Search results for: ABC-VED inventory classification

Authors: Belkacem Selma, Boumediene Selma, Tourkia Guerzou, Abbes Labdelli

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Natural disasters have occurred and will continue to cause human and material damage. Therefore, the idea of "preventing" natural disasters will never be possible. However, their prediction is possible with the advancement of technology. Even if natural disasters are effectively inevitable, their consequences may be partly controlled. The rapid growth and progress of artificial intelligence (AI) had a major impact on the prediction of natural disasters and risk assessment which are necessary for effective disaster reduction. The Earthquakes prediction to prevent the loss of human lives and even property damage is an important factor; that is why it is crucial to develop techniques for predicting this natural disaster. This present study aims to analyze the ability of artificial neural networks (ANNs) to predict earthquakes that occur in a given area. The used data describe the problem of high energy (higher than 10^4J) seismic bumps forecasting in a coal mine using two long walls as an example. For this purpose, seismic bumps data obtained from mines has been analyzed. The results obtained show that the ANN with high accuracy was able to predict earthquake parameters; the classification accuracy through neural networks is more than 94%, and that the models developed are efficient and robust and depend only weakly on the initial database.

Keywords: earthquake prediction, ANN, seismic bumps

Procedia PDF Downloads 128

Authors: Ebenezer O. Olaniyi, Oyebade K. Oyedotun, Khashman Adnan

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In the food industry high quality production is required within a limited time to meet up with the demand in the society. In this research work, we have developed a model which can be used to replace the human operator due to their low output in production and slow in making decisions as a result of an individual differences in deciding the defective and healthy banana. This model can perform the vision attributes of human operators in deciding if the banana is defective or healthy for food production based. This research work is divided into two phase, the first phase is the image processing where several image processing techniques such as colour conversion, edge detection, thresholding and morphological operation were employed to extract features for training and testing the network in the second phase. These features extracted in the first phase were used in the second phase; the classification system phase where the multilayer perceptron using backpropagation neural network was employed to train the network. After the network has learned and converges, the network was tested with feedforward neural network to determine the performance of the network. From this experiment, a recognition rate of 97% was obtained and the time taken for this experiment was limited which makes the system accurate for use in the food industry.

Keywords: banana, food processing, identification system, neural network

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Authors: Qasim Idrees, Gao Liangtian, Liu Bo, Miao Yiran

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In order to improve the efficiency and accuracy of the pressure hull structure, optimization of underwater vehicle based on response surface methodology, a method for optimizing the design of pressure hull structure was studied. To determine the pressure shell of five dimensions as a design variable, the application of thin shell theory and the Chinese Classification Society (CCS) specification was carried on the preliminary design. In order to optimize variables of the feasible region, different methods were studied and implemented such as Opt LHD method (to determine the design test sample points in the feasible domain space), parametric ABAQUS solution for each sample point response, and the two-order polynomial response for the surface model of the limit load of structures. Based on the ultimate load of the structure and the quality of the shell, the two-generation genetic algorithm was used to solve the response surface, and the Pareto optimal solution set was obtained. The final optimization result was 41.68% higher than that of the initial design, and the shell quality was reduced by about 27.26%. The parametric method can ensure the accuracy of the test and improve the efficiency of optimization.

Keywords: parameterization, response surface, structure optimization, pressure hull

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Authors: Fatma El-Zahraa El-Taher, Ayman Taha, Jane Courtney, Susan Mckeever

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Understanding road networks plays an important role in navigation applications such as self-driving vehicles and route planning for individual journeys. Intersections of roads are essential components of road networks. Understanding the features of an intersection, from a simple T-junction to larger multi-road junctions, is critical to decisions such as crossing roads or selecting the safest routes. The identification and profiling of intersections from satellite images is a challenging task. While deep learning approaches offer the state-of-the-art in image classification and detection, the availability of training datasets is a bottleneck in this approach. In this paper, a labelled satellite image dataset for the intersection recognition problem is presented. It consists of 14,692 satellite images of Washington DC, USA. To support other users of the dataset, an automated download and labelling script is provided for dataset replication. The challenges of construction and fine-grained feature labelling of a satellite image dataset is examined, including the issue of how to address features that are spread across multiple images. Finally, the accuracy of the detection of intersections in satellite images is evaluated.

