Search results for: hyperspectral image classification using tree search algorithm

Authors: Abdullah M. Almeshal, Mohammad R. Alenezi, Muhammad Moaz

Abstract:

In this paper, we discuss the performance of applying hybrid spiral dynamic bacterial chemotaxis (HSDBC) optimisation algorithm on an intelligent controller for a differential drive robot. A unicycle class of differential drive robot is utilised to serve as a basis application to evaluate the performance of the HSDBC algorithm. A hybrid fuzzy logic controller is developed and implemented for the unicycle robot to follow a predefined trajectory. Trajectories of various frictional profiles and levels were simulated to evaluate the performance of the robot at different operating conditions. Controller gains and scaling factors were optimised using HSDBC and the performance is evaluated in comparison to previously adopted optimisation algorithms. The HSDBC has proven its feasibility in achieving a faster convergence toward the optimal gains and resulted in a superior performance.

Keywords: differential drive robot, hybrid fuzzy controller, optimization, path tracking, unicycle robot

Procedia PDF Downloads 467

Authors: S. Curteanu, F. Leon, M. Gavrilescu, S. A. Floria

Abstract:

Optimization algorithms inspired from human behavior were applied in this approach, associated with neural networks models. The algorithms belong to human behaviors of learning and cooperation and human competitive behavior classes. For the first class, the main strategies include: random learning, individual learning, and social learning, and the selected algorithms are: simplified human learning optimization (SHLO), social learning optimization (SLO), and teaching-learning based optimization (TLBO). For the second class, the concept of learning is associated with competitiveness, and the selected algorithms are sports-inspired algorithms (with Football Game Algorithm, FGA and Volleyball Premier League, VPL) and Imperialist Competitive Algorithm (ICA). A real process, the synthesis of polyacrylamide-based multicomponent hydrogels, where some parameters are difficult to obtain experimentally, is considered as a case study. Reaction yield and swelling degree are predicted as a function of reaction conditions (acrylamide concentration, initiator concentration, crosslinking agent concentration, temperature, reaction time, and amount of inclusion polymer, which could be starch, poly(vinyl alcohol) or gelatin). The experimental results contain 175 data. Artificial neural networks are obtained in optimal form with biologically inspired algorithm; the optimization being perform at two level: structural and parametric. Feedforward neural networks with one or two hidden layers and no more than 25 neurons in intermediate layers were obtained with values of correlation coefficient in the validation phase over 0.90. The best results were obtained with TLBO algorithm, correlation coefficient being 0.94 for an MLP(6:9:20:2) – a feedforward neural network with two hidden layers and 9 and 20, respectively, intermediate neurons. Good results obtained prove the efficiency of the optimization algorithms. More than the good results, what is important in this approach is the simulation methodology, including neural networks and optimization biologically inspired algorithms, which provide satisfactory results. In addition, the methodology developed in this approach is general and has flexibility so that it can be easily adapted to other processes in association with different types of models.

Keywords: artificial neural networks, human behaviors of learning and cooperation, human competitive behavior, optimization algorithms

Procedia PDF Downloads 110

Authors: Odete Paiva, Cláudia Seabra, José Luís Abrantes, Fernanda Cravidão

Abstract:

There is a clear ‘cult of authenticity’, at least in modern Western society. So, there is a need to analyze the tourist perception of authenticity, bearing in mind the destination, its attractions, motivations, cultural distance, and contact with other tourists. Our study seeks to investigate the relationship among cultural values, image, sense of place, perception of authenticity and behavior intentions at World Heritage Historic Centers. From a theoretical perspective, few researches focus on the impact of cultural values, image and sense of place on authenticity and intentions behavior in tourists. The intention of this study is to help close this gap. A survey was applied to collect data from tourists visiting two World Heritage Historic Centers – Guimarães in Portugal and Cordoba in Spain. Data was analyzed in order to establish a structural equation model (SEM). Discussion centers on the implications of model to theory and managerial development of tourism strategies. Recommendations for destinations managers and promoters and tourist organizations administrators are addressed.

Keywords: authenticity perception, behavior intentions, cultural tourism, cultural values, world heritage historic centers

Procedia PDF Downloads 319

Authors: R. Ananthanarayanan, S. B. Nakula, D. R. Seenivasagam, J. Naua, B. Sharma

Abstract:

Product packaging is a critical component of production, trade, and marketing, playing numerous vital roles that often go unnoticed by consumers. Packaging is essential for maintaining the shelf life, quality assurance, and safety of both manufactured and agricultural products. For example, harvested produce or processed foods can quickly lose quality and freshness, making secure packaging crucial for preservation and safety throughout the food supply chain. In Fiji, agricultural packaging has primarily been managed by local companies for international trade, with gradual advancements in these practices. To further enhance the industry’s performance, this study examines the challenges and constraints hindering the optimization of agricultural packaging practices in Fiji. The study utilizes Multi-Criteria Decision Making (MCDM) tools, specifically Interpretive Structural Modeling (ISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC). ISM analyzes the hierarchical structure of barriers, categorizing them from the least to the most influential, while MICMAC classifies barriers based on their driving and dependence power. This approach helps identify the interrelationships between barriers, providing valuable insights for policymakers and decision-makers to propose innovative solutions for sustainable development in the agricultural packaging sector, ultimately shaping the future of packaging practices in Fiji.

