Search results for: automatic classification

Authors: Bjorn-Ole Meyer, Dominik Marti, Peter E. Andersen

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A multimodal imaging system, combining spectrally resolved multiphoton microscopy (MPM) and optical coherence microscopy (OCM) is demonstrated. MPM and OCM are commonly integrated into multimodal imaging platforms to combine functional and morphological information. The MPM signals, such as two-photon fluorescence emission (TPFE) and signals created by second harmonic generation (SHG) are biomarkers which exhibit information on functional biological features such as the ratio of pyridine nucleotide (NAD(P)H) and flavin adenine dinucleotide (FAD) in the classification of cancerous tissue. While the spectrally resolved imaging allows for the study of biomarkers, using a spectrometer as a detector limits the imaging speed of the system significantly. To overcome those limitations, an OCM setup was added to the system, which allows for fast acquisition of structural information. Thus, after rapid imaging of larger specimens, navigation within the sample is possible. Subsequently, distinct features can be selected for further investigation using MPM. Additionally, by probing a different contrast, complementary information is obtained, and different biomarkers can be investigated. OCM images of tissue and cell samples are obtained, and distinctive features are evaluated using MPM to illustrate the benefits of the system.

Keywords: optical coherence microscopy, multiphoton microscopy, multimodal imaging, two-photon fluorescence emission

Procedia PDF Downloads 507

Authors: T. S. Ijimdiya, K. J. Osinubi

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This study examines the influence of compactive efforts on hydraulic conductivity behaviour of compacted black cotton soil treated with bagasse ash which is necessary in assessing the performance of the soil - bagasse ash mixture for use as a suitable barrier material in waste containment application. Black cotton soil treated with up to 12% bagasse ash (obtained from burning the fibrous residue from the extraction of sugar juice from sugarcane) by dry weight of soil for use in waste containment application. The natural soil classifies as A-7-6 or CH in accordance with the AASHTO and the Unified Soil Classification System, respectively. The treated soil samples were prepared at molding water contents of -2, 0, +2, and +4 % of optimum moisture contents and compacted using four compactive efforts of Reduced British Standard Light (RBSL), British Standard light (BSL), West African Standard (WAS) and British Standard Heavy (BSH). The results obtained show that hydraulic conductivity decreased with increase in bagasse ash content, moulding water content and compaction energy.

Keywords: bagasse ash treatment, black cotton soil, hydraulic conductivity, moulding water contents, compactive efforts

Procedia PDF Downloads 431

Authors: Kashika P. H.

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The development of computer vision systems has been greatly aided by the efficient and precise detection of images and videos. Although the ability to recognize and comprehend images is a strength of the human brain, employing technology to tackle this issue is exceedingly challenging. In the past few years, the use of Deep Learning algorithms to treat object detection has dramatically expanded. One of the key issues in the realm of image recognition is the recognition and detection of certain notable people from randomly acquired photographs. Face recognition uses a way to identify, assess, and compare faces for a variety of purposes, including user identification, user counting, and classification. With the aid of an accessible deep learning-based API, this article intends to recognize various faces of people and their facial descriptors more accurately. The purpose of this study is to locate suitable individuals and deliver accurate information about them by using the Amazon Rekognition system to identify a specific human from a vast image dataset. We have chosen the Amazon Rekognition system, which allows for more accurate face analysis, face comparison, and face search, to tackle this difficulty.

Keywords: Amazon rekognition, API, deep learning, computer vision, face detection, text detection

Procedia PDF Downloads 102

Authors: Dedah Ahmed Babou, Nicolas Bez

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The Min-Max autocorrelation factor (MAF) approach makes it possible to express in a space formed by spatially independent factors, spatiotemporal observations. These factors are ordered in decreasing order of spatial autocorrelation. The starting observations are thus expressed in the space formed by these factors according to temporal coordinates. Each vector of temporal coefficients expresses the temporal evolution of the weight of the corresponding factor. Applying this approach has enabled us to achieve the following results: (i) Define a spatially orthogonal space in which the projections of the raw data are determined; (ii) Define a limit threshold for the factors with the strongest structures in order to analyze the weight, and the temporal evolution of these different structures (iii) Study the correlation between the temporal evolution of the persistent spatial structures and that of the observed average abundance (iv) Propose prototypes of campaigns reflecting a high vs. low abundance (v) Propose a classification of campaigns that highlights seasonal and/or temporal similarities. These results were obtained by analyzing the octopus yield during the scientific campaigns of the oceanographic vessel Al Awam during the period 1989-2017 in the Mauritanian exclusive economic zone.