Keywords: satellite images, remote sensing images, data acquisition, autonomous vehicles

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Authors: Basit Aftab, Feng Zhongke

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The land use/land cover change (LULCC) is a significant indicator sensitive to an area's environmental changes. As a rapidly developing capital city near the Himalayas Mountains, the city area of Islamabad, Pakistan, has expanded dramatically over the past 20 years. In order to precisely measure the impact of urbanization on the forest and agricultural lands, the Spatio-temporal analysis of LULCC was utilized, which helped us to know the impacts of urbanization, especially on ecosystem processes, biological cycles, and biodiversity. The Islamabad region's Multispectral Satellite Images (MSI) for 2000, 2010, and 2020 were employed as the remote sensing data source. Local documents of city planning, forest inventory and archives in the agriculture management departments were included to verify the image-derived result. The results showed that from 2000 to 2020, the built-up area increased to 48.3% (505.02 Km2). Meanwhile, the forest, agricultural, and barre land decreased to 28.9% (305.64 Km2), 10.04% (104.87 Km2), and 11.61% (121.30 Km2). The overall percentage change in land area between 2000 – 2020 was recorded maximum for the built-up (227.04%). Results revealed that the increase in the built-up area decreased forestland, barren, and agricultural lands (-0.36, -1.00 & -0.34). The association of built-up with respective years was positively linear (R2 = 0.96), whereas forestland, agricultural, and barren lands association with years were recorded as negatively linear (R2 = -0.29, R2 = -0.02, and R2 = -0.96). Large-scale deforestation leads to multiple negative impacts on the local environment, e.g., water degradation and climate change. It would finally affect the environment of the greater Himalayan region in some way. We further analyzed the driving forces of urbanization. It was determined by economic expansion, climate change, and population growth. We hope our study could be utilized to develop efforts to mitigate the consequences of deforestation and agricultural land damage, reducing greenhouse gas emissions while preserving the area's biodiversity.

Keywords: urbanization, Himalaya mountains, landuse landcover change (LULCC), remote sensing., multi-spectral satellite imagery

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Authors: Iftikhar A. Tayubi, Aabdulrahman Alsubhi, Abdullah Althrwi

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The deoxyribonucleic acid text provides a single source of high-quality Cryptography about Deoxyribonucleic acid sequence for structural biologists. We will provide an intuitive, well-organized and user-friendly web interface that allows users to encrypt and decrypt Deoxy Ribonucleic Acid sequence text. It includes complex, securing by using Algorithm to encrypt and decrypt Deoxy Ribonucleic Acid sequence. The utility of this Deoxy Ribonucleic Acid Sequence Text is that, it can provide a user-friendly interface for users to Encrypt and Decrypt store the information about Deoxy Ribonucleic Acid sequence. These interfaces created in this project will satisfy the demands of the scientific community by providing fully encrypt of Deoxy Ribonucleic Acid sequence during this website. We have adopted a methodology by using C# and Active Server Page.NET for programming which is smart and secure. Deoxy Ribonucleic Acid sequence text is a wonderful piece of equipment for encrypting large quantities of data, efficiently. The users can thus navigate from one encoding and store orange text, depending on the field for user’s interest. Algorithm classification allows a user to Protect the deoxy ribonucleic acid sequence from change, whether an alteration or error occurred during the Deoxy Ribonucleic Acid sequence data transfer. It will check the integrity of the Deoxy Ribonucleic Acid sequence data during the access.

Keywords: algorithm, ASP.NET, DNA, encrypt, decrypt

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Authors: Adriana Diaz Triana, Wolfgang Wimmer, Sebastian Glaser, Rainer Pamminger

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SUSTAINEXT is a pioneer initiative in Extremadura, Spain under the EU Biobased industries. SUSTANEXT will scale-up and validate an industrial facility to produce botanical extracts, based on three key pillars. First, the whole valorization of bio-based feedstocks with a zero-waste and zero-emissions ambition. SUSTAINEXT will be deployed with six feedstocks. Three medicinal and aromatic plants (Rosemary, Chamomile, and Lemon verbena) will be locally sourced from disused tobacco fields with installed agri-voltaics; and three underexploited agro-industrial side streams will be further valorized (Olive, artichoke-cardoon, and pomegranate). Second, a dynamic, analytical biorefinery (DYANA) will isolate polyphenol and tri-terpenes from feedstocks in a disruptive and circular way. SUSTAINEXT explores 12 valorization routes (VRs) to extract and purify 46 functional ingredients, of which 13 are new in the market and 12 are newly produced in Europe. Third, the integrated and versatile value chain engages all actors, from feedstocks suppliers to extract users in the industries of food, animal feed, nutraceuticals, cosmetics, chemical performance, soil enhancers and fertilizers. This paper addresses SUTAINEXT activities towards zero impacts and full regulatory compliance. A comprehensive Life Cycle Thinking approach is proposed, with four complementary assessments running iteratively along the project duration (4,5 years). These are the Life Cycle Cost (LCCA), Life Cycle (LCA), Social Life Cycle (S-LCA) and Circularity (CA) assessments. The LCA will help evaluate the feedstock suitability parameters and intrinsic characteristics that quantify the feedstock´s grade for a determined use, and the feedstock´s suitability index for a specific VR. The LCA will also study the emissions, land use change, energy generation and consumption, and other environmental aspects and impacts of the VRs, to identify the most resource efficient and less impactful distribution of products from the circular biorefinery model used in SUSTAINEXT. Challenges to complete the LCA include the definition of the system boundaries, carrying out a robust inventory, and the proper allocation of impacts to the different VRs.