Keywords: agricultural packaging, barriers, ISM, MICMAC

Procedia PDF Downloads 37

Authors: Sreto Boljevic

Abstract:

Heating systems based on geothermal energy sources are becoming increasingly popular among commercial/community buildings as management of these buildings looks for a more efficient and environmentally friendly way to manage the heating system. The thermal energy supply of most European commercial/community buildings at present is provided mainly by energy extracted from natural gas. In order to reduce greenhouse gas emissions and achieve climate change targets set by the EU, restructuring in the area of thermal energy supply is essential. At present, heating and cooling account for approx... 50% of the EU primary energy supply. Due to its physical characteristics, thermal energy cannot be distributed or exchange over long distances, contrary to electricity and gas energy carriers. Compared to electricity and the gas sectors, heating remains a generally black box, with large unknowns to a researcher and policymaker. Ain literature number of documents address policies for promoting renewable energy technology to facilitate heating for residential/community/commercial buildings and assess the balance between heat supply and heat savings. Ground source heat pump (GSHP) technology has been an extremely attractive alternative to traditional electric and fossil fuel space heating equipment used to supply thermal energy for residential/community/commercial buildings. The main purpose of this paper is to create an algorithm using an analytical approach that could enable a feasibility study regarding the implementation of GSHP technology in community building with existing fossil-fueled heating systems. The main results obtained by the algorithm will enable building management and GSHP system designers to define the optimal size of the system regarding technical, environmental, and economic impacts of the system implementation, including payback period time. In addition, an algorithm is created to be utilized for a feasibility study for many different types of buildings. The algorithm is tested on a building that was built in 1930 and is used as a church located in Cork city. The heating of the building is currently provided by a 105kW gas boiler.

Keywords: GSHP, greenhouse gas emission, low-enthalpy, renewable energy

Procedia PDF Downloads 223

Authors: Usama Shahid, Muhammad Zunnurain Hussain

Abstract:

This research paper presents a comprehensive analysis of a sentiment analysis approach that employs valence and arousal as its foundational pillars, in comparison to traditional techniques. Sentiment analysis is an indispensable task in natural language processing that involves the extraction of opinions and emotions from textual data. The valence and arousal dimensions, representing the intensity and positivity/negativity of emotions, respectively, enable the creation of four quadrants, each representing a specific emotional state. The study seeks to determine the impact of utilizing these quadrants to identify distinct emotional states on the accuracy and efficiency of sentiment analysis, in comparison to traditional techniques. The results reveal that the valence and arousal-based approach outperforms other approaches, particularly in identifying nuanced emotions that may be missed by conventional methods. The study's findings are crucial for applications such as social media monitoring and market research, where the accurate classification of emotions and opinions is paramount. Overall, this research highlights the potential of using valence and arousal as a framework for sentiment analysis and offers invaluable insights into the benefits of incorporating specific types of emotions into the analysis. These findings have significant implications for researchers and practitioners in the field of natural language processing, as they provide a basis for the development of more accurate and effective sentiment analysis tools.

Keywords: sentiment analysis, valence and arousal, emotional states, natural language processing, machine learning, text analysis, sentiment classification, opinion mining

Procedia PDF Downloads 105

Authors: Ugochukwu Ejike Akpudo, Jank-Wook Hur

Abstract:

The increasing demand for improved productivity, maintainability, and reliability has prompted rapidly increasing research studies on the emerging condition-based maintenance concept- Prognostics and health management (PHM). Varieties of fuel pumps serve critical functions in several hydraulic systems; hence, their failure can have daunting effects on productivity, safety, etc. The need for condition monitoring and assessment of these pumps cannot be overemphasized, and this has led to the uproar in research studies on standard feature extraction techniques for optimized condition assessment of fuel pumps. By extracting time-based, frequency-based and the more robust time-frequency based features from these vibrational signals, a more comprehensive feature assessment (and selection) can be achieved for a more accurate and reliable condition assessment of these pumps. With the aid of emerging deep classification and regression algorithms like the locally linear embedding (LLE), we propose a method for comprehensive condition assessment of electromagnetic fuel pumps (EMFPs). Results show that the LLE as a comprehensive feature extraction technique yields better feature fusion/dimensionality reduction results for condition assessment of EMFPs against the use of single features. Also, unlike other feature fusion techniques, its capabilities as a fault classification technique were explored, and the results show an acceptable accuracy level using standard performance metrics for evaluation.

Keywords: electromagnetic fuel pumps, comprehensive feature extraction, condition assessment, locally linear embedding, feature fusion

Procedia PDF Downloads 122

Authors: Panagiotis Gkekas, Christos Sougles, Dionysios Kehagias, Dimitrios Tzovaras

Abstract:

Cooperative intelligent transport systems (C-ITS) can greatly improve safety and efficiency in road transport by enabling communication, not only between vehicles themselves but also between vehicles and infrastructure. For that reason, traffic surveillance systems on the road are of great importance. This paper focuses on the development of an on-road unit comprising several magnetic sensors for real-time vehicle detection, movement direction, and speed measurement calculations. Magnetic sensors can feel and measure changes in the earth’s magnetic field. Vehicles are composed of many parts with ferromagnetic properties. Depending on sensors’ sensitivity, changes in the earth’s magnetic field caused by passing vehicles can be detected and analyzed in order to extract information on the properties of moving vehicles. In this paper, we present a prototype algorithm for real-time, high-accuracy, vehicle detection, and speed measurement, which can be implemented as a portable, low-cost, and non-invasive to existing infrastructure solution with the potential to replace existing high-cost implementations. The paper describes the algorithm and presents results from its preliminary lab testing in a close to real condition environment. Acknowledgments: Work presented in this paper was co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation (call RESEARCH–CREATE–INNOVATE) under contract no. Τ1EDK-03081 (project ODOS2020).