Keywords: spatiotemporal , autocorrelation, kriging, variogram, Octopus vulgaris

Procedia PDF Downloads 143

Authors: Mohammad I. Hossain, Omar F. Hamim

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This paper presents a case study on a pile integrity test for assessing the integrity of piles as well as a physical dimension (e.g., cross-sectional area, length), continuity, and consistency of the pile materials. The recent boom in the socio-economic condition of Bangladesh has given rise to the building of high-rise commercial and residential infrastructures. The advantage of the pile integrity test lies in the fact that it is possible to get an approximate indication regarding the quality of the sub-structure before commencing the construction of the super-structure. This paper aims at providing a classification of cast-in-situ piles based on characteristic reflectograms obtained using the Sonic Integrity Testing program for the sub-soil condition of Narayanganj, Bangladesh. The piles have been classified as 'Pile Type-1', 'Pile Type-2', 'Pile Type-3', 'Pile type-4', 'Pile Type-5' or 'Pile Type-6' from the visual observations of reflections from the generated stress waves by striking the pile head with a handheld hammer. With respect to construction quality and integrity, piles have been further classified into three distinct categories, i.e., satisfactory, may be satisfactory, and unsatisfactory.

Keywords: cast-in-situ piles, characteristic reflectograms, pile integrity test, sonic integrity testing program

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Authors: Maryam Heidari, James H. Jones Jr.

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Millions of online posts about different topics and products are shared on popular social media platforms. One use of this content is to provide crowd-sourced information about a specific topic, event or product. However, this use raises an important question: what percentage of information available through these services is trustworthy? In particular, might some of this information be generated by a machine, i.e., a bot, instead of a human? Bots can be, and often are, purposely designed to generate enough volume to skew an apparent trend or position on a topic, yet the consumer of such content cannot easily distinguish a bot post from a human post. In this paper, we introduce a model for social media bot detection which uses Bidirectional Encoder Representations from Transformers (Google Bert) for sentiment classification of tweets to identify topic-independent features. Our use of a Natural Language Processing approach to derive topic-independent features for our new bot detection model distinguishes this work from previous bot detection models. We achieve 94\% accuracy classifying the contents of data as generated by a bot or a human, where the most accurate prior work achieved accuracy of 92\%.

Keywords: bot detection, natural language processing, neural network, social media

Procedia PDF Downloads 111

Authors: Qing Li, Maiko Kobayashi, Shigeyoshi Kumeda, Hiroko Ochiai, Toshiya Ochiai, Takashi Miura, Takahide Kagawa, Michiko Imai, Toshiaki Otsuka, Tomoyuki Kawada

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In the present study, we investigated the effects of a forest bathing program on cardiovascular and metabolic parameters. Nineteen healthy male subjects (mean age: 51.3 ± 8.8 years) were selected after obtaining informed consent. These subjects took day trips to a forest park named Akasawa Shizen Kyuyourin, Agematsu, Nagano Prefecture (situated in central Japan), and to an urban area of Nagano Prefecture as a control in August 2015. On both trips, they walked 2.6 km for 80 min each in the morning and afternoon on Saturdays. Blood and urine were sampled in the morning before and after each trip. Cardiovascular and metabolic parameters were measured. Blood pressure and pulse rate were measured by an ambulatory automatic blood pressure monitor. The Japanese version of the profile of mood states (POMS) test was conducted before, during and after the trips. Ambient temperature and humidity were monitoring during the trips. The forest bathing program significantly reduced pulse rate, and significantly increased the score for vigor and decreased the scores for depression, fatigue, and confusion in the POMS test. The levels of urinary noradrenaline and dopamine after forest bathing were significantly lower than those after urban area walking, suggesting the relaxing effect of the forest bathing program. The level of adiponectin in serum after the forest bathing program was significantly greater than that after urban area walking. There was no significant difference in blood pressure between forest and urban area trips during the trips.

Keywords: ambient temperature, blood pressure, forest bathing, forest therapy, human health, POMS, pulse rate

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Authors: Usiobaifo Agharese Rosemary, Osaseri Roseline Oghogho

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Diabetes is a complex group of disease with a variety of causes; it is a disorder of the body metabolism in the digestion of carbohydrates food. The application of machine learning in the field of medical diagnosis has been the focus of many researchers and the use of recognition and classification model as a decision support tools has help the medical expert in diagnosis of diseases. Considering the large volume of medical data which require special techniques, experience, and high diagnostic skill in the diagnosis of diseases, the application of an artificial intelligent system to assist medical personnel in order to enhance their efficiency and accuracy in diagnosis will be an invaluable tool. In this study will propose a diabetes diagnosis model using rough set and K-nearest Neighbor classifier algorithm. The system consists of two modules: the feature extraction module and predictor module, rough data set is used to preprocess the attributes while K-nearest neighbor classifier is used to classify the given data. The dataset used for this model was taken for University of Benin Teaching Hospital (UBTH) database. Half of the data was used in the training while the other half was used in testing the system. The proposed model was able to achieve over 80% accuracy.