Keywords: biorefinery, botanical extracts, life cycle assessment, valorization routes.

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Authors: Luis Fonseca, Filipe Cabral Pinto, Susana Sargento

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The increase of the world population, especially in large urban centers, has resulted in new challenges particularly with the control and optimization of public safety. Thus, in the present work, a solution is proposed for the prediction of criminal occurrences in a city based on historical data of incidents and demographic information. The entire research and implementation will be presented start with the data collection from its original source, the treatment and transformations applied to them, choice and the evaluation and implementation of the Machine Learning model up to the application layer. Classification models will be implemented to predict criminal risk for a given time interval and location. Machine Learning algorithms such as Random Forest, Neural Networks, K-Nearest Neighbors and Logistic Regression will be used to predict occurrences, and their performance will be compared according to the data processing and transformation used. The results show that the use of Machine Learning techniques helps to anticipate criminal occurrences, which contributed to the reinforcement of public security. Finally, the models were implemented on a platform that will provide an API to enable other entities to make requests for predictions in real-time. An application will also be presented where it is possible to show criminal predictions visually.

Keywords: crime prediction, machine learning, public safety, smart city

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Authors: Khaled Nagaty, Heba Nagaty, Gerard McKee

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A signature is a behavioral biometric that is used for authenticating users in most financial and legal transactions. Signatures can be easily forged by skilled forgers. Therefore, it is essential to verify whether a signature is genuine or forged. The aim of any signature verification algorithm is to accommodate the differences between signatures of the same person and increase the ability to discriminate between signatures of different persons. This work presented in this paper proposes an automatic signature verification system to indicate whether a signature is genuine or not. The system comprises four phases: (1) The pre-processing phase in which image scaling, binarization, image rotation, dilation, thinning, and connecting ridge breaks are applied. (2) The feature extraction phase in which global and local features are extracted. The local features are minutiae points, curvature orientation, and curve plateau. The global features are signature area, signature aspect ratio, and Hu moments. (3) The post-processing phase, in which false minutiae are removed. (4) The classification phase in which features are enhanced before feeding it into the classifier. k-nearest neighbors and support vector machines are used. The classifier was trained on a benchmark dataset to compare the performance of the proposed offline signature verification system against the state-of-the-art. The accuracy of the proposed system is 92.3%.

Keywords: signature, ridge breaks, minutiae, orientation

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Authors: Mo-Se Lee, Cheol-Hwi Ahn, Kee-Young Kwahk, Hyunchul Ahn

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Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN (Convolutional Neural Network), which is known as effective solution for recognizing and classifying images, has been popularly applied to classification and prediction problems in various fields. In this study, we try to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. In specific, we propose to apply CNN as the binary classifier that predicts stock market direction (up or down) by using a graph as its input. That is, our proposal is to build a machine learning algorithm that mimics a person who looks at the graph and predicts whether the trend will go up or down. Our proposed model consists of four steps. In the first step, it divides the dataset into 5 days, 10 days, 15 days, and 20 days. And then, it creates graphs for each interval in step 2. In the next step, CNN classifiers are trained using the graphs generated in the previous step. In step 4, it optimizes the hyper parameters of the trained model by using the validation dataset. To validate our model, we will apply it to the prediction of KOSPI200 for 1,986 days in eight years (from 2009 to 2016). The experimental dataset will include 14 technical indicators such as CCI, Momentum, ROC and daily closing price of KOSPI200 of Korean stock market.