Keywords: magnetic sensors, vehicle detection, speed measurement, traffic surveillance system

Procedia PDF Downloads 126

Authors: Muhammet Baldan, Emel Timuçin

Abstract:

Molecular solubility prediction plays a crucial role in various fields, such as drug discovery, environmental science, and material science. In this study, we compare the performance of five machine learning algorithms—linear regression, support vector machines (SVM), random forests, gradient boosting machines (GBM), and neural networks—for predicting molecular solubility using the AqSolDB dataset. The dataset consists of 9981 data points with their corresponding solubility values. MACCS keys (166 bits), RDKit properties (20 properties), and structural properties(3) features are extracted for every smile representation in the dataset. A total of 189 features were used for training and testing for every molecule. Each algorithm is trained on a subset of the dataset and evaluated using metrics accuracy scores. Additionally, computational time for training and testing is recorded to assess the efficiency of each algorithm. Our results demonstrate that random forest model outperformed other algorithms in terms of predictive accuracy, achieving an 0.93 accuracy score. Gradient boosting machines and neural networks also exhibit strong performance, closely followed by support vector machines. Linear regression, while simpler in nature, demonstrates competitive performance but with slightly higher errors compared to ensemble methods. Overall, this study provides valuable insights into the performance of machine learning algorithms for molecular solubility prediction, highlighting the importance of algorithm selection in achieving accurate and efficient predictions in practical applications.

Keywords: random forest, machine learning, comparison, feature extraction

Procedia PDF Downloads 45

Authors: Abhit Kumar

Abstract:

The world is full of master slave Telemanipulator where the doctor’s masters the console and the surgical arm perform the operations, i.e. these robots are passive robots, what the world needs to focus is that in use of these passive robots we are acquiring doctors for operating these console hence the utilization of the concept of robotics is still not fully utilized ,hence the focus should be on active robots, Auto Surgical-Emissive Hand use the similar concept of active robotics where this anthropomorphic hand focuses on the autonomous surgical, emissive and scanning operation, enabled with the vision of 3 way emission of Laser Beam/-5°C < ICY Steam < 5°C/ TIC embedded in palm of the anthropomorphic hand and structured in a form of 3 way disc. Fingers of AS-EH (Auto Surgical-Emissive Hand) as called, will have tactile, force, pressure sensor rooted to it so that the mechanical mechanism of force, pressure and physical presence on the external subject can be maintained, conversely our main focus is on the concept of “emission” the question arises how all the 3 non related methods will work together that to merged in a single programmed hand, all the 3 methods will be utilized according to the need of the external subject, the laser if considered will be emitted via a pin sized outlet, this radiation is channelized via a thin channel which further connect to the palm of the surgical hand internally leading to the pin sized outlet, here the laser is used to emit radiation enough to cut open the skin for removal of metal scrap or any other foreign material while the patient is in under anesthesia, keeping the complexity of the operation very low, at the same time the TIC fitted with accurate temperature compensator will be providing us the real time feed of the surgery in the form of heat image, this gives us the chance to analyze the level, also ATC will help us to determine the elevated body temperature while the operation is being proceeded, the thermal imaging camera in rooted internally in the AS-EH while also being connected to the real time software externally to provide us live feedback. The ICY steam will provide the cooling effect before and after the operation, however for more utilization of this concept we can understand the working of simple procedure in which If a finger remain in icy water for a long time it freezes the blood flow stops and the portion become numb and isolated hence even if you try to pinch it will not provide any sensation as the nerve impulse did not coordinated with the brain hence sensory receptor did not got active which means no sense of touch was observed utilizing the same concept we can use the icy stem to be emitted via a pin sized hole on the area of concern ,temperature below 273K which will frost the area after which operation can be done, this steam can also be use to desensitized the pain while the operation in under process. The mathematical calculation, algorithm, programming of working and movement of this hand will be installed in the system prior to the procedure, since this AS-EH is a programmable hand it comes with the limitation hence this AS-EH robot will perform surgical process of low complexity only.

Keywords: active robots, algorithm, emission, icy steam, TIC, laser

Procedia PDF Downloads 359

Authors: Dao Phuong Nam, Tran Van Tuyen, Do Trong Tan, Bui Minh Dinh, Nguyen Van Huong

Abstract:

In this paper, we investigate the adaptive optimal control law for continuous-time systems with input disturbances and unknown parameters. This paper extends previous works to obtain the robust control law of uncertain systems. Through theoretical analysis, an adaptive dynamic programming (ADP) based optimal control is proposed to stabilize the closed-loop system and ensure the convergence properties of proposed iterative algorithm. Moreover, the global asymptotic stability (GAS) for closed system is also analyzed. The theoretical analysis for continuous-time systems and simulation results demonstrate the performance of the proposed algorithm for an inverted pendulum system.