Keywords: classifier algorithm, diabetes, diagnostic model, machine learning

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Authors: Nijat S. İmamverdiyev

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The transition to renewable energy sources has become a critical component of global efforts to mitigate climate change and promote sustainable development. However, the deployment of renewable energy technologies can also have significant impacts on ecosystems and the services they provide, such as carbon sequestration, soil fertility, water quality, and biodiversity. It also highlights the potential co-benefits of renewable energy deployment for ecosystem services, such as reducing greenhouse gas emissions and improving air and water quality. Renewable energy sources, such as wind, solar, hydro, and biomass, are increasingly being used to meet the world's energy needs due to their environmentally friendly nature and the desire to reduce greenhouse gas emissions. However, the expansion of renewable energy infrastructure can also impact ecosystem services, which are the benefits that humans derive from nature, such as clean water, air, and food. This geographical assessment aims to evaluate the relationship between renewable energy infrastructure and ecosystem services. Here, also explores potential solutions to mitigate the negative effects of renewable energy infrastructure on ecosystem services, such as the use of ecological compensation measures, biodiversity-friendly design of renewable energy infrastructure, and stakeholder involvement in decision-making processes.

Keywords: renewable energy, solar energy, climate change, energy production

Procedia PDF Downloads 58

Authors: Wei-Jong Yang, Yu-Siang Su, Pau-Choo Chung, Jar-Ferr Yang

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Moving object detection (MOD) is an important issue in advanced driver assistance systems (ADAS). There are two important moving objects, pedestrians and scooters in ADAS. In real-world systems, there exist two important challenges for MOD, including the computational complexity and the detection accuracy. The histogram of oriented gradient (HOG) features can easily detect the edge of object without invariance to changes in illumination and shadowing. However, to reduce the execution time for real-time systems, the image size should be down sampled which would lead the outlier influence to increase. For this reason, we propose the histogram of uniformly-oriented gradient (HUG) features to get better accurate description of the contour of human body. In the testing phase, the support vector machine (SVM) with linear kernel function is involved. Experimental results show the correctness and effectiveness of the proposed method. With SVM classifiers, the real testing results show the proposed HUG features achieve better than classification performance than the HOG ones.

Keywords: moving object detection, histogram of oriented gradient, histogram of uniformly-oriented gradient, linear support vector machine

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Authors: Zhe Li, Aihua Cai

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Aiming at the problem of high-density clutter and interference affecting radar detection target track initiation in ECM and complex radar mission, the traditional radar target track initiation method has been difficult to adapt. To this end, we propose a multi-algorithm fusion learning track initiation algorithm, which transforms the track initiation problem into a true-false track discrimination problem, and designs an algorithm based on 1DCNN(One-Dimensional CNN)combined with Bi-LSTM (Bi-Directional Long Short-Term Memory )for fusion classification. The experimental dataset consists of real trajectories obtained from a certain type of three-coordinate radar measurements, and the experiments are compared with traditional trajectory initiation methods such as rule-based method, logical-based method and Hough-transform-based method. The simulation results show that the overall performance of the multi-algorithm fusion learning track initiation algorithm is significantly better than that of the traditional method, and the real track initiation rate can be effectively improved under high clutter density with the average initiation time similar to the logical method.

Keywords: track initiation, multi-algorithm fusion, 1DCNN, Bi-LSTM

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Authors: K. Shiba, T. Kobayashi, T. Kaburagi, Y. Kurihara

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Elderly population in the world is increasing, and consequently, their nursing burden is also increasing. In such situations, monitoring and evaluating their daily action facilitates efficient nursing care. Especially, we focus on an unconscious activity during sleep, i.e. turnover. Monitoring turnover during sleep is essential to evaluate various conditions related to sleep. Bedsores are considered as one of the monitoring conditions. Changing patient’s posture every two hours is required for caregivers to prevent bedsore. Herein, we attempt to develop an unconstrained nocturnal monitoring system using a sensing device based on piezoelectric ceramics that can detect the vibrations owing to human body movement on the bed. In the proposed method, in order to construct a multi-points sensing, we placed two sensing devices under the right and left legs at the head-side of an ordinary bed. Using this equipment, when a subject lies on the bed, feature is calculated from the output voltages of the sensing devices. In order to evaluate our proposed method, we conducted an experiment with six healthy male subjects. Consequently, the period during which turnover occurs can be correctly classified as the turnover period with 100% accuracy.

Keywords: turnover, piezoelectric ceramics, multi-points sensing, unconstrained monitoring system

Procedia PDF Downloads 192

Authors: Rony Reátegui, Cesar Chácara, Benjamin Castañeda, Rafael Aguilar

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Nowadays, heritage building information modeling (HBIM) is considered an efficient tool to represent and manage information of cultural heritage (CH). The basis of this tool relies on a 3D model generally obtained from a cloud-to-BIM procedure. There are different methods to create an HBIM model that goes from manual modeling based on the point cloud to the automatic detection of shapes and the creation of objects. The selection of these methods depends on the desired level of development (LOD), level of information (LOI), grade of generation (GOG), as well as on the availability of commercial software. This paper presents the 3D modeling of a stone masonry chapel using Recap Pro, Revit, and Dynamo interface following a three-step methodology. The first step consists of the manual modeling of simple structural (e.g., regular walls, columns, floors, wall openings, etc.) and architectural (e.g., cornices, moldings, and other minor details) elements using the point cloud as reference. Then, Dynamo is used for generative modeling of complex structural elements such as vaults, infills, and domes. Finally, semantic information (e.g., materials, typology, state of conservation, etc.) and pathologies are added within the HBIM model as text parameters and generic models families, respectively. The application of this methodology allows the documentation of CH following a relatively simple to apply process that ensures adequate LOD, LOI, and GOG levels. In addition, the easy implementation of the method as well as the fact of using only one BIM software with its respective plugin for the scan-to-BIM modeling process means that this methodology can be adopted by a larger number of users with intermediate knowledge and limited resources since the BIM software used has a free student license.

Keywords: cloud-to-BIM, cultural heritage, generative modeling, HBIM, parametric modeling, Revit

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Authors: Van Thinh Bui, Anh Tuan Tran, Quoc Viet Ngo, The Bao Pham

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Video retrieval is a searching problem on videos or clips based on content in which they are relatively close to an input image or video. The application of this retrieval consists of selecting video in a folder or recognizing a human in security camera. However, some recent approaches have been in challenging problem due to the diversity of video types, frame transitions and camera positions. Besides, that an appropriate measures is selected for the problem is a question. In order to overcome all obstacles, we propose a content-based video retrieval system in some main steps resulting in a good performance. From a main video, we process extracting keyframes and principal objects using Segmentation of Aggregating Superpixels (SAS) algorithm. After that, Speeded Up Robust Features (SURF) are selected from those principal objects. Then, the model “Bag-of-words” in accompanied by SVM classification are applied to obtain the retrieval result. Our system is performed on over 300 videos in diversity from music, history, movie, sports, and natural scene to TV program show. The performance is evaluated in promising comparison to the other approaches.

Keywords: video retrieval, principal objects, keyframe, segmentation of aggregating superpixels, speeded up robust features, bag-of-words, SVM

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Authors: Amira Ben Rabeh, Faouzi Benzarti, Hamid Amiri, Mouna Bouaziz

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Alzheimer is a disease that affects the brain. It causes degeneration of nerve cells (neurons) and in particular cells involved in memory and intellectual functions. Early diagnosis of Alzheimer Diseases (AD) raises ethical questions, since there is, at present, no cure to offer to patients and medicines from therapeutic trials appear to slow the progression of the disease as moderate, accompanying side effects sometimes severe. In this context, analysis of medical images became, for clinical applications, an essential tool because it provides effective assistance both at diagnosis therapeutic follow-up. Computer Assisted Diagnostic systems (CAD) is one of the possible solutions to efficiently manage these images. In our work; we proposed an application to detect Alzheimer’s diseases. For detecting the disease in early stage we used the three sections: frontal to extract the Hippocampus (H), Sagittal to analysis the Corpus Callosum (CC) and axial to work with the variation features of the Cortex(C). Our method of classification is based on Support Vector Machine (SVM). The proposed system yields a 90.66% accuracy in the early diagnosis of the AD.

Keywords: Alzheimer Diseases (AD), Computer Assisted Diagnostic(CAD), hippocampus, Corpus Callosum (CC), cortex, Support Vector Machine (SVM)

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Authors: Eric Muñoz de la Torre, Julián González Trinidad, Efrén González Ramírez

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Rain varies greatly in its duration, intensity, and spatial coverage, it is important to have sub-daily rainfall data for various applications, including risk prevention. However, the ground measurements are limited by the low and irregular density of rain gauges. An alternative to this problem are the Satellite Precipitation Products (SPPs) that use passive microwave and infrared sensors to estimate rainfall, as IMERG, however, these SPPs have to be validated before their application. The aim of this study is to evaluate the performance of the IMERG: Integrated Multi-satellitE Retrievals for Global Precipitation Measurament final run V06B SPP in a semi-arid region of Mexico, using 4 automatic rain gauges (pluviographs) sub-daily data of October 2019 and June to September 2021, using the Minimum inter-event Time (MIT) criterion to separate unique rain events with a dry period of 10 hrs. for the purpose of evaluating the rainfall properties (depth, duration and intensity). Point to pixel analysis, continuous, categorical, and volumetric statistical metrics were used. Results show that IMERG is capable to estimate the rainfall depth with a slight overestimation but is unable to identify the real duration and intensity of the rain events, showing large overestimations and underestimations, respectively. The study zone presented 80 to 85 % of convective rain events, the rest were stratiform rain events, classified by the depth magnitude variation of IMERG pixels and pluviographs. IMERG showed poorer performance at detecting the first ones but had a good performance at estimating stratiform rain events that are originated by Cold Fronts.

Keywords: IMERG, rainfall, rain gauge, remote sensing, statistical evaluation

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Authors: Radhika Ranjan Roy

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Deployment of machine learning (ML)/deep learning (DL) algorithms for cyberattack detection in operational communications networks (wireless and/or wire-line) is being delayed because of low-performance parameters (e.g., recall, precision, and f₁-score). If datasets become imbalanced, which is the usual case for communications networks, the performance tends to become worse. Complexities in handling reducing dimensions of the feature sets for increasing performance are also a huge problem. Mahalanobis algorithms have been widely applied in scientific research because Mahalanobis distance metric learning is a successful framework. In this paper, we have investigated the Mahalanobis binary classifier algorithm for increasing cyberattack detection performance over communications networks as a proof of concept. We have also found that high-dimensional information in intermediate features that are not utilized as much for classification tasks in ML/DL algorithms are the main contributor to the state-of-the-art of improved performance of the Mahalanobis method, even for imbalanced and sparse datasets. With no feature reduction, MD offers uniform results for precision, recall, and f₁-score for unbalanced and sparse NSL-KDD datasets.

Keywords: Mahalanobis distance, machine learning, deep learning, NS-KDD, local intrinsic dimensionality, chi-square, positive semi-definite, area under the curve

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Authors: Tomyslav Sledevič, Artūras Serackis, Gintautas Tamulevičius, Dalius Navakauskas

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This paper presents a comparative evaluation of features extraction algorithm for a real-time isolated word recognition system based on FPGA. The Mel-frequency cepstral, linear frequency cepstral, linear predictive and their cepstral coefficients were implemented in hardware/software design. The proposed system was investigated in the speaker-dependent mode for 100 different Lithuanian words. The robustness of features extraction algorithms was tested recognizing the speech records at different signals to noise rates. The experiments on clean records show highest accuracy for Mel-frequency cepstral and linear frequency cepstral coefficients. For records with 15 dB signal to noise rate the linear predictive cepstral coefficients give best result. The hard and soft part of the system is clocked on 50 MHz and 100 MHz accordingly. For the classification purpose, the pipelined dynamic time warping core was implemented. The proposed word recognition system satisfies the real-time requirements and is suitable for applications in embedded systems.

Keywords: isolated word recognition, features extraction, MFCC, LFCC, LPCC, LPC, FPGA, DTW

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Authors: M. M. Qasaymeh, M. A. Khodeir

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Subspace channel estimation methods have been studied widely. It depends on subspace decomposition of the covariance matrix to separate signal subspace from noise subspace. The decomposition normally is done by either Eigenvalue Decomposition (EVD) or Singular Value Decomposition (SVD) of the Auto-Correlation matrix (ACM). However, the subspace decomposition process is computationally expensive. In this paper, the multipath channel estimation problem for a Slow Frequency Hopping (SFH) system using noise space based method is considered. An efficient method to estimate multipath the time delays basically is proposed, by applying MUltiple Signal Classification (MUSIC) algorithm which used the null space extracted by the Rank Revealing LU factorization (RRLU). The RRLU provides accurate information about the rank and the numerical null space which make it a valuable tool in numerical linear algebra. The proposed novel method decreases the computational complexity approximately to the half compared with RRQR methods keeping the same performance. Computer simulations are also included to demonstrate the effectiveness of the proposed scheme.

Keywords: frequency hopping, channel model, time delay estimation, RRLU, RRQR, MUSIC, LS-ESPRIT

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Authors: Nathenal Thomas Lambamo

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

Keywords: septoria, leaf rust, deep learning, CNN

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Authors: Marwa Qaraqe, Muhammad Ismail, Erchin Serpedin

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This paper presents a novel architecture for a patient-specific epileptic seizure onset detector using scalp electroencephalography (EEG). The proposed architecture is based on the decision fusion calculated from energy and neural synchronization related features. Specifically, one level of the detector calculates the condition number (CN) of an EEG matrix to evaluate the amount of neural synchronization present within the EEG channels. On a parallel level, the detector evaluates the energy contained in four EEG frequency subbands. The information is then fed into two independent (parallel) classification units based on support vector machines to determine the onset of a seizure event. The decisions from the two classifiers are then combined together according to two fusion techniques to determine a global decision. Experimental results demonstrate that the detector based on the AND fusion technique outperforms existing detectors with a sensitivity of 100%, detection latency of 3 seconds, while it achieves a 2:76 false alarm rate per hour. The OR fusion technique achieves a sensitivity of 100%, and significantly improves delay latency (0:17 seconds), yet it achieves 12 false alarms per hour.

Keywords: epilepsy, EEG, seizure onset, electroencephalography, neuron, detection

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Authors: Wen Xu

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The expansion of Confucius institutes and Chinese as a Foreign Language (CFL) education is often considered as cultural invasion and part of much bigger, if not ambitious, Chinese central government agenda among Western public opinion. The CFL knowledge and teaching practice inherent in textbooks are also harshly critiqued as failing to align with Western educational principles. This paper takes up these concerns and attempts to articulate that Confucius’s idea of ‘education without discrimination’ appears to have become synonymous with social justice touted in contemporary Australian education and policy discourses. To do so, it capitalises on Bernstein's conceptualization of classification and pedagogic rights to articulate CFL curriculum's potential of drawing in and drawing out curriculum boundaries to achieve educational justice. In this way, the potential useful knowledge of CFL constitutes a worthwhile tool to engage in a peripheral Western country’s education issues, as well as to include disenfranchised students in the multicultural Australian society. It opens spaces for critically theorising CFL curricular justice in Australian educational contexts, and makes an original contribution to scholarly argumentation that CFL curriculum has the potential of including socially and economically disenfranchised students in schooling.

Keywords: curriculum justice, Chinese as a Foreign Language curriculum, Bernstein, equity

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Authors: Neha Singh, Shristi Singh

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Accidents involving motor vehicles are more likely to cause serious injuries and fatalities. It also has a host of other perpetual issues, such as the regular loss of life and goods in accidents. To solve these issues, appropriate measures must be implemented, such as establishing an autonomous incident detection system that makes use of machine learning and artificial intelligence. In order to reduce traffic accidents, this article examines the overview of artificial intelligence and machine learning in autonomous event detection systems. The paper explores the major issues, prospective solutions, and use of artificial intelligence and machine learning in road transportation systems for minimising traffic accidents. There is a lot of discussion on additional, fresh, and developing approaches that less frequent accidents in the transportation industry. The study structured the following subtopics specifically: traffic management using machine learning and artificial intelligence and an incident detector with these two technologies. The internet of vehicles and vehicle ad hoc networks, as well as the use of wireless communication technologies like 5G wireless networks and the use of machine learning and artificial intelligence for the planning of road transportation systems, are elaborated. In addition, safety is the primary concern of road transportation. Route optimization, cargo volume forecasting, predictive fleet maintenance, real-time vehicle tracking, and traffic management, according to the review's key conclusions, are essential for ensuring the safety of road transportation networks. In addition to highlighting research trends, unanswered problems, and key research conclusions, the study also discusses the difficulties in applying artificial intelligence to road transport systems. Planning and managing the road transportation system might use the work as a resource.

Keywords: artificial intelligence, machine learning, incident detector, road transport systems, traffic management, automatic incident detection, deep learning

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Authors: Hung-Yi Chen, Shih-Jye Wu

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The existing literature provides little discussion on why a public monopolist gives up its market dominant position and allows private firms entering the market. We argue that the privatization of a public monopolist under a vertically related market may induce the entry of private firms. We develop a model of a mixed oligopoly with vertically related markets to explain the change in the market from a public monopolist to a mixed oligopoly and examine issues on privatizing the downstream public enterprise both in the short run and long run in the vertically related markets. We first show that the welfare-maximizing public monopoly firm is suboptimal in the vertically related markets. This is due to the fact that the privatization will reduce the input price charged by the upstream foreign monopolist. Further, the privatization will induce the entry of private firms since input price will decrease after privatization. Third, we demonstrate that the complete privatizing the public firm becomes a possible solution if the entry cost of private firm is low. Finally, we indicate that the public firm should partially privatize if the free-entry of private firms is allowed. JEL classification: F12, F14, L32, L33

Keywords: free entry, mixed oligopoly, public monopoly, the initiation of privatization, vertically related markets, mixed oligopoly

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Authors: Hao Min Chuah, Tatsuya Yamazaki, Ryosui Iwasawa, Tatsumi Suto

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In recent years, with the advancement of digital technology, the movement to introduce so-called ICT (Information and Communication Technology), such as computer technology and network technology, to civil engineering construction sites and construction sites is accelerating. As part of this movement, attempts are being made in various situations to reproduce actual sites inside computers and use them for designing and construction planning, as well as for training inexperienced engineers. The installation of wave-dissipating blocks on coasts, etc., is a type of work that has been carried out by skilled workers based on their years of experience and is one of the tasks that is difficult for inexperienced workers to carry out on site. Wave-dissipating blocks are structures that are designed to protect coasts, beaches, and so on from erosion by reducing the energy of ocean waves. Wave-dissipating blocks usually weigh more than 1 t and are installed by being suspended by a crane, so it would be time-consuming and costly for inexperienced workers to train on-site. In this paper, therefore, a block installation simulator is developed based on Unity 3D, a game development engine. The simulator computes porosity. Porosity is defined as the ratio of the total volume of the wave breaker blocks inside the structure to the final shape of the ideal structure. Using the evaluation of porosity, the simulator can determine how well the user is able to install the blocks. The voxelization technique is used to calculate the porosity of the structure, simplifying the calculations. Other techniques, such as raycasting and box overlapping, are employed for accurate simulation. In the near future, the simulator will install an automatic block installation algorithm based on combinatorial optimization solutions and compare the user-demonstrated block installation and the appropriate installation solved by the algorithm.

Keywords: 3D simulator, porosity, user interface, voxelization, wave-dissipating blocks

Procedia PDF Downloads 98

Authors: Adel Sabry Eesa, Adnan Mohsin Abdulazeez Brifcani, Zeynep Orman

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Feature selection has been used in many fields such as classification, data mining and object recognition and proven to be effective for removing irrelevant and redundant features from the original data set. In this paper, a new design of distributed intrusion detection system using a combination feature selection model based on bees and decision tree. Bees algorithm is used as the search strategy to find the optimal subset of features, whereas decision tree is used as a judgment for the selected features. Both the produced features and the generated rules are used by Decision Making Mobile Agent to decide whether there is an attack or not in the networks. Decision Making Mobile Agent will migrate through the networks, moving from node to another, if it found that there is an attack on one of the nodes, it then alerts the user through User Interface Agent or takes some action through Action Mobile Agent. The KDD Cup 99 data set is used to test the effectiveness of the proposed system. The results show that even if only four features are used, the proposed system gives a better performance when it is compared with the obtained results using all 41 features.

Keywords: distributed intrusion detection system, mobile agent, feature selection, bees algorithm, decision tree

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Authors: Alessandra Marcelletti, Giovanni Trovato

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Corruption in public offices is found to be the reflection of country-specific features, however, the exact magnitude and the statistical significance of its determinants effect has not yet been identified. The paper aims to propose an estimation method to measure the impact of country fundamentals on corruption, showing that covariates could differently affect the extent of corruption across countries. Thus, we exploit a model able to take into account different factors affecting the incentive to ask or to be asked for a bribe, coherently with the use of the Corruption Perception Index. We assume that discordant results achieved in literature may be explained by omitted hidden factors affecting the agents' decision process. Moreover, assuming homogeneous covariates effect may lead to unreliable conclusions since the country-specific environment is not accounted for. We apply a Finite Mixture Model with concomitant variables to 129 countries from 1995 to 2006, accounting for the impact of the initial conditions in the socio-economic structure on the corruption patterns. Our findings confirm the hypothesis of the decision process of accepting or asking for a bribe varies with specific country fundamental features.

Keywords: Corruption, Finite Mixture Models, Concomitant Variables, Countries Classification

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Authors: Fatima Ali Abbass, Hassan Ibrahim Rkein

Abstract:

The number of entities worldwide that currently accept digital currency as payment is increasing; however, digital currency still is not widely accepted as a medium of exchange, nor they represent legal tender. At the same time, this makes accounting for cryptocurrency, as cash (Currency) is not possible under IAS 7 and IAS 32, Cryptocurrency also cannot be accounted for as Financial Assets at fair value through profit or loss under IFRS 9. Therefore, this paper studies the possible means to account for Cryptocurrency, since, as of today, there is not yet an accounting standard that deals with cryptocurrency. The request to have a specific accounting standard is increasing from top accounting firms and from professional accounting bodies. This study uses a mixture of qualitative and quantitative analysis in its quest to explore the best possible way to account for cryptocurrency. Interviews and surveys were conducted targeting accounting professionals. This study highlighted the deficiencies in the current way of accounting for Cryptocurrency as intangible Assets with an indefinite life. The deficiency becomes well highlighted, as the asset will then be subject to impairment, where under GAAP, only depreciation in the value of the intangible asset is recognized. On the other hand, appreciation in the value of the asset is ignored, and this prohibits the reporting entity from showing the true value of the cryptocurrency asset. This research highlights the gap that arises due to using accounting standards that are not specific for Cryptocurrency and this study confirmed that there is an urgent need to call upon the accounting standards setters (IASB and FASB) to issue accounting standards specifically for Cryptocurrency.

Keywords: cryptocurrency, accounting, IFRS, GAAP, classification, measurement

Procedia PDF Downloads 91

Authors: Salam N. Abdo, Orien L. Tulp, George P. Einstein

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The purpose of this exploratory study was to gather the insights into psoriasis etiology, treatment, and patient experience, for developing psoriasis and psoriatic arthritis diagnostic test. Data collection methods consisted of a comprehensive meta-analysis of relevant studies and psoriasis patient survey. Established meta-analysis guidelines were used for the selection and qualitative comparative analysis of psoriasis and psoriatic arthritis research studies. Only studies that clearly discussed psoriasis etiology, treatment, and patient experience were reviewed and analyzed, to establish a qualitative data base for the study. Using the insights gained from meta-analysis, an existing psoriasis patient survey was modified and administered to collect additional data as well as triangulate the results. The hypothesis is that specific types of psoriatic disease have specific etiology and pathophysiologic pattern. The following etiology categories were identified: bacterial, environmental/microbial, genetic, immune, infectious, trauma/stress, and viral. Additional results, obtained from meta-analysis and confirmed by patient survey, were the common age of onset (early to mid-20s) and type of psoriasis (plaque; mild; symmetrical; scalp, chest, and extremities, specifically elbows and knees). Almost 70% of patients reported no prescription drug use due to severe side effects and prohibitive cost. These results will guide the development of psoriasis and psoriatic arthritis diagnostic test. The significant number of medical publications classified psoriatic arthritis disease as inflammatory of an unknown etiology. Thus numerous meta-analyses struggle to report any meaningful conclusions since no definitive results have been reported to date. Therefore, return to the basics is an essential step to any future meaningful results. To date, medical literature supports the fact that psoriatic disease in its current classification could be misidentifying subcategories, which in turn hinders the success of studies conducted to date. Moreover, there has been an enormous commercial support to pursue various immune-modulation therapies, thus following a narrow hypothesis/mechanism of action that is yet to yield resolution of disease state. Recurrence and complications may be considered unacceptable in a significant number of these studies. The aim of the ongoing study is to focus on a narrow subgroup of patient population, as identified by this exploratory study via meta-analysis and patient survey, and conduct an exhaustive work up, aiming at mechanism of action and causality before proposing a cure or therapeutic modality. Remission in psoriasis has been achieved and documented in medical literature, such as immune-modulation, phototherapy, various over-the-counter agents, including salts and tar. However, there is no psoriasis and psoriatic arthritis diagnostic test to date, to guide the diagnosis and treatment of this debilitating and, thus far, incurable disease. Because psoriasis affects approximately 2% of population, the results of this study may affect the treatment and improve the quality of life of a significant number of psoriasis patients, potentially millions of patients in the United States alone and many more millions worldwide.

Keywords: biologics, early diagnosis, etiology, immune disease, immune modulation therapy, inflammation skin disorder, phototherapy, plaque psoriasis, psoriasis, psoriasis classification, psoriasis disease marker, psoriasis diagnostic test, psoriasis marker, psoriasis mechanism of action, psoriasis treatment, psoriatic arthritis, psoriatic disease, psoriatic disease marker, psoriatic patient experience, psoriatic patient quality of life, remission, salt therapy, targeted immune therapy

Procedia PDF Downloads 117

Authors: Agastya Pratap Singh

Abstract:

Generative Adversarial Networks (GANs) have emerged as powerful tools in the fields of image recognition and anomaly detection due to their ability to model complex data distributions and generate realistic images. This systematic review explores recent advancements and applications of GANs in both image recognition and anomaly detection tasks. We discuss various GAN architectures, such as DCGAN, CycleGAN, and StyleGAN, which have been tailored to improve accuracy, robustness, and efficiency in visual data analysis. In image recognition, GANs have been used to enhance data augmentation, improve classification models, and generate high-quality synthetic images. In anomaly detection, GANs have proven effective in identifying rare and subtle abnormalities across various domains, including medical imaging, cybersecurity, and industrial inspection. The review also highlights the challenges and limitations associated with GAN-based methods, such as instability during training and mode collapse, and suggests future research directions to overcome these issues. Through this review, we aim to provide researchers with a comprehensive understanding of the capabilities and potential of GANs in transforming image recognition and anomaly detection practices.

Keywords: generative adversarial networks, image recognition, anomaly detection, DCGAN, CycleGAN, StyleGAN, data augmentation

Procedia PDF Downloads 7