Keywords: convolutional neural network, deep learning, Korean stock market, stock market prediction

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Authors: Cheng-Jhe Lee, Chiun-Chieh Hsu

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Due to the rapid development of the Internet and the increased availability of digital documents, the excessive information on the Internet has led to information overflow problem. In order to solve these problems for effective information retrieval, document clustering in text mining becomes a popular research topic. Clustering is the unsupervised classification of data items into groups without the need of training data. Many conventional document clustering methods perform inefficiently for large document collections because they were originally designed for relational database. Therefore they are impractical in real-world document clustering and require special handling for high dimensionality and high volume. We propose the FIHC (Frequent Itemset-based Hierarchical Clustering) method, which is a hierarchical clustering method developed for document clustering, where the intuition of FIHC is that there exist some common words for each cluster. FIHC uses such words to cluster documents and builds hierarchical topic tree. In this paper, we combine FIHC algorithm with ontology to solve the semantic problem and mine the meaning behind the words in documents. Furthermore, we use the closed frequent itemsets instead of only use frequent itemsets, which increases efficiency and scalability. The experimental results show that our method is more accurate than those of well-known document clustering algorithms.

Keywords: FIHC, documents clustering, ontology, closed frequent itemset

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Authors: Wan-Ting Liao, Angela K. Tzeng

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Empathy has been an important issue in psychology, education, as well as cognitive neuroscience. Empathy has two major components: cognitive and emotional. Cognitive component refers to the ability to understand others’ perspectives, thoughts, and actions, whereas emotional component refers to understand how others feel. Empathy can be induced, attitude can then be changed, and with enough attitude change, helping behavior can occur. This finding leads us to two questions: is attitude change really necessary for prosocial behavior? And, what roles cognitive and affective empathy play? For the second question, participants with different psychopathic personality (PP) traits are critical because high PP people were found to suffer only affective empathy deficit. Their cognitive empathy shows no significant difference from the control group. 132 college students voluntarily participated in the current three-stage study. Stage 1 was to collect basic information including Interpersonal Reactivity Index (IRI), Psychopathic Personality Inventory-Revised (PPI-R), Attitude Scale, Visual Analogue Scale (VAS), and demographic data. Stage two was for empathy induction with three controversial scenarios, namely domestic violence, depression with a suicide attempt, and an ex-offender. Participants read all three stories and then rewrite the stories by one of two perspectives (empathetic vs. objective). They would then complete the VAS and Attitude Scale one more time for their post-attitude and emotional status. Three IVs were introduced for data analysis: PP (High vs. Low), Responsibility (whether or not the character is responsible for what happened), and Perspective-taking (Empathic vs. Objective). Stage 3 was for the action. Participants were instructed to freely use the 17 tokens they received as donations. They were debriefed and interviewed at the end of the experiment. The major findings were people with higher empathy tend to take more action in helping. Attitude change is not necessary for prosocial behavior. The controversy of the scenarios and how familiar participants are towards target groups play very important roles. Finally, people with high PP tend to show more public prosocial behavior due to their affective empathy deficit. Pre-existing value and belief as well as recent dramatic social events seem to have a big impact and possibly reduce the effect of the independent variables (IV) in our paradigm.

Keywords: empathy, cognitive, emotional, psychopathy

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Authors: Mina Adel Shokry Fahim, Jūratė Sužiedelytė Visockienė

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With the growing concern over air pollution (AP), it is clear that this has gained more prominence than ever before. The level of consciousness has increased and a sense of knowledge now has to be forwarded as a duty by those enlightened enough to disseminate it to others. This realisation often comes after an understanding of how poor air quality indices (AQI) damage human health. The study focuses on assessing air pollution prediction models specifically for Lithuania, addressing a substantial need for empirical research within the region. Concentrating on Vilnius, it specifically examines particulate matter concentrations 10 micrometers or less in diameter (PM10). Utilizing Gaussian Process Regression (GPR) and Regression Tree Ensemble, and Regression Tree methodologies, predictive forecasting models are validated and tested using hourly data from January 2020 to December 2022. The study explores the classification of AP data into anthropogenic and natural sources, the impact of AP on human health, and its connection to cardiovascular diseases. The study revealed varying levels of accuracy among the models, with GPR achieving the highest accuracy, indicated by an RMSE of 4.14 in validation and 3.89 in testing.

Keywords: air pollution, anthropogenic and natural sources, machine learning, Gaussian process regression, tree ensemble, forecasting models, particulate matter

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Authors: Nikola Tomasevic, Marko Batic, Sanja Vranes

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Emerging Semantic Web technologies can be seen as the next step in evolution of the intelligent facility management systems. Particularly, this considers increased usage of open source and/or standardized concepts for data classification and semantic interpretation. To deliver such facility management systems, providing the comprehensive integration and interoperability platform in from of the facility data model is a prerequisite. In this paper, one of the possible modelling approaches to provide such integrative facility data model which was based on the ontology modelling concept was presented. Complete ontology development process, starting from the input data acquisition, ontology concepts definition and finally ontology concepts population, was described. At the beginning, the core facility ontology was developed representing the generic facility infrastructure comprised of the common facility concepts relevant from the facility management perspective. To develop the data model of a specific facility infrastructure, first extension and then population of the core facility ontology was performed. For the development of the full-blown facility data models, Malpensa and Fiumicino airports in Italy, two major European air-traffic hubs, were chosen as a test-bed platform. Furthermore, the way how these ontology models supported the integration and interoperability of the overall airport energy management system was analyzed as well.

Keywords: airport ontology, energy management, facility data model, ontology modeling

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Authors: Win Thandar Soe, Innocent Mpawenimana, Mathieu Di Fazio, Cécile Belleudy, Aung Ze Ya

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Intelligent electronic equipment and automation network is the brain of high-tech energy management systems in critical role of smart homes dominance. Smart home is a technology integration for greater comfort, autonomy, reduced cost, and energy saving as well. These services can be provided to home owners for managing their home appliances locally or remotely and consequently allow them to automate intelligently and responsibly their consumption by individual or collective control systems. In this study, three smart plugs are described and one of them tested on typical household appliances. This article proposes to collect the data from the wireless technology and to extract some smart data for energy management system. This smart data is to quantify for three kinds of load: intermittent load, phantom load and continuous load. Phantom load is a waste power that is one of unnoticed power of each appliance while connected or disconnected to the main. Intermittent load and continuous load take in to consideration the power and using time of home appliances. By analysing the classification of loads, this smart data will be provided to reduce the communication of wireless sensor network for energy management system.

Keywords: energy management, load profile, smart plug, wireless sensor network

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Authors: Badriya Almutairi, Ashraf El-Hamalawi

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Wind energy technologies are considered to be among the most promising types of renewable energy sources due to the growing concerns over climate change and energy security. Kuwait is amongst the countries that began realising the consequences of climate change and the long-term economic and energy security situation, considering options when oil runs out. Added to this are the fluctuating oil prices, rapid increase in population, high electricity consumption and protection of the environment It began to make efforts in the direction of greener solutions for energy needs by looking for alternative forms of energy and assessing potential renewable energy resources, including wind and solar. The aim of this paper is to examine wind energy as an alternative renewable energy source in Kuwait, due to its availability and low cost, reducing the dependency on fossil fuels compared to other forms of renewable energy. This paper will present a life cycle assessment of onshore wind turbine systems in Kuwait, comprising 4 stages; goal and scope of the analysis, inventory analysis, impact assessment and interpretation of the results. It will also provide an assessment of potential renewable energy resources and technologies applied for power generation and the environmental benefits for Kuwait. An optimum location for a site (Shagaya) will be recommended for reasons such as high wind speeds, land availability and distance to the next grid connection, and be the focus of this study. The potential environmental impacts and resources used throughout the wind turbine system’s life-cycle are then analysed using a Life Cycle Assessment (LCA). The results show the total carbon dioxide (CO₂) emission for a turbine with steel pile foundations is greater than emissions from a turbine with concrete foundations by 18 %. The analysis also shows the average CO₂ emissions from electricity generated using crude oil is 645gCO₂/kWh and the carbon footprint per functional unit for a wind turbine ranges between 6.6 g/kWh to 10 g/kWh, an increase of 98%, thus providing cost and environmental benefits by creating a wind farm in Kuwait. Using a cost-benefit analysis, it was also found that the electricity produced from wind energy in Kuwait would cost 17.6fils/kWh (0.05834 $/kWh), which is less than the cost of electricity currently being produced using conventional methods at 22 fils/kW (0.07$/kWh), i.e., a reduction of 20%.

Keywords: CO₂ emissions, Kuwait, life cycle assessment, renewable energy, wind energy

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Authors: Ebru Turgal, Beyza Doganay Erdogan

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Machine learning aims to model the relationship between the response and features. Medical decision-making researchers would like to make decisions about patients’ course and treatment, by examining the repeated measurements over time. Boosting approach is now being used in machine learning area for these aims as an influential tool. The aim of this study is to show the usage of multivariate tree boosting in this field. The main reason for utilizing this approach in the field of decision-making is the ease solutions of complex relationships. To show how multivariate tree boosting method can be used to identify important features and feature-time interaction, we used the data, which was collected retrospectively from Ankara University Chest Diseases Department records. Dataset includes repeated PF ratio measurements. The follow-up time is planned for 120 hours. A set of different models is tested. In conclusion, main idea of classification with weighed combination of classifiers is a reliable method which was shown with simulations several times. Furthermore, time varying variables will be taken into consideration within this concept and it could be possible to make accurate decisions about regression and survival problems.

Keywords: boosted multivariate trees, longitudinal data, multivariate regression tree, panel data

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Authors: Christine Eickhoff, Alyssa Adams, Alaine Duncan

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The Washington, DC Veterans Affairs Medical Center (DC VAMC) offers complementary and integrative health (CIH) services such as acupuncture, yoga, meditation, and nutrition education through a coordinated outpatient clinic. The primary population utilizing CIH services are veterans with chronic pain. Acupuncture is one of the most popular of the CIH services available at the DC VAMC. As interest and availability grows, it is important to measure health outcomes associated with CIH service utilization. The purpose of this study was to investigate pain and mental health outcomes for veterans with chronic pain enrolled in individual acupuncture services in the DC VAMC. Veterans at the DC VAMC with self-identified chronic pain and no prior acupuncture experience were recruited for the study (n=70). Veterans were referred for services by a medical provider and completed baseline assessments at the program orientation prior to participating in any CIH services. Veterans received four individual, full-body acupuncture appointments within four weeks of study enrollment. After the first month, participants were scheduled for six appointments that occurred every two weeks and then eight more sessions that were scheduled one month apart. Follow-up assessments were administered at 2, 4, 6, 8, and 12 months. The findings reported will include completed time points at two and four months. Measures include a demographics survey, the Measure Yourself Medical Outcome Profile-2 (MYMOP-2), The Beck Depression Inventory (BDI-II), the Defense Veterans Pain Rating Scale (DVPRS), and the Pain Disability Questionnaire (PDQ). In this sample, 67% identified a pain condition as their primary health concern. Between baseline and two-month follow-up, there were significant improvements in participants’ primary health concern (MYMOP-2 p=0.010), general wellbeing (MYMOP-2 p=0.011), and a significant decrease in the use of medication (MYMOP-2 p<0.000). Between 2 and 4-month follow-up, pain disability (PDQ p=0.035), pain rating (DVPRS p=0.027), and depression (BDI-II p=0.003) significantly improved. Preliminary findings indicate that individual acupuncture therapy can be effective at improving health outcomes, well-being, and decreasing medication use in U.S. military veterans with chronic pain. Findings also suggest that individual acupuncture therapy can improve pain ratings, pain disability, and depression in veterans with chronic pain.

Keywords: acupuncture, chronic pain, depression, integrative health, medication use, military, pain, veterans, wellbeing

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Authors: Jasna Hudek-Knezevic, Igor Kardum, Nada Krapic, Martina Jurcic

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The aim of this study was to examine the effects of self- and partner reported attachment orientations on self-reported mate retention behaviors in romantic couples using the actor-partner interdependence model. The study was carried out on 187 heterosexual couples aged from 18 to 35 years, with an average relationship length of 4.5 years. Participants were asked to complete the revised scale of adult attachment and short form of mate retention inventory. Actor and partner effects of self- and partner reported anxious and avoidant attachment orientations on mate retention categories (direct guarding, intersexual negative inducements, positive inducements, public signals of possession and intrasexual negative inducements) and domains (cost-inflicting and benefit-provisioning), as well on overall mate retention were examined. Actor effects for women estimate whether their attachment orientations predict their own mate retention behaviors, whereas men’s actor effects estimate whether their attachment orientations predict their own mate retention behaviors. Women’s partner effects estimate whether their attachment orientations predict their partner’s mate retention behaviors, whereas men’s partner effects estimate whether their attachment orientations predict their partner’s mate retention behaviors. The use of two data sources, self- and partner reports, allow the control of the effects of common method variance when exploring actor and partner effects. Positive actor and partner effects of anxious attachment, as well as negative actor and partner effects of avoidant attachment on mate retention, were expected. In other words, it was expected that more anxiously attached individuals themselves, as well as their partners, will use mate retention behaviors more frequently. On the other hand, more avoidantly attached individuals themselves, as well as their partners, will use mate retention behaviors less frequently. These hypotheses were partially confirmed. The results showed that the strongest and most consistent effects across both data sources were men’s actor effects on the cost-inflicting mate retention domain, and especially on two mate retention categories, direct guarding, and intersexual negative inducements. Additionally, a consistent positive partner effect of men’s anxious attachment orientations on direct guarding was also obtained. Avoidant attachment orientation exerted few and inconsistent actor and partner effects on mate retention domains and categories. The results are explained by theoretical propositions addressing the effects of attachment orientations on an interpersonal romantic relationship in early adulthood.

Keywords: actor and partner effects, attachment orientations, dyadic analysis, mate retention behavior

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Authors: Dang Thuy Binh, Truong Thi Oanh, Le Phan Khanh Hung, Luong thi Tuong Vy

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One of the greatest environmental challenges facing Asia is the management and conservation of the marine biodiversity threaten by fisheries overexploitation, pollution, habitat destruction, and climate change. To date, a few molecular taxonomical studies has been conducted on marine fauna in Vietnam. The purpose of this study was to clarify the phylogeny of economic and ecological reef fish species in Vietnam Reef fish species covering Labridae, Scaridae, Nemipteridae, Serranidae, Acanthuridae, Lutjanidae, Lethrinidae, Mullidae, Balistidae, Pseudochromidae, Pinguipedidae, Fistulariidae, Holocentridae, Synodontidae, and Pomacentridae representing 28 genera were collected from South and Center, Vietnam. Combine with Genbank sequences, a phylogenetic tree was constructed based on 16S gene of mitochondrial DNA using maximum parsimony, maximum likelihood, and Bayesian inference approaches. The phylogram showed the well-resolved clades at genus and family level. Perciformes is the major order of reef fish species in Vietnam. The monophyly of Perciformes is not strongly supported as it was clustered in the same clade with Tetraodontiformes syngnathiformes and Beryciformes. Continue sampling of commercial fish species and classification based on morphology and genetics to build DNA barcoding of fish species in Vietnam is really necessary.

Keywords: reef fish, 16s rDNA, Vietnam, phylogeny

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Authors: Kaushik Sathupadi, Sandesh Achar

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Human action recognition modeling is a critical task in machine learning. These systems require better techniques for recognizing body parts and selecting optimal features based on vision sensors to identify complex action patterns efficiently. Still, there is a considerable gap and challenges between images and videos, such as brightness, motion variation, and random clutters. This paper proposes a robust approach for classifying human actions over cloud-based image data. First, we apply pre-processing and detection, human and outer shape detection techniques. Next, we extract valuable information in terms of cues. We extract two distinct features: fuzzy local binary patterns and sequence representation. Then, we applied a greedy, randomized adaptive search procedure for data optimization and dimension reduction, and for classification, we used a random forest. We tested our model on two benchmark datasets, AAMAZ and the KTH Multi-view football datasets. Our HMR framework significantly outperforms the other state-of-the-art approaches and achieves a better recognition rate of 91% and 89.6% over the AAMAZ and KTH multi-view football datasets, respectively.

Keywords: computer vision, human motion analysis, random forest, machine learning

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Authors: Musatafa Abbas Abbood Albadr, Masri Ayob, Sabrina Tiun, Fahad Taha Al-Dhief, Mohammad Kamrul Hasan

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Breast Cancer (BC) is considered one of the most frequent reasons of cancer death in women between 40 to 55 ages. The BC is diagnosed by using digital images of the FNA (Fine Needle Aspirate) for both benign and malignant tumors of the breast mass. Therefore, this work proposes the Online Sequential Extreme Learning Machine (OSELM) algorithm for diagnosing BC by using the tumor features of the breast mass. The current work has used the Wisconsin Diagnosis Breast Cancer (WDBC) dataset, which contains 569 samples (i.e., 357 samples for benign class and 212 samples for malignant class). Further, numerous measurements of assessment were used in order to evaluate the proposed OSELM algorithm, such as specificity, precision, F-measure, accuracy, G-mean, MCC, and recall. According to the outcomes of the experiment, the highest performance of the proposed OSELM was accomplished with 97.66% accuracy, 98.39% recall, 95.31% precision, 97.25% specificity, 96.83% F-measure, 95.00% MCC, and 96.84% G-Mean. The proposed OSELM algorithm demonstrates promising results in diagnosing BC. Besides, the performance of the proposed OSELM algorithm was superior to all its comparatives with respect to the rate of classification.

Keywords: breast cancer, machine learning, online sequential extreme learning machine, artificial intelligence

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Authors: Shriya Shukla, Lachin Fernando

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Pneumonia, a leading cause of mortality in young children globally, presents significant diagnostic challenges, particularly in resource-limited settings. This study presents an approach to diagnosing pediatric pneumonia using Chest X-Ray (CXR) images, employing a lightweight MobileNetV2 model enhanced with synthetic data augmentation. Addressing the challenge of dataset scarcity and imbalance, the study used a Variational Autoencoder-Generative Adversarial Network (VAE-GAN) to generate synthetic CXR images, improving the representation of normal cases in the pediatric dataset. This approach not only addresses the issues of data imbalance and scarcity prevalent in medical imaging but also provides a more accessible and reliable diagnostic tool for early pneumonia detection. The augmented data improved the model’s accuracy and generalization, achieving an overall accuracy of 95% in pneumonia detection. These findings highlight the efficacy of the MobileNetV2 model, offering a computationally efficient yet robust solution well-suited for resource-constrained environments such as mobile health applications. This study demonstrates the potential of synthetic data augmentation in enhancing medical image analysis for critical conditions like pediatric pneumonia.

Keywords: pneumonia, MobileNetV2, image classification, GAN, VAE, deep learning

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Authors: Panwasn Mahalawalert

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The purposes of this research were analyzed differential item functioning and differential test functioning of SWUSAT aptitude test classification by sex variable. The data used in this research is the secondary data from Srinakharinwirot University Scholastic Aptitude Test 2013 (SWUSAT). SWUSAT test consists of four subjects. There are verbal ability test, number ability test, reasoning ability test and spatial ability test. The data analysis was analyzed in 2 steps. The first step was analyzing descriptive statistics. In the second step were analyzed differential item functioning (DIF) and differential test functioning (DTF) by using the DIFAS program. The research results were as follows: The results of DIF and DTF analysis for all 10 tests in year 2013. Gender was the characteristic that found DIF all 10 tests. The percentage of item number that found DIF is between 6.67% - 60%. There are 5 tests that most of items favors female group and 2 tests that most of items favors male group. There are 3 tests that the number of items favors female group equal favors male group. For Differential test functioning (DTF), there are 8 tests that have small level.

Keywords: aptitude test, differential item functioning, differential test functioning, educational measurement

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Authors: Swati Rajput

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Environmental psychology explains that the person is a social agent that seeks to extract meaning from their built and natural environment to behave in a particular manner. It also shows the attachment or detachment of people to their environment. Assessing environmental psychology of people is imperative for planners and policy makers for urban planning. The paper investigates the environmental psychology of people living in nine districts of Delhi by calculating and assessing their Environmental Emotional Quotient (EEQ). Emotional Quotient deals with the ability to sense, understand, attach and respond according to the power of emotions. An Environmental Emotional Quotient has been formulated based upon the inventory administered to them. The respondents were asked questions related to their view and emotions about the green spaces, water resource conservation, air and environmental quality. An effort has been made to assess the feeling of belongingness among the residents. Their views were assessed on green spaces, reuse, and recycling of resources and their participation level. They were also been assessed upon health awareness level by considering both preventive and curative segments of health care. It was found that only 12 percent of the people is emotionally attached to their surroundings in the city. The emotional attachment reduces as we move away from the house to housing complex to neighbouring areas and rest of the city. In fact, the emotional quotient goes lower to lowest from house to other ends of the city. It falls abruptly after the radius of 1 km from the residence. The result also shows that nearly 54% respondents accept that there is environment pollution in their area. Around 47.8% respondents in the survey consider that diseases occur because of green cover depiction in their area. Major diseases are to airborne diseases like asthma and bronchitis. Seasonal disease prevalent, which specially occurred from last 3-4 years are malaria, dengue and chikengunya. Survey also shows that only 31 % of respondents visit government hospitals while 69% respondents visit private hospitals or small clinics for healthcare services. The paper suggests the need for environmental sensitive policies and need for green insurance in mega cities like Delhi.

Keywords: environmental psychology, environmental emotional quotient, preventive health care and curative health care, sustainable living

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Authors: Maria E. Laconi, Kyla D. Scherr, Mary A. Jamieson

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Human populations in urban environments have rapidly grown in recent decades. Consequently, the intensity of land-use and development has also increased in many urban and peri-urban environments. Some cities, such as Detroit, Michigan, USA, have embraced urban agriculture and local food production. Little is known, however, about how the local and landscape scale environmental factors influence crop productivity on urban farms. Our study aims to evaluate factors influencing the productivity of