Keywords: approximate/adaptive dynamic programming, ADP, adaptive optimal control law, input state stability, ISS, inverted pendulum

Procedia PDF Downloads 196

Authors: M. Petunia Tsweleng

Abstract:

Statement of the Problem: Research and aid organisations report that children and adolescents orphaned due to HIV and AIDS are particularly vulnerable as they are often exposed to negative effects of both HIV and AIDS and orphanhood. Without much-needed parental love, care, and support, these children and adolescents are at risk of poor developmental outcomes. A cursory look at the available literature on AIDS-orphaned adolescents, and the quality of caregiving relationships with caregivers, shows that this is a relatively under-researched terrain. This article is a review of the literature on caregiving relationships of adolescents orphaned due to AIDS and their current primary caregivers. It aims to inform community programmes and policymakers by providing insight into the qualities of these relationships. Methodology: A comprehensive search of both peer-reviewed and non-peer-reviewed literature was conducted through EBSCOhost, SpringLINK, PsycINFO, SAGE, PubMed, Elsevier ScienceDirect, JSTOR, Wiley Online Library databases, and Google Scholar. The combination of keywords used for the search were: (caregiving relationships); (orphans OR AIDS orphaned children OR AIDS orphaned adolescents); (primary caregivers); and (quality caregiving); (orphans); (HIV and AIDS). The search took place between 24 January and 28 February 2022. Both qualitative and quantitative research studies published between 2010 and 2020 were reviewed. However, only qualitative studies were selected in the end -as they presented more profound findings concerning orphan-caregiver relationships. The following three stages of meta-synthesis analysis were used to analyse data: refutational syntheses, reciprocal syntheses, and line of argument. Results: The search resulted in a total of 2090 titles, of which 750 were duplicates and therefore subtracted. The researcher reviewed all the titles and abstracts of the remaining 1340 articles. 329 articles were identified as relevant, and full texts were reviewed. Following the review of the full texts, 313 studies were excluded for relevance and 4 for methodology. Twelve articles representing 11 studies fulfilled the inclusion criteria and were selected. These studies, representing different countries across the globe, reported similar forms of hardships experienced by caregivers economically, psychosocially, and healthwise. However, the studies also show that the majority of caregivers found contentment in caring for orphans, particularly grandmother carers, and were thus enabled to provide love, care, and support despite hardships. This resulted in positive caregiving relationships -as orphans fared well emotionally and psychosocially. Some relationships, however, were found negative due to unhealed emotional wounds suffered by both caregivers and orphans and others due to the caregiver’s lack of interest in providing care. These findings were based on self-report data from both orphans and caregivers. Conclusion: Findings suggest that intervention efforts need to be intensified to: alleviate poverty in households that are affected by HIV and AIDS pandemic, strengthen the community psychosocial support programmes for orphans and their caregivers; and integrate clinical services with community programmes for the healing of emotional and psychological wounds. Contributions: Findings inform community programmes and policymakers by providing insight into the qualities of the mentioned relationships as well as identifying factors commonly associated with high-quality caregiving and poor-quality caregiving.

Keywords: systematic review, caregiving relationships, orphans and primary caregivers, AIDS

Procedia PDF Downloads 189

Authors: Mya Achike

Abstract:

Background and Significance: Perinatal mental illness covers a wide range of conditions and has a huge influence on maternal-child health. Issues and challenges with perinatal mental health have been associated with poor pregnancy, birth, and postpartum outcomes. It is estimated that one out of five new and expectant mothers experience some degree of perinatal mental illness, which makes this a hugely significant health outcome. Certain factors increase the maternal risk for mental illness. Challenges related to poverty, migration, extreme stress, exposure to violence, emergency and conflict situations, natural disasters, and pandemics can exacerbate mental health disorders. It is widely expected that perinatal mental health is being negatively affected during the present COVID-19 pandemic. Methods: A review of studies that reported a measurement tool to assess perinatal mental health outcomes during the COVID-19 pandemic was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. PubMed, CINAHL, and Google Scholar were used to search for peer-reviewed studies published after late 2019, in accordance with the emergence of the virus. The search resulted in the inclusion of ten studies. Approach to measure health outcome: The main approach to measure perinatal mental illness is the use of self-administered, validated questionnaires, usually in the clinical setting. Summary: Widespread use of these tools has afforded the clinical and research communities the ability to identify and support women who may be suffering from mental illness disorders during a pandemic. More research is needed to validate tools in other vulnerable, perinatal populations.

Keywords: mental health during covid, perinatal mental health, perinatal mental health measurement tools, perinatal mental health tools

Procedia PDF Downloads 142

Authors: Hamidreza Hoshyarmanesh, Hosein Madieh, Sanju Lama, Yaser Maddahi, Garnette R. Sutherland, Kourosh Zareinia

Abstract:

This paper illustrates the concept of an algorithm to register specified markers on the neuroArm surgical manipulators, an image-guided MR-compatible tele-operated robot for microsurgery and stereotaxy. Two range-finding algorithms, namely time-of-flight and phase-shift, are evaluated for registration and supervisory control. The time-of-flight approach is implemented in a semi-field experiment to determine the precise position of a tiny retro-reflective moving object. The moving object simulates a surgical tool tip. The tool is a target that would be connected to the neuroArm end-effector during surgery inside the magnet bore of the MR imaging system. In order to apply flight approach, a 905-nm pulsed laser diode and an avalanche photodiode are utilized as the transmitter and receiver, respectively. For the experiment, a high frequency time to digital converter was designed using a field-programmable gate arrays. In the phase-shift approach, a continuous green laser beam with a wavelength of 530 nm was used as the transmitter. Results showed that a positioning error of 0.1 mm occurred when the scanner-target point distance was set in the range of 2.5 to 3 meters. The effectiveness of this non-contact approach exhibited that the method could be employed as an alternative for conventional mechanical registration arm. Furthermore, the approach is not limited by physical contact and extension of joint angles.

Keywords: 3D laser scanner, intraoperative MR imaging, neuroArm, real time registration, robot-assisted surgery, supervisory control

Procedia PDF Downloads 290

Authors: S. Sarumathi, C. Deepadharani, Garimella Archana, S. Dakshayani, D. Logeshwaran, D. Jayakumar, Vijayarangan Natarajan

Abstract:

The need of the hour for the customers who use a dial-up or a low broadband connection for their internet services is to access HD video data. This can be achieved by developing a new video format using H. 265. This is the latest video codec standard developed by ISO/IEC Moving Picture Experts Group (MPEG) and ITU-T Video Coding Experts Group (VCEG) on April 2013. This new standard for video compression has the potential to deliver higher performance than the earlier standards such as H. 264/AVC. In comparison with H. 264, HEVC offers a clearer, higher quality image at half the original bitrate. At this lower bitrate, it is possible to transmit high definition videos using low bandwidth. It doubles the data compression ratio supporting 8K Ultra HD and resolutions up to 8192×4320. In the proposed model, we design a new video format which supports this H. 265 standard. The major areas of applications in the coming future would lead to enhancements in the performance level of digital television like Tata Sky and Sun Direct, BluRay Discs, Mobile Video, Video Conferencing and Internet and Live Video streaming.

Keywords: access HD video, H. 265 video standard, high performance, high quality image, low bandwidth, new video format, video streaming applications

Procedia PDF Downloads 357

Authors: Gopi Ram, Durbadal Mandal, Rajib Kar, Sakti Prasad Ghoshal

Abstract:

A design problem of non-uniform circular antenna arrays for maximum reduction of both the side lobe level (SLL) and first null beam width (FNBW) is dealt with. This problem is modeled as a simple optimization problem. The method of Firefly algorithm (FFA) is used to determine an optimal set of current excitation weights and antenna inter-element separations that provide radiation pattern with maximum SLL reduction and much improvement on FNBW as well. Circular array antenna laid on x-y plane is assumed. FFA is applied on circular arrays of 8-, 10-, and 12- elements. Various simulation results are presented and hence performances of side lobe and FNBW are analyzed. Experimental results show considerable reductions of both the SLL and FNBW with respect to those of the uniform case and some standard algorithms GA, PSO, and SA applied to the same problem.

Keywords: circular arrays, first null beam width, side lobe level, FFA

Procedia PDF Downloads 261

Authors: Wen Zhang

Abstract:

The smartphone plays a significant role in media convergence, and smartphone photography is reconstructing the way we communicate and think. This article aims to explore the smartphone photography practices of urban Chinese smartphone users and images produced by smartphones from a techno-cultural perspective. The analysis consists of two types of data: One is a semi-structured interview of 21 participants, and the other consists of the images created by the participants. The findings are organised in two parts. The first part summarises the current tendencies of capturing, editing, sharing and archiving digital images via smartphones. The second part shows that food and selfie/anti-selfie are the preferred subjects of smartphone photographic images from a technical and multi-purpose perspective and demonstrates that screenshots and image texts are new genres of non-photographic images that are frequently made by smartphones, which contributes to improving operational efficiency, disseminating information and sharing knowledge. The analyses illustrate the positive impacts between smartphones and photography enthusiasm and practices based on the diffusion of innovation theory, which also makes us rethink the value of photographs and the practice of ‘photographic seeing’ from the screen itself.

Keywords: digital photography, image-text, media convergence, photographic- seeing, selfie/anti-selfie, smartphone, technological innovation

Procedia PDF Downloads 358

Authors: Kaitlyn Deierlein, Janet Lydecker

Abstract:

Stereotypes surrounding anorexia nervosa are that the illness is commonly perceived as a self-inflicted disorder influenced by controlling parents, vanity, and cultural pressures. According to the authors' best knowledge minimal research has examined interactions with other factors, including gender and racial stereotypes involving this disorder. A common stereotype of this disease is that it mainly only affects Caucasian women and is very rarely seen in any other ethnicity. Previous literature has failed to investigate how visual body image and ethnic stereotypes affect the mental health of different ethnic groups, how various cultures impact the type of anorexia nervosa in the patient, and the different stereotypes associated with their eating disorder. Participants completed a pre-test questionnaire with vignettes, an image exposure portion, and a post-test questionnaire, which will all be evaluated and analyzed by ANOVA t-test and SPSS. Results showed that participants picked Caucasian females as more likely to have anorexia nervosa than those of Asian, Latin American, or African American descent subjects in both picture identification and vignettes. Future research should be conducted to further the results of this study by examining differences between gender stereotypes with anorexia nervosa as well as how sexuality has a role in perception.

Keywords: anorexia nervosa, ethnicity, stereotypes, eating disorders, perception

Procedia PDF Downloads 79

Authors: Raninder Kaur Dhillon, Mayur Jethwa, Hardeep Singh Rai

Abstract:

Information technology has made a pivotal progress across disparate disciplines, one of which is AEC (Architecture, Engineering and Construction) industry. CAD is a form of computer-aided building modulation that architects, engineers and contractors use to create and view two- and three-dimensional models. The AEC industry also uses building information modeling (BIM), a newer computerized modeling system that can create four-dimensional models; this software can greatly increase productivity in the AEC industry. BIM models generate open source IFC (Industry Foundation Classes) files which aim for interoperability for exchanging information throughout the project lifecycle among various disciplines. The methods developed in previous studies require either an IFC schema or MVD and software applications, such as an IFC model server or a Building Information Modeling (BIM) authoring tool, to extract a partial or complete IFC instance model. This paper proposes an efficient algorithm for extracting a partial and total model from an Industry Foundation Classes (IFC) instance model without an IFC schema or a complete IFC model view definition (MVD).

Keywords: BIM, CAD, IFC, MVD

Procedia PDF Downloads 301

Authors: Claudia Ehrentraut, Osama Ibrahim, Hercules Dalianis

Abstract:

Policy making situations are real-world problems that exhibit complexity in that they are composed of many interrelated problems and issues. To be effective, policies must holistically address the complexity of the situation rather than propose solutions to single problems. Formulating and understanding the situation and its complex dynamics, therefore, is a key to finding holistic solutions. Analysis of text based information on the policy problem, using Natural Language Processing (NLP) and Text analysis techniques, can support modelling of public policy problem situations in a more objective way based on domain experts knowledge and scientific evidence. The objective behind this study is to support modelling of public policy problem situations, using text analysis of verbal descriptions of the problem. We propose a formal methodology for analysis of qualitative data from multiple information sources on a policy problem to construct a causal diagram of the problem. The analysis process aims at identifying key variables, linking them by cause-effect relationships and mapping that structure into a graphical representation that is adequate for designing action alternatives, i.e., policy options. This study describes the outline of an algorithm used to automate the initial step of a larger methodological approach, which is so far done manually. In this initial step, inferences about key variables and their interrelationships are extracted from textual data to support a better problem structuring. A small prototype for this step is also presented.

Keywords: public policy, problem structuring, qualitative analysis, natural language processing, algorithm, inference extraction

Procedia PDF Downloads 595

Authors: Wenbo Wang, Yi-Fang Brook Wu

Abstract:

The spread of misinformation and disinformation has become a major concern, particularly with the rise of social media as a primary source of information for many people. As a means to address this phenomenon, automated fact-checking has emerged as a safeguard against the spread of misinformation and disinformation. Existing fact-checking approaches aim to determine whether a news claim is true or false, and they have achieved decent veracity prediction accuracy. However, the state-of-the-art methods rely on manually verified external information to assist the checking model in making judgments, which requires significant human resources. This study introduces a framework, SAC, which focuses on 1) augmenting the representation of a claim by incorporating additional context using general-purpose, comprehensive, and authoritative data; 2) developing a search function to automatically select relevant, new, and credible references; 3) focusing on the important parts of the representations of a claim and its reference that are most relevant to the fact-checking task. The experimental results demonstrate that 1) Augmenting the representations of claims and references through the use of a knowledge base, combined with the multi-head attention technique, contributes to improved performance of fact-checking. 2) SAC with auto-selected references outperforms existing fact-checking approaches with manual selected references. Future directions of this study include I) exploring knowledge graphs in Wikidata to dynamically augment the representations of claims and references without introducing too much noise, II) exploring semantic relations in claims and references to further enhance fact-checking.

Keywords: fact checking, claim verification, deep learning, natural language processing

Procedia PDF Downloads 64

Authors: Vanessa G. P. Severino, JOÃO Gabriel M. Junqueira, Michelle N. G. do Nascimento, Francisco W. B. Aquino, João B. Fernandes, Ana P. Terezan

Abstract:

The scientific interest in analyzing VOCs represents a significant modern research field as a result of importance in most branches of the present life and industry. Therefore it is extremely important to investigate, identify and isolate volatile substances, since they can be used in different areas, such as food, medicine, cosmetics, perfumery, aromatherapy, pesticides, repellents and other household products through methods for extracting volatile constituents, such as solid phase microextraction (SPME), hydrodistillation (HD), solvent extraction (SE), Soxhlet extraction, supercritical fluid extraction (SFE), stream distillation (SD) and vacuum distillation (VD). The Chemometrics is an area of chemistry that uses statistical and mathematical tools for the planning and optimization of the experimental conditions, and to extract relevant chemical information multivariate chemical data. In this context, the focus of this work was the study of the chemical VOCs by SPME of the specie X. aromatica, in search of constituents that can be used in the industrial sector as well as in food, cosmetics and perfumery, since these areas industrial has a considerable role. In addition, by chemometric analysis, we sought to maximize the answers of this research, in order to search for the largest number of compounds. The investigation of flowers from X. aromatica in vitro and in alive mode proved consistent, but certain factors supposed influence the composition of metabolites, and the chemometric analysis strengthened the analysis. Thus, the study of the chemical composition of X. aromatica contributed to the VOCs knowledge of the species and a possible application.

Keywords: chemometrics, flowers, HS-SPME, Xylopia aromatica

Procedia PDF Downloads 367

Authors: H. Arzani, Z. Arzani

Abstract:

Every piece of rangeland has a different set of physical and biological characteristics. This requires the manager to synthesis various information for regular monitoring to define changes trend to get wright decision for sustainable management. So range managers need to use computerized technologies to monitor rangeland, and select. The best management practices. There are four examples of computerized technologies that can benefit sustainable management: (1) Photographic method for cover measurement: The method was tested in different vegetation communities in semi humid and arid regions. Interpretation of pictures of quadrats was done using Arc View software. Data analysis was done by SPSS software using paired t test. Based on the results, generally, photographic method can be used to measure ground cover in most vegetation communities. (2) GPS application for corresponding ground samples and satellite pixels: In two provinces of Tehran and Markazi, six reference points were selected and in each point, eight GPS models were tested. Significant relation among GPS model, time and location with accuracy of estimated coordinates was found. After selection of suitable method, in Markazi province coordinates of plots along four transects in each 6 sites of rangelands was recorded. The best time of GPS application was in the morning hours, Etrex Vista had less error than other models, and a significant relation among GPS model, time and location with accuracy of estimated coordinates was found. (3) Application of satellite data for rangeland monitoring: Focusing on the long term variation of vegetation parameters such as vegetation cover and production is essential. Our study in grass and shrub lands showed that there were significant correlations between quantitative vegetation characteristics and satellite data. So it is possible to monitor rangeland vegetation using digital data for sustainable utilization. (4) Rangeland suitability classification with GIS: Range suitability assessment can facilitate sustainable management planning. Three sub-models of sensitivity to erosion, water suitability and forage production out puts were entered to final range suitability classification model. GIS was facilitate classification of range suitability and produced suitability maps for sheep grazing. Generally digital computers assist range managers to interpret, modify, calibrate or integrating information for correct management.

Keywords: computer, GPS, GIS, remote sensing, photographic method, monitoring, rangeland ecosystem, management, suitability, sheep grazing

Procedia PDF Downloads 371

Authors: Maik Kschischo, Dominik Kahl, Philipp Wendland, Andreas Weber

Abstract:

Understanding and modelling of real-world complex dynamic systems in biology, engineering and other fields is often made difficult by incomplete knowledge about the interactions between systems states and by unknown disturbances to the system. In fact, most real-world dynamic networks are open systems receiving unknown inputs from their environment. To understand a system and to estimate the state dynamics, these inputs need to be reconstructed from output measurements. Reconstructing the input of a dynamic system from its measured outputs is an ill-posed problem if only a limited number of states is directly measurable. A first requirement for solving this problem is the invertibility of the input-output map. In our work, we exploit the fact that invertibility of a dynamic system is a structural property, which depends only on the network topology. Therefore, it is possible to check for invertibility using a structural invertibility algorithm which counts the number of node disjoint paths linking inputs and outputs. The algorithm is efficient enough, even for large networks up to a million nodes. To understand structural features influencing the invertibility of a complex dynamic network, we analyze synthetic and real networks using the structural invertibility algorithm. We find that invertibility largely depends on the degree distribution and that dense random networks are easier to invert than sparse inhomogeneous networks. We show that real networks are often very difficult to invert unless the sensor nodes are carefully chosen. To overcome this problem, we present a sensor node placement algorithm to achieve invertibility with a minimum set of measured states. This greedy algorithm is very fast and also guaranteed to find an optimal sensor node-set if it exists. Our results provide a practical approach to experimental design for open, dynamic systems. Since invertibility is a necessary condition for unknown input observers and data assimilation filters to work, it can be used as a preprocessing step to check, whether these input reconstruction algorithms can be successful. If not, we can suggest additional measurements providing sufficient information for input reconstruction. Invertibility is also important for systems design and model building. Dynamic models are always incomplete, and synthetic systems act in an environment, where they receive inputs or even attack signals from their exterior. Being able to monitor these inputs is an important design requirement, which can be achieved by our algorithms for invertibility analysis and sensor node placement.

Keywords: data-driven dynamic systems, inversion of dynamic systems, observability, experimental design, sensor node placement

Procedia PDF Downloads 153

Authors: Sumeeta Khurana, Kirti Megha, Amit Gupta, Rakesh Sehgal

Abstract:

Background: Amoebic keratitis is a painful vision threatening infection caused by a free living pathogenic amoeba Acanthamoeba. It can be misdiagnosed and very difficult to treat if not suspected early. The epidemiology of Acanthamoeba genotypes causing infection in our geographical area is not yet known to the best of our knowledge. Objective: To characterize Acanthamoeba isolates from amoebic keratitis patients. Methods: A total of 19 isolates obtained from patients with amoebic keratitis presenting to the Advanced Eye Centre at Postgraduate Institute of Medical Education and Research, a tertiary care centre of North India over a period of last 10 years were included. Their corneal scrapings, lens solution and lens case (in case of lens wearer) were collected for microscopic examination, culture and molecular diagnosis. All the isolates were maintained in the Non Nutrient agar culture medium overlaid with E.coli and 13 strains were axenised and maintained in modified Peptone Yeast Dextrose Agar. Identification of Acanthamoeba genotypes was based on amplification of diagnostic fragment 3 (DF3) region of the 18srRNA gene followed by sequencing. Nucleotide similarity search was performed by BLAST search of sequenced amplicons in GenBank database (http//www.ncbi.nlm.nih.gov/blast). Multiple Sequence alignments were determined by using CLUSTAL X. Results: Nine out of 19 Acanthamoeba isolates were found to belong to Genotype T4 followed by 6 isolates of genotype T11, 3 T5 and 1 T3 genotype. Conclusion: T4 is the predominant Acanthamoeba genotype in our geographical area. Further studies should focus on differences in pathogenicity of these genotypes and their clinical significance.

Keywords: Acanthamoeba, free living amoeba, keratitis, genotype, ocular

Procedia PDF Downloads 240

Authors: John A. Morales, Guillermo Guidi, B. M. Keune

Abstract:

Electric Power supply is a crucial topic for economic and social development. Power outages statistics show that discharges atmospherics are imperative phenomena to produce those outages. In this context, it is necessary to correctly detect when overhead line insulators are faulted. In this paper, an algorithm to detect if a lightning stroke generates or not permanent fault on insulator strings is proposed. On top of that, lightning stroke simulations developed by using the Alternative Transients Program, are used. Based on these insights, a novel approach is designed that depends on mother functions analysis corresponding to the given variance-covariance matrix. Signals registered at the insulator string are projected on corresponding axes by the means of Principal Component Analysis. By exploiting these new axes, it is possible to determine a flashover characteristic zone useful to a good insulation design. The proposed methodology for flashover detection extends the existing approaches for the analysis and study of lightning performance on transmission lines.

Keywords: mother function, outages, lightning, sensitivity analysis

Procedia PDF Downloads 589

Authors: M. D. D. Chathurangi, M. G. K. Nayanathara, K. M. H. M. M. Gunapala, G. M. R. G. Dayananda, Kavinga Yapa Abeywardena, Deemantha Siriwardana

Abstract:

Due to the growing popularity of social media platforms at present, there are various concerns, mostly cyberbullying, spam, bot accounts, and the spread of incorrect information. To develop a risk score calculation system as a thorough method for deciphering and exposing unethical social media profiles, this research explores the most suitable algorithms to our best knowledge in detecting the mentioned concerns. Various multiple models, such as Naïve Bayes, CNN, KNN, Stochastic Gradient Descent, Gradient Boosting Classifier, etc., were examined, and the best results were taken into the development of the risk score system. For cyberbullying, the Logistic Regression algorithm achieved an accuracy of 84.9%, while the spam-detecting MLP model gained 98.02% accuracy. The bot accounts identifying the Random Forest algorithm obtained 91.06% accuracy, and 84% accuracy was acquired for fake news detection using SVM.

Keywords: cyberbullying, spam behavior, bot accounts, fake news, machine learning

Procedia PDF Downloads 43

Authors: Bharatendra Rai

Abstract:

Predictive data analysis and modeling involving machine learning techniques become challenging in presence of too many explanatory variables or features. Presence of too many features in machine learning is known to not only cause algorithms to slow down, but they can also lead to decrease in model prediction accuracy. This study involves housing dataset with 79 quantitative and qualitative features that describe various aspects people consider while buying a new house. Boruta algorithm that supports feature selection using a wrapper approach build around random forest is used in this study. This feature selection process leads to 49 confirmed features which are then used for developing predictive random forest models. The study also explores five different data partitioning ratios and their impact on model accuracy are captured using coefficient of determination (r-square) and root mean square error (rsme).

Keywords: housing data, feature selection, random forest, Boruta algorithm, root mean square error

Procedia PDF Downloads 327

Authors: Johnson Rotimi Oluremi, A. Adedayo Adegbola, A. Samson Adediran, O. Solomon Oladapo

Abstract:

Spent engine oil contains heavy metals and polycyclic aromatic hydrocarbons which contribute to chronic health hazards, poor soil aeration, immobilisation of nutrients and lowering of pH in soil. It affects geotechnical properties of lateritic soil thereby constituting geotechnical and foundation problems. This study is therefore based on the stabilization of spent engine oil (SEO) contaminated lateritic soil using cement kiln dust (CKD) as a mean of restoring it to its pristine state. Geotechnical tests which include sieve analysis, atterberg limit, compaction, California bearing ratio and unconfined compressive strength tests were carried out on the natural, SEO contaminated and CKD stabilized SEO contaminated lateritic soil samples. The natural soil classified as A-2-7 (2) by AASHTO classification and GC according to the Unified Soil Classification System changed to A-4 non-plastic soil due to SEO contaminated even under the influence of CKD it remained unchanged. However, the maximum dry density (MDD) of the SEO contaminated soil increased while the optimum moisture content (OMC) behaved vice versa with the increase in the percentages of CKD. Similarly, the bearing strength of the stabilized SEO contaminated soil measured by California Bearing Ratio (CBR) increased with percentage increment in CKD. In conclusion, spent engine oil has a detrimental effect on the geotechnical properties of the lateritic soil sample but which can be remediated using 10% CKD as a stand alone admixture in stabilizing spent engine oil contaminated soil.

Keywords: spent engine oil, lateritic soil, cement kiln dust, stabilization, compaction, unconfined compressive strength

Procedia PDF Downloads 395

Authors: Hongbo Zhang, Xinlu Tang, Jiangwei Li, Chi Yan

Abstract:

Home zone parking pilot (HPP) is a fast-growing segment in low-speed autonomous driving applications. It requires the car automatically cruise around a parking lot and park itself in a range of up to 100 meters inside a recurrent home/office parking lot, which requires precise parking lot mapping and localization solution. Although Lidar is ideal for SLAM, the car OEMs favor a low-cost fish-eye camera based visual SLAM approach. Recent approaches have employed segmentation models to extract semantic features and improve mapping accuracy, but these AI models are memory unfriendly and computationally expensive, making deploying on embedded ADAS systems difficult. To address this issue, we proposed a new method that utilizes object detection models to extract robust and accurate parking lot features. The proposed method could reduce computational costs while maintaining high accuracy. Once combined with vehicles’ wheel-pulse information, the system could construct maps and locate the vehicle in real-time. This article will discuss in detail (1) the fish-eye based Around View Monitoring (AVM) with transparent chassis images as the inputs, (2) an Object Detection (OD) based feature point extraction algorithm to generate point cloud, (3) a low computational parking lot mapping algorithm and (4) the real-time localization algorithm. At last, we will demonstrate the experiment results with an embedded ADAS system installed on a real car in the underground parking lot.

Keywords: ADAS, home zone parking pilot, object detection, visual SLAM

Procedia PDF Downloads 71