Search results for: international classification of functioning
6112 Technologic Information about Photovoltaic Applied in Urban Residences
Authors: Stephanie Fabris Russo, Daiane Costa Guimarães, Jonas Pedro Fabris, Maria Emilia Camargo, Suzana Leitão Russo, José Augusto Andrade Filho
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Among renewable energy sources, solar energy is the one that has stood out. Solar radiation can be used as a thermal energy source and can also be converted into electricity by means of effects on certain materials, such as thermoelectric and photovoltaic panels. These panels are often used to generate energy in homes, buildings, arenas, etc., and have low pollution emissions. Thus, a technological prospecting was performed to find patents related to the use of photovoltaic plates in urban residences. The patent search was based on ESPACENET, associating the keywords photovoltaic and home, where we found 136 patent documents in the period of 1994-2015 in the fields title and abstract. Note that the years 2009, 2010, 2011, 2012, 2013 and 2014 had the highest number of applicants, with respectively, 11, 13, 23, 29, 15 and 21. Regarding the country that deposited about this technology, it is clear that China leads with 67 patent deposits, followed by Japan with 38 patents applications. It is important to note that most depositors, 50% are companies, 44% are individual inventors and only 6% are universities. On the International Patent classification (IPC) codes, we noted that the most present classification in results was H02J3/38, which represents provisions in parallel to feed a single network by two or more generators, converters or transformers. Among all categories, there is the H session, which means Electricity, with 70% of the patents.Keywords: photovoltaic, urban residences, technology forecasting, prospecting
Procedia PDF Downloads 2866111 Pilot Trial of Evidence-Based Integrative Group Therapy to Improve Executive Functioning among Adults: Implications for Community Mental Health and Training Clinics
Authors: B. Parchem, M. Watanabe, D. Modrakovic, L. Mathew, A. Franklin, M. Cao, R. E. Broudy
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Objective: Executive functioning (EF) deficits underlie several mental health diagnoses including ADHD, anxiety, and depression. Community mental health clinics face extensive waitlists for services with many referrals involving EF deficits. A pilot trial of a four-week group therapy was developed using key components from Cognitive-Behavioral Therapy (CBT), Dialectical Behavior Therapy (DBT), and mindfulness with an aim to improve EF skills and offer low-fee services. Method: Eight adults (M = 34.5) waiting for services at a community clinic were enrolled in a four-week group therapy at an in-house training clinic for doctoral trainees. Baseline EF, pre-/post-intervention ADHD and distress symptoms, group satisfaction, and curriculum helpfulness were assessed. Results: Downward trends in ADHD and distress symptoms pre/post-intervention were not significant. Favorable responses on group satisfaction and helpfulness suggest clinical utility. Conclusion: Preliminary pilot data from a brief group therapy to improve EF may be an efficacious, acceptable, and feasible intervention for adults waiting for services at community mental health and training clinics where there are high demands and limits to services and staffs.Keywords: executive functioning, cognitive-behavioral therapy, dialectical behavior therapy, mindfulness, adult group therapy
Procedia PDF Downloads 1536110 Application of Argumentation for Improving the Classification Accuracy in Inductive Concept Formation
Authors: Vadim Vagin, Marina Fomina, Oleg Morosin
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This paper contains the description of argumentation approach for the problem of inductive concept formation. It is proposed to use argumentation, based on defeasible reasoning with justification degrees, to improve the quality of classification models, obtained by generalization algorithms. The experiment’s results on both clear and noisy data are also presented.Keywords: argumentation, justification degrees, inductive concept formation, noise, generalization
Procedia PDF Downloads 4316109 Comparison of Various Classification Techniques Using WEKA for Colon Cancer Detection
Authors: Beema Akbar, Varun P. Gopi, V. Suresh Babu
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Colon cancer causes the deaths of about half a million people every year. The common method of its detection is histopathological tissue analysis, it leads to tiredness and workload to the pathologist. A novel method is proposed that combines both structural and statistical pattern recognition used for the detection of colon cancer. This paper presents a comparison among the different classifiers such as Multilayer Perception (MLP), Sequential Minimal Optimization (SMO), Bayesian Logistic Regression (BLR) and k-star by using classification accuracy and error rate based on the percentage split method. The result shows that the best algorithm in WEKA is MLP classifier with an accuracy of 83.333% and kappa statistics is 0.625. The MLP classifier which has a lower error rate, will be preferred as more powerful classification capability.Keywords: colon cancer, histopathological image, structural and statistical pattern recognition, multilayer perception
Procedia PDF Downloads 5676108 Tomato-Weed Classification by RetinaNet One-Step Neural Network
Authors: Dionisio Andujar, Juan lópez-Correa, Hugo Moreno, Angela Ri
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The increased number of weeds in tomato crops highly lower yields. Weed identification with the aim of machine learning is important to carry out site-specific control. The last advances in computer vision are a powerful tool to face the problem. The analysis of RGB (Red, Green, Blue) images through Artificial Neural Networks had been rapidly developed in the past few years, providing new methods for weed classification. The development of the algorithms for crop and weed species classification looks for a real-time classification system using Object Detection algorithms based on Convolutional Neural Networks. The site study was located in commercial corn fields. The classification system has been tested. The procedure can detect and classify weed seedlings in tomato fields. The input to the Neural Network was a set of 10,000 RGB images with a natural infestation of Cyperus rotundus l., Echinochloa crus galli L., Setaria italica L., Portulaca oeracea L., and Solanum nigrum L. The validation process was done with a random selection of RGB images containing the aforementioned species. The mean average precision (mAP) was established as the metric for object detection. The results showed agreements higher than 95 %. The system will provide the input for an online spraying system. Thus, this work plays an important role in Site Specific Weed Management by reducing herbicide use in a single step.Keywords: deep learning, object detection, cnn, tomato, weeds
Procedia PDF Downloads 956107 Model of Optimal Centroids Approach for Multivariate Data Classification
Authors: Pham Van Nha, Le Cam Binh
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Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm. PSO was inspired by the natural behavior of birds and fish in migration and foraging for food. PSO is considered as a multidisciplinary optimization model that can be applied in various optimization problems. PSO’s ideas are simple and easy to understand but PSO is only applied in simple model problems. We think that in order to expand the applicability of PSO in complex problems, PSO should be described more explicitly in the form of a mathematical model. In this paper, we represent PSO in a mathematical model and apply in the multivariate data classification. First, PSOs general mathematical model (MPSO) is analyzed as a universal optimization model. Then, Model of Optimal Centroids (MOC) is proposed for the multivariate data classification. Experiments were conducted on some benchmark data sets to prove the effectiveness of MOC compared with several proposed schemes.Keywords: analysis of optimization, artificial intelligence based optimization, optimization for learning and data analysis, global optimization
Procedia PDF Downloads 2006106 International College Students Understand Entrepreneurial Readiness and Business-Related Skills: A Qualitative Study
Authors: Aleksandar Chonevski
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The free-market economy provides many opportunities for entrepreneurship or starting one’s own business, attracting many students to study business at for-profit colleges in the United States. This is also true for international students, many of whom are filled with the hope of making a better life for themselves and their families through entrepreneurial endeavors. This qualitative research showed that not all graduates business students start their own business. In investigating this phenomenon, the effectiveness of entrepreneurship curricula at international colleges needs to be examined in order to adjust, improve and reform entrepreneurship curricula. This qualitative study will explore how business skills learned in college for-profit play a role in the entrepreneurial readiness of undergraduate business students in the south Florida. Business curricula helps international students achieve goals and transform their actions to understand challenges in a corporate society. Students will be interviewed to gain information about the students’ experience with entrepreneurship curricula in a for-profit college in south Florida.Keywords: business skills, college curriculum, entrepreneurial readiness, international students
Procedia PDF Downloads 656105 Applying Semi-Automatic Digital Aerial Survey Technology and Canopy Characters Classification for Surface Vegetation Interpretation of Archaeological Sites
Authors: Yung-Chung Chuang
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The cultural layers of archaeological sites are mainly affected by surface land use, land cover, and root system of surface vegetation. For this reason, continuous monitoring of land use and land cover change is important for archaeological sites protection and management. However, in actual operation, on-site investigation and orthogonal photograph interpretation require a lot of time and manpower. For this reason, it is necessary to perform a good alternative for surface vegetation survey in an automated or semi-automated manner. In this study, we applied semi-automatic digital aerial survey technology and canopy characters classification with very high-resolution aerial photographs for surface vegetation interpretation of archaeological sites. The main idea is based on different landscape or forest type can easily be distinguished with canopy characters (e.g., specific texture distribution, shadow effects and gap characters) extracted by semi-automatic image classification. A novel methodology to classify the shape of canopy characters using landscape indices and multivariate statistics was also proposed. Non-hierarchical cluster analysis was used to assess the optimal number of canopy character clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy character classification (seven categories). Therefore, people could easily predict the forest type and vegetation land cover by corresponding to the specific canopy character category. The results showed that the semi-automatic classification could effectively extract the canopy characters of forest and vegetation land cover. As for forest type and vegetation type prediction, the average prediction accuracy reached 80.3%~91.7% with different sizes of test frame. It represented this technology is useful for archaeological site survey, and can improve the classification efficiency and data update rate.Keywords: digital aerial survey, canopy characters classification, archaeological sites, multivariate statistics
Procedia PDF Downloads 1326104 Spontaneous and Posed Smile Detection: Deep Learning, Traditional Machine Learning, and Human Performance
Authors: Liang Wang, Beste F. Yuksel, David Guy Brizan
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A computational model of affect that can distinguish between spontaneous and posed smiles with no errors on a large, popular data set using deep learning techniques is presented in this paper. A Long Short-Term Memory (LSTM) classifier, a type of Recurrent Neural Network, is utilized and compared to human classification. Results showed that while human classification (mean of 0.7133) was above chance, the LSTM model was more accurate than human classification and other comparable state-of-the-art systems. Additionally, a high accuracy rate was maintained with small amounts of training videos (70 instances). The derivation of important features to further understand the success of our computational model were analyzed, and it was inferred that thousands of pairs of points within the eyes and mouth are important throughout all time segments in a smile. This suggests that distinguishing between a posed and spontaneous smile is a complex task, one which may account for the difficulty and lower accuracy of human classification compared to machine learning models.Keywords: affective computing, affect detection, computer vision, deep learning, human-computer interaction, machine learning, posed smile detection, spontaneous smile detection
Procedia PDF Downloads 1146103 An AK-Chart for the Non-Normal Data
Authors: Chia-Hau Liu, Tai-Yue Wang
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Traditional multivariate control charts assume that measurement from manufacturing processes follows a multivariate normal distribution. However, this assumption may not hold or may be difficult to verify because not all the measurement from manufacturing processes are normal distributed in practice. This study develops a new multivariate control chart for monitoring the processes with non-normal data. We propose a mechanism based on integrating the one-class classification method and the adaptive technique. The adaptive technique is used to improve the sensitivity to small shift on one-class classification in statistical process control. In addition, this design provides an easy way to allocate the value of type I error so it is easier to be implemented. Finally, the simulation study and the real data from industry are used to demonstrate the effectiveness of the propose control charts.Keywords: multivariate control chart, statistical process control, one-class classification method, non-normal data
Procedia PDF Downloads 4156102 Electroencephalogram Based Alzheimer Disease Classification using Machine and Deep Learning Methods
Authors: Carlos Roncero-Parra, Alfonso Parreño-Torres, Jorge Mateo Sotos, Alejandro L. Borja
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In this research, different methods based on machine/deep learning algorithms are presented for the classification and diagnosis of patients with mental disorders such as alzheimer. For this purpose, the signals obtained from 32 unipolar electrodes identified by non-invasive EEG were examined, and their basic properties were obtained. More specifically, different well-known machine learning based classifiers have been used, i.e., support vector machine (SVM), Bayesian linear discriminant analysis (BLDA), decision tree (DT), Gaussian Naïve Bayes (GNB), K-nearest neighbor (KNN) and Convolutional Neural Network (CNN). A total of 668 patients from five different hospitals have been studied in the period from 2011 to 2021. The best accuracy is obtained was around 93 % in both ADM and ADA classifications. It can be concluded that such a classification will enable the training of algorithms that can be used to identify and classify different mental disorders with high accuracy.Keywords: alzheimer, machine learning, deep learning, EEG
Procedia PDF Downloads 1136101 Constraining the Potential Nickel Laterite Area Using Geographic Information System-Based Multi-Criteria Rating in Surigao Del Sur
Authors: Reiner-Ace P. Mateo, Vince Paolo F. Obille
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The traditional method of classifying the potential mineral resources requires a significant amount of time and money. In this paper, an alternative way to classify potential mineral resources with GIS application in Surigao del Sur. The three (3) analog map data inputs integrated to GIS are geologic map, topographic map, and land cover/vegetation map. The indicators used in the classification of potential nickel laterite integrated from the analog map data inputs are a geologic indicator, which is the presence of ultramafic rock from the geologic map; slope indicator and the presence of plateau edges from the topographic map; areas of forest land, grassland, and shrublands from the land cover/vegetation map. The potential mineral of the area was classified from low up to very high potential. The produced mineral potential classification map of Surigao del Sur has an estimated 4.63% low nickel laterite potential, 42.15% medium nickel laterite potential, 43.34% high nickel laterite potential, and 9.88% very high nickel laterite from its ultramafic terrains. For the validation of the produced map, it was compared with known occurrences of nickel laterite in the area using a nickel mining tenement map from the area with the application of remote sensing. Three (3) prominent nickel mining companies were delineated in the study area. The generated potential classification map of nickel-laterite in Surigao Del Sur may be of aid to the mining companies which are currently in the exploration phase in the study area. Also, the currently operating nickel mines in the study area can help to validate the reliability of the mineral classification map produced.Keywords: mineral potential classification, nickel laterites, GIS, remote sensing, Surigao del Sur
Procedia PDF Downloads 1136100 Measures Adopted by FIFA and UEFA against Russian Athletes: A Human Rights Perspective
Authors: Ayyoub Jamali, Alena Kozlova
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The Russian invasion of Ukraine has tested the mettle of the international community, prompting not only States but also non-state actors to take deterrent action in response. Indeed, international sports organisations, namely FIFA and UEFA, have been rather successful in shifting the power dynamics by introducing a complete ban on the Russian national and club teams. This article aims to inquire into the human rights implications of such actions taken by international sports organisations. First, the article departs from an assessment of the legal status of FIFA and UEFA under international law and reflects on how a legal link could be established vis-à-vis their human rights obligations. Second, it examines the human rights aspects of the impugned measures by FIFA and UEFA on the part of the Russian athletes, further scrutinising them against the international human rights law principle of non-discrimination through a proportionality test. Last, it draws basic pathways for how possible human rights violations committed in the context of measures adopted by such organisations could be remedied, outlining the challenges of arbitration and litigation in Switzerland.Keywords: FIFA, UEFA, FUR, ban, human rights, Russia, Ukraine, non-state actors
Procedia PDF Downloads 756099 Identification and Classification of Medicinal Plants of Indian Himalayan Region Using Hyperspectral Remote Sensing and Machine Learning Techniques
Authors: Kishor Chandra Kandpal, Amit Kumar
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The Indian Himalaya region harbours approximately 1748 plants of medicinal importance, and as per International Union for Conservation of Nature (IUCN), the 112 plant species among these are threatened and endangered. To ease the pressure on these plants, the government of India is encouraging its in-situ cultivation. The Saussurea costus, Valeriana jatamansi, and Picrorhiza kurroa have also been prioritized for large scale cultivation owing to their market demand, conservation value and medicinal properties. These species are found from 1000 m to 4000 m elevation ranges in the Indian Himalaya. Identification of these plants in the field requires taxonomic skills, which is one of the major bottleneck in the conservation and management of these plants. In recent years, Hyperspectral remote sensing techniques have been precisely used for the discrimination of plant species with the help of their unique spectral signatures. In this background, a spectral library of the above 03 medicinal plants was prepared by collecting the spectral data using a handheld spectroradiometer (325 to 1075 nm) from farmer’s fields of Himachal Pradesh and Uttarakhand states of Indian Himalaya. The Random forest (RF) model was implied on the spectral data for the classification of the medicinal plants. The 80:20 standard split ratio was followed for training and validation of the RF model, which resulted in training accuracy of 84.39 % (kappa coefficient = 0.72) and testing accuracy of 85.29 % (kappa coefficient = 0.77). This RF classifier has identified green (555 to 598 nm), red (605 nm), and near-infrared (725 to 840 nm) wavelength regions suitable for the discrimination of these species. The findings of this study have provided a technique for rapid and onsite identification of the above medicinal plants in the field. This will also be a key input for the classification of hyperspectral remote sensing images for mapping of these species in farmer’s field on a regional scale. This is a pioneer study in the Indian Himalaya region for medicinal plants in which the applicability of hyperspectral remote sensing has been explored.Keywords: himalaya, hyperspectral remote sensing, machine learning; medicinal plants, random forests
Procedia PDF Downloads 1976098 Limits of the Dot Counting Test: A Culturally Responsive Approach to Neuropsychological Evaluations and Treatment
Authors: Erin Curtis, Avraham Schwiger
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Neuropsychological testing and evaluation is a crucial step in providing patients with effective diagnoses and treatment while in clinical care. The variety of batteries used in these evaluations can help clinicians better understand the nuanced declivities in a patient’s cognitive, behavioral, or emotional functioning, consequently equipping clinicians with the insights to make intentional choices about a patient’s care. Despite the knowledge these batteries can yield, some aspects of neuropsychological testing remain largely inaccessible to certain patient groups as a result of fundamental cultural, educational, or social differences. One such battery includes the Dot Counting Test (DCT), during which patients are required to count a series of dots on a page as rapidly and accurately as possible. As the battery progresses, the dots appear in clusters that are designed to be easily multiplied. This task evaluates a patient’s cognitive functioning, attention, and level of effort exerted on the evaluation as a whole. However, there is evidence to suggest that certain social groups, particularly Latinx groups, may perform worse on this task as a result of cultural or educational differences, not reduced cognitive functioning or effort. As such, this battery fails to account for baseline differences among patient groups, thus creating questions surrounding the accuracy, generalizability, and value of its results. Accessibility and cultural sensitivity are critical considerations in the testing and treatment of marginalized groups, yet have been largely ignored in the literature and in clinical settings to date. Implications and improvements to applications are discussed.Keywords: culture, latino, neuropsychological assessment, neuropsychology, accessibility
Procedia PDF Downloads 1066097 An Investigation of Tourists’ Destination Loyalty: A Case Study of Bangkok, Thailand
Authors: Sukritta Larsen, Kevin Wongleedee
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The purposes of this research were to study tourists’ destination loyalty from the perspective of international tourists in Bangkok and to study the level of interest to revisit Bangkok in the near future. A probability random sampling of 200 international tourists was utilized. Half of the sample group was male and the other half was female. A Likert-five-scale questionnaire was designed to collect the data and small in-depth interviews were also used to obtain their opinions. The findings revealed that the majority of respondents had a medium level of loyalty. When examined in detail, the destination loyalty indicators can be ranked according to the mean average from high to low as follows: to recommend the visit, to say positive things, to revisit in the next three years, to refer the information, and to plan to visit regularly. Finally, the findings from the in-depth interviews with small group of international tourists revealed that the major obstacles that prevented many international tourists who may interested in revisiting Thailand included traffic congestions, high crime rate, and political instability.Keywords: destination loyalty, international tourists, revisit, Bangkok
Procedia PDF Downloads 3316096 Performance Enrichment of Deep Feed Forward Neural Network and Deep Belief Neural Networks for Fault Detection of Automobile Gearbox Using Vibration Signal
Authors: T. Praveenkumar, Kulpreet Singh, Divy Bhanpuriya, M. Saimurugan
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This study analysed the classification accuracy for gearbox faults using Machine Learning Techniques. Gearboxes are widely used for mechanical power transmission in rotating machines. Its rotating components such as bearings, gears, and shafts tend to wear due to prolonged usage, causing fluctuating vibrations. Increasing the dependability of mechanical components like a gearbox is hampered by their sealed design, which makes visual inspection difficult. One way of detecting impending failure is to detect a change in the vibration signature. The current study proposes various machine learning algorithms, with aid of these vibration signals for obtaining the fault classification accuracy of an automotive 4-Speed synchromesh gearbox. Experimental data in the form of vibration signals were acquired from a 4-Speed synchromesh gearbox using Data Acquisition System (DAQs). Statistical features were extracted from the acquired vibration signal under various operating conditions. Then the extracted features were given as input to the algorithms for fault classification. Supervised Machine Learning algorithms such as Support Vector Machines (SVM) and unsupervised algorithms such as Deep Feed Forward Neural Network (DFFNN), Deep Belief Networks (DBN) algorithms are used for fault classification. The fusion of DBN & DFFNN classifiers were architected to further enhance the classification accuracy and to reduce the computational complexity. The fault classification accuracy for each algorithm was thoroughly studied, tabulated, and graphically analysed for fused and individual algorithms. In conclusion, the fusion of DBN and DFFNN algorithm yielded the better classification accuracy and was selected for fault detection due to its faster computational processing and greater efficiency.Keywords: deep belief networks, DBN, deep feed forward neural network, DFFNN, fault diagnosis, fusion of algorithm, vibration signal
Procedia PDF Downloads 1036095 Investigation of Topic Modeling-Based Semi-Supervised Interpretable Document Classifier
Authors: Dasom Kim, William Xiu Shun Wong, Yoonjin Hyun, Donghoon Lee, Minji Paek, Sungho Byun, Namgyu Kim
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There have been many researches on document classification for classifying voluminous documents automatically. Through document classification, we can assign a specific category to each unlabeled document on the basis of various machine learning algorithms. However, providing labeled documents manually requires considerable time and effort. To overcome the limitations, the semi-supervised learning which uses unlabeled document as well as labeled documents has been invented. However, traditional document classifiers, regardless of supervised or semi-supervised ones, cannot sufficiently explain the reason or the process of the classification. Thus, in this paper, we proposed a methodology to visualize major topics and class components of each document. We believe that our methodology for visualizing topics and classes of each document can enhance the reliability and explanatory power of document classifiers.Keywords: data mining, document classifier, text mining, topic modeling
Procedia PDF Downloads 3906094 Automatic Classification for the Degree of Disc Narrowing from X-Ray Images Using CNN
Authors: Kwangmin Joo
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Automatic detection of lumbar vertebrae and classification method is proposed for evaluating the degree of disc narrowing. Prior to classification, deep learning based segmentation is applied to detect individual lumbar vertebra. M-net is applied to segment five lumbar vertebrae and fine-tuning segmentation is employed to improve the accuracy of segmentation. Using the features extracted from previous step, clustering technique, k-means clustering, is applied to estimate the degree of disc space narrowing under four grade scoring system. As preliminary study, techniques proposed in this research could help building an automatic scoring system to diagnose the severity of disc narrowing from X-ray images.Keywords: Disc space narrowing, Degenerative disc disorders, Deep learning based segmentation, Clustering technique
Procedia PDF Downloads 1166093 One-Shot Text Classification with Multilingual-BERT
Authors: Hsin-Yang Wang, K. M. A. Salam, Ying-Jia Lin, Daniel Tan, Tzu-Hsuan Chou, Hung-Yu Kao
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Detecting user intent from natural language expression has a wide variety of use cases in different natural language processing applications. Recently few-shot training has a spike of usage on commercial domains. Due to the lack of significant sample features, the downstream task performance has been limited or leads to an unstable result across different domains. As a state-of-the-art method, the pre-trained BERT model gathering the sentence-level information from a large text corpus shows improvement on several NLP benchmarks. In this research, we are proposing a method to change multi-class classification tasks into binary classification tasks, then use the confidence score to rank the results. As a language model, BERT performs well on sequence data. In our experiment, we change the objective from predicting labels into finding the relations between words in sequence data. Our proposed method achieved 71.0% accuracy in the internal intent detection dataset and 63.9% accuracy in the HuffPost dataset. Acknowledgment: This work was supported by NCKU-B109-K003, which is the collaboration between National Cheng Kung University, Taiwan, and SoftBank Corp., Tokyo.Keywords: OSML, BERT, text classification, one shot
Procedia PDF Downloads 906092 Interaction with Earth’s Surface in Remote Sensing
Authors: Spoorthi Sripad
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Remote sensing is a powerful tool for acquiring information about the Earth's surface without direct contact, relying on the interaction of electromagnetic radiation with various materials and features. This paper explores the fundamental principle of "Interaction with Earth's Surface" in remote sensing, shedding light on the intricate processes that occur when electromagnetic waves encounter different surfaces. The absorption, reflection, and transmission of radiation generate distinct spectral signatures, allowing for the identification and classification of surface materials. The paper delves into the significance of the visible, infrared, and thermal infrared regions of the electromagnetic spectrum, highlighting how their unique interactions contribute to a wealth of applications, from land cover classification to environmental monitoring. The discussion encompasses the types of sensors and platforms used to capture these interactions, including multispectral and hyperspectral imaging systems. By examining real-world applications, such as land cover classification and environmental monitoring, the paper underscores the critical role of understanding the interaction with the Earth's surface for accurate and meaningful interpretation of remote sensing data.Keywords: remote sensing, earth's surface interaction, electromagnetic radiation, spectral signatures, land cover classification, archeology and cultural heritage preservation
Procedia PDF Downloads 486091 The Applicability of International Humanitarian Law to Non-State Actors
Authors: Yin Cheung Lam
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In 1949, the ratification of the Geneva Conventions heralded the international community’s adoption of a new universal and non-discriminatory approach to human rights in situations of conflict. However, with the proliferation of international terrorism after the 9/11 attacks on the United States (U.S.), the international community’s uneven and contradictory implementations of international humanitarian law (IHL) questioned its agenda of universal human rights. Specifically, the derogation from IHL has never been so pronounced in the U.S. led ‘War on Terror’. While an extensive literature has ‘assessed the impact’ of the implementation of the Geneva Conventions, limited attention has been paid to interrogating the ways in which the Geneva Conventions and its resulting implementation have functioned to discursively reproduce certain understandings of human rights between states and non-state actors. Through a discursive analysis of the Geneva Conventions and the conceptualization of human rights in relation to terrorism, this thesis problematises the way in which the U.S. has understood and reproduced understandings of human rights. Using the U.S. ‘War on Terror’ as an example, it seeks to extend previous analyses of the U.S.’ practice of IHL through a qualitative discursive analysis of the human rights content that appears in the Geneva Conventions in addition to the speeches and policy documents on the ‘War on Terror’.Keywords: discursive analysis, human rights, non-state actors, war on terror
Procedia PDF Downloads 5976090 Comparison of the Effectiveness of Tree Algorithms in Classification of Spongy Tissue Texture
Authors: Roza Dzierzak, Waldemar Wojcik, Piotr Kacejko
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Analysis of the texture of medical images consists of determining the parameters and characteristics of the examined tissue. The main goal is to assign the analyzed area to one of two basic groups: as a healthy tissue or a tissue with pathological changes. The CT images of the thoracic lumbar spine from 15 healthy patients and 15 with confirmed osteoporosis were used for the analysis. As a result, 120 samples with dimensions of 50x50 pixels were obtained. The set of features has been obtained based on the histogram, gradient, run-length matrix, co-occurrence matrix, autoregressive model, and Haar wavelet. As a result of the image analysis, 290 descriptors of textural features were obtained. The dimension of the space of features was reduced by the use of three selection methods: Fisher coefficient (FC), mutual information (MI), minimization of the classification error probability and average correlation coefficients between the chosen features minimization of classification error probability (POE) and average correlation coefficients (ACC). Each of them returned ten features occupying the initial place in the ranking devised according to its own coefficient. As a result of the Fisher coefficient and mutual information selections, the same features arranged in a different order were obtained. In both rankings, the 50% percentile (Perc.50%) was found in the first place. The next selected features come from the co-occurrence matrix. The sets of features selected in the selection process were evaluated using six classification tree methods. These were: decision stump (DS), Hoeffding tree (HT), logistic model trees (LMT), random forest (RF), random tree (RT) and reduced error pruning tree (REPT). In order to assess the accuracy of classifiers, the following parameters were used: overall classification accuracy (ACC), true positive rate (TPR, classification sensitivity), true negative rate (TNR, classification specificity), positive predictive value (PPV) and negative predictive value (NPV). Taking into account the classification results, it should be stated that the best results were obtained for the Hoeffding tree and logistic model trees classifiers, using the set of features selected by the POE + ACC method. In the case of the Hoeffding tree classifier, the highest values of three parameters were obtained: ACC = 90%, TPR = 93.3% and PPV = 93.3%. Additionally, the values of the other two parameters, i.e., TNR = 86.7% and NPV = 86.6% were close to the maximum values obtained for the LMT classifier. In the case of logistic model trees classifier, the same ACC value was obtained ACC=90% and the highest values for TNR=88.3% and NPV= 88.3%. The values of the other two parameters remained at a level close to the highest TPR = 91.7% and PPV = 91.6%. The results obtained in the experiment show that the use of classification trees is an effective method of classification of texture features. This allows identifying the conditions of the spongy tissue for healthy cases and those with the porosis.Keywords: classification, feature selection, texture analysis, tree algorithms
Procedia PDF Downloads 1676089 Analysis of Matching Pursuit Features of EEG Signal for Mental Tasks Classification
Authors: Zin Mar Lwin
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Brain Computer Interface (BCI) Systems have developed for people who suffer from severe motor disabilities and challenging to communicate with their environment. BCI allows them for communication by a non-muscular way. For communication between human and computer, BCI uses a type of signal called Electroencephalogram (EEG) signal which is recorded from the human„s brain by means of an electrode. The electroencephalogram (EEG) signal is an important information source for knowing brain processes for the non-invasive BCI. Translating human‟s thought, it needs to classify acquired EEG signal accurately. This paper proposed a typical EEG signal classification system which experiments the Dataset from “Purdue University.” Independent Component Analysis (ICA) method via EEGLab Tools for removing artifacts which are caused by eye blinks. For features extraction, the Time and Frequency features of non-stationary EEG signals are extracted by Matching Pursuit (MP) algorithm. The classification of one of five mental tasks is performed by Multi_Class Support Vector Machine (SVM). For SVMs, the comparisons have been carried out for both 1-against-1 and 1-against-all methods. Procedia PDF Downloads 2676088 Executive Function Assessment with Aboriginal Australians
Authors: T. Keiller, E. Hindman, P. Hassmen, K. Radford, L. Lavrencic
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Background: Psychosocial disadvantage is associated with impaired cognitive abilities, with executive functioning (EF) abilities particularly vulnerable. EF abilities strongly predict general daily functioning, educational and career prospects, and health choices. A reliable and valid assessment of EF is important to support appropriate care and intervention strategies. However, evidence-based EF assessment tools for use with Aboriginal Australians are limited. Aim and Method: This research aims to develop and validate a culturally appropriate EF tool for use with indigenous Australians. To this end, Study One aims to review current literature examining the benefits and disadvantages of current EF assessment tools for use with Indigenous Australians. Study Two aims to collate expert opinion on the strengths and weaknesses of various current EF assessment tools for use with Indigenous Australians using Delphi methodology with experienced psychologists (n = 10). The initial two studies will inform the development of a culturally appropriate assessment tool. Study Three aims to evaluate the psychometric properties of the tool with an Indigenous sample living in the New South Wales Mid-North Coast. The study aims to quantify the predictive validity of this tool via comparison to functionality predictors and neuropsychological assessment scores. Study Four aims to collect qualitative data surrounding the feasibility and acceptability of the tool among indigenous Australians and health professionals. Expected Results: Findings from this research are likely to inform cognitive assessment practices and tool selection for health professionals conducting cognitive assessments with Indigenous Australians. Improved assessment of EF will inform appropriate care and intervention strategies for individuals with EF deficits.Keywords: aboriginal Australians, assessment tool, cognition, executive functioning
Procedia PDF Downloads 2606087 China and the Criminalization of Aggression. The Juxtaposition of Justice and the Maintenance of International Peace and Security
Authors: Elisabetta Baldassini
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Responses to atrocities are always unique and context-dependent. They cannot be foretold nor easily prompted. However, the events of the twentieth century had set the scene for the international community to explore new and more robust systems in response to war atrocities, with the ultimate goal being the restoration and maintenance of peace and security. The outlawry of war and the attribution of individual liability for international crimes were two major landmarks that set the roots for the development of international criminal law. From the London Conference (1945) for the establishment of the first international military tribunal in Nuremberg to Rome at the inauguration of the first permanent international criminal court, the development of international criminal law has shaped in itself a fluctuating degree of tensions between justice and maintenance of international peace and security, the cardinal dichotomy of this article. The adoption of judicial measures to achieve peace indeed set justice as an essential feature at the heart of the new international system. Blackhole of this dichotomy is the crime of aggression. Aggression was at first the key component of a wide body of peace projects prosecuted under the charges of crimes against peace. However, the wide array of controversies around aggression mostly related to its definition, determination and the involvement of the Security Council silenced, partly, a degree of efforts and agreements. Notwithstanding the establishment of the International Criminal Court (ICC), jurisdiction over the crime of aggression was suspended until an agreement over the definition and the conditions for the Court’s exercise of jurisdiction was reached. Compromised over the crime was achieved in Kampala in 2010 and the Court’s jurisdiction over the crime of aggression was eventually activated on 17 July 2018. China has steadily supported the advancement of international criminal justice together with the establishment of a permanent international judicial body to prosecute grave crimes and has proactively participated at the various stages of the codification and development of the crime of aggression. However, China has also expressed systematic reservations and setbacks. With the use of primary and secondary sources, including semi-structured interviews, this research aims at analyzing the role that China has played throughout the substantive historical development of the crime of aggression, demonstrating a sharp inclination in the maintenance of international peace and security. Such state behavior seems to reflect national and international political mechanisms that gravitate around a distinct rationale that involves a share of culture and tradition.Keywords: maintenance of peace and security, cultural expression of justice, crime of aggression, China
Procedia PDF Downloads 2206086 An Examination of Criminology and Cyber Crime in Contemporary Society
Authors: Uche A. Nnawulezi
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The evolving global environment has as of late seen formative difficulties bordering on cyber crime and its attendant effects. This paper looks at what constitutes an offense of cyber crime under the tenets of International Law as no nation can lay bona-fide claim in managing cyber crime as a criminal phenomenon. Therefore, there has been a plethora of ideological, conceptual and mental propositions of policies aimed at domesticating cyber crimes – an international crime. These policies were as a result of parochial consideration and social foundations which negate the spirit of internationally accepted procedures. The study also noted that the non-domestication of cyber crime laws by most countries has led to an increase in cyber crimes and its attendant effects have remained unabated. The author has pointed out emerging international rules as a panacea for a sustainable cyber crime-free society. The paper relied on documentary evidence and hence scooped much of the data from secondary sources such as text books, journals, articles and periodicals and more so, opinion papers, emanating from international criminal court. It concludes that the necessary recommendations made in this paper, if fully adopted, shall go a long way in maintaining a cyber crime-free society. Ultimately, the domestic and international law mechanisms capable of dealing with cyber crime offenses should be expanded and be made proactive in order to deal with the demands of modern day challenges.Keywords: criminology, cyber crime, domestic law, international law
Procedia PDF Downloads 1816085 Heart Failure Identification and Progression by Classifying Cardiac Patients
Authors: Muhammad Saqlain, Nazar Abbas Saqib, Muazzam A. Khan
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Heart Failure (HF) has become the major health problem in our society. The prevalence of HF has increased as the patient’s ages and it is the major cause of the high mortality rate in adults. A successful identification and progression of HF can be helpful to reduce the individual and social burden from this syndrome. In this study, we use a real data set of cardiac patients to propose a classification model for the identification and progression of HF. The data set has divided into three age groups, namely young, adult, and old and then each age group have further classified into four classes according to patient’s current physical condition. Contemporary Data Mining classification algorithms have been applied to each individual class of every age group to identify the HF. Decision Tree (DT) gives the highest accuracy of 90% and outperform all other algorithms. Our model accurately diagnoses different stages of HF for each age group and it can be very useful for the early prediction of HF.Keywords: decision tree, heart failure, data mining, classification model
Procedia PDF Downloads 3976084 The Impact of the Russian Democratic Weaknesses on the International Society
Authors: Leone Sherman
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While the democratic rights of a citizen may be very clearly outlined in a country’s constitution, it’s not uncommon for political elite to undermine those rights and gain more power and control over a country than it is allowed by this constitution. Moreover, while such a change in some smaller states may not have a substantial impact on the international community, the same change in countries with vast resources and political influence, such as Russia, is always a considerable factor for the world policy. This article aims to research the weaknesses of the Russian democratic system and their effect on the international policy through the three key aspects: The Russian people’s ability to produce the required political will to control their government’s decisions, the current development of the Russian political environment, and the affection of this environment on the world community as a whole during the recent years. The used methodology is a narrative analysis of recent political events, official statistics, international investigations and media statements. As a result, the ever-widening gap between the people and the government becomes evidently seen, as well as the challenges it imposes on the political world arena, both current and those that still lie ahead of us.Keywords: Russia, political analysis, democratic weaknesses, international society
Procedia PDF Downloads 3366083 Methods for Enhancing Ensemble Learning or Improving Classifiers of This Technique in the Analysis and Classification of Brain Signals
Authors: Seyed Mehdi Ghezi, Hesam Hasanpoor
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This scientific article explores enhancement methods for ensemble learning with the aim of improving the performance of classifiers in the analysis and classification of brain signals. The research approach in this field consists of two main parts, each with its own strengths and weaknesses. The choice of approach depends on the specific research question and available resources. By combining these approaches and leveraging their respective strengths, researchers can enhance the accuracy and reliability of classification results, consequently advancing our understanding of the brain and its functions. The first approach focuses on utilizing machine learning methods to identify the best features among the vast array of features present in brain signals. The selection of features varies depending on the research objective, and different techniques have been employed for this purpose. For instance, the genetic algorithm has been used in some studies to identify the best features, while optimization methods have been utilized in others to identify the most influential features. Additionally, machine learning techniques have been applied to determine the influential electrodes in classification. Ensemble learning plays a crucial role in identifying the best features that contribute to learning, thereby improving the overall results. The second approach concentrates on designing and implementing methods for selecting the best classifier or utilizing meta-classifiers to enhance the final results in ensemble learning. In a different section of the research, a single classifier is used instead of multiple classifiers, employing different sets of features to improve the results. The article provides an in-depth examination of each technique, highlighting their advantages and limitations. By integrating these techniques, researchers can enhance the performance of classifiers in the analysis and classification of brain signals. This advancement in ensemble learning methodologies contributes to a better understanding of the brain and its functions, ultimately leading to improved accuracy and reliability in brain signal analysis and classification.Keywords: ensemble learning, brain signals, classification, feature selection, machine learning, genetic algorithm, optimization methods, influential features, influential electrodes, meta-classifiers
Procedia PDF Downloads 68