gas lift operation is caused by three major phenomena – the density wave oscillation, the casing heading pressure and the flow perturbation within the two-phase flow region. This paper focuses on the causes and the effect of flow instability during gas lift operation and suggests ways to control it in order to maximise productivity during gas lift operations. A laboratory-scale two-phase flow system to study the effects of flow perturbation was designed and built. The apparatus is comprised of a 2 m long by 66 mm ID transparent PVC pipe with air injection point situated at 0.1 m above the base of the pipe. This is the point where stabilised bubbles were visibly clear after injection. Air is injected into the water filled transparent pipe at different flow rates and pressures. The behavior of the different sizes of the bubbles generated within the two-phase region was captured using a digital camera and the images were analysed using the advanced image processing package. It was observed that the average maximum bubbles sizes increased with the increase in the length of the vertical pipe column from 29.72 to 47 mm. The increase in air injection pressure from 0.5 to 3 bars increased the bubble sizes from 29.72 mm to 44.17 mm and then decreasing when the pressure reaches 4 bars. It was observed that at higher bubble velocity of 6.7 m/s, larger diameter bubbles coalesce and burst due to high agitation and collision with each other. This collapse of the bubbles causes pressure drop and reverse flow within two phase flow and is the main cause of the flow instability phenomena.

Keywords: gas lift instability, bubbles forming, bubbles collapsing, image processing

Procedia PDF Downloads 419

Authors: Ayesha Ashfaq, Muhammad Ajmal Ashfaq

Abstract:

It has been assumed that metaphorical and symbolic contests are waged with metaphors, captions, and signs in political cartoons that play a significant role in image construction of political actors, situations or events in the political arena. This paper is an effort to explore the metaphorical devices in political cartoons related to the political confrontation in Pakistan between the ruling party Pakistan Muslim League Nawaz (PMLN) and opposition parties especially after Panama leaks. For this purpose, political cartoons sketched by five renowned political cartoonists on the basis of their belongings to the most highly circulated mainstream English newspapers of Pakistan and their professional experiences in their genre, were selected. The cartoons were analyzed through the Barthes’s model of Semiotics under the umbrella of the first level of agenda setting theory ‘framing’. It was observed that metaphorical devices in political cartoons are one of the key weapons of cartoonists’ armory. These devices are used to attack the candidates and contribute to the image and character building. It was found that all the selected political cartoonists used different forms of metaphors including situational metaphors and embodying metaphors. Not only the physical stature but also the debates and their activities were depicted metaphorically in the cartoons that create the scenario of comparison between the cartoons and their real political confrontation. It was examined that both forms of metaphors shed light on cartoonist’s perception and newspaper’s policy about political candidates, political parties and particular events. In addition, it was found that zoomorphic metaphors and metaphors of diminishments were also predominantly used to depict the conflict between two said political actors.

Keywords: metaphor, Panama leaks, political cartoons, political communication

Procedia PDF Downloads 305

Authors: Sara Rejeb, Catherine Duveau, Tabea Rebafka

Abstract:

To monitor the production process of turbofan aircraft engines, multiple measurements of various geometrical parameters are systematically recorded on manufactured parts. Engine parts are subject to extremely high standards as they can impact the performance of the engine. Therefore, it is essential to analyze these databases to better understand the influence of the different parameters on the engine's performance. Self-organizing maps are unsupervised neural networks which achieve two tasks simultaneously: they visualize high-dimensional data by projection onto a 2-dimensional map and provide clustering of the data. This technique has become very popular for data exploration since it provides easily interpretable results and a meaningful global view of the data. As such, self-organizing maps are usually applied to aircraft engine condition monitoring. As databases in this field are huge and complex, they naturally contain multiple missing entries for various reasons. The classical Kohonen algorithm to compute self-organizing maps is conceived for complete data only. A naive approach to deal with partially observed data consists in deleting items or variables with missing entries. However, this requires a sufficient number of complete individuals to be fairly representative of the population; otherwise, deletion leads to a considerable loss of information. Moreover, deletion can also induce bias in the analysis results. Alternatively, one can first apply a common imputation method to create a complete dataset and then apply the Kohonen algorithm. However, the choice of the imputation method may have a strong impact on the resulting self-organizing map. Our approach is to address simultaneously the two problems of computing a self-organizing map and imputing missing values, as these tasks are not independent. In this work, we propose an extension of self-organizing maps for partially observed data, referred to as missSOM. First, we introduce a criterion to be optimized, that aims at defining simultaneously the best self-organizing map and the best imputations for the missing entries. As such, missSOM is also an imputation method for missing values. To minimize the criterion, we propose an iterative algorithm that alternates the learning of a self-organizing map and the imputation of missing values. Moreover, we develop an accelerated version of the algorithm by entwining the iterations of the Kohonen algorithm with the updates of the imputed values. This method is efficiently implemented in R and will soon be released on CRAN. Compared to the standard Kohonen algorithm, it does not come with any additional cost in terms of computing time. Numerical experiments illustrate that missSOM performs well in terms of both clustering and imputation compared to the state of the art. In particular, it turns out that missSOM is robust to the missingness mechanism, which is in contrast to many imputation methods that are appropriate for only a single mechanism. This is an important property of missSOM as, in practice, the missingness mechanism is often unknown. An application to measurements on one type of part is also provided and shows the practical interest of missSOM.

Keywords: imputation method of missing data, partially observed data, robustness to missingness mechanism, self-organizing maps

Procedia PDF Downloads 149

Authors: Mohammad Rawashdeh, Mark McEntee, Maha Zaitoun, Mostafa Abdelrahman, Patrick Brennan, Haytham Alewaidat, Sarah Lewis, Charbel Saade

Abstract:

Despite the tremendous advancements that have been generated by Computed Tomography (CT) in the field of diagnosis, concerns have been raised about the potential cancer induction risk from CT because of the exponentially increased use of it in medicine. This study aims at investigating the application and knowledge of practicing radiographers in Jordan about CT radiation. In order to collect the primary data of this study, a questionnaire was designed and distributed by social media using a snow-balling sampling method. The respondents (n=54) have answered 36 questions including the questions about their demographic information, knowledge about Diagnostic Reference Levels (DRLs), CT exposure and adaptation of pediatric patients exposure. The educational level of the respondents was either at a diploma degree (35.2%) or bachelor (64.8%). The results of this study have indicated a good level of general knowledge between radiographers about the relationship between image quality, exposure parameters, and patient dose. The level of knowledge related to DRL was poor where less than 7.4 percent of the sample members were able to give specific values for a number of common anatomical fields, including abdomen, brain, and chest. Overall, Jordanian radiographers need to gain more knowledge about the expected levels of the dose when applying good practice. Additional education on DRL or DRL inclusion in educational programs is highlighted.

Keywords: computed tomography, CT scan, DRLs, exposure parameters, image quality, radiation dose

Procedia PDF Downloads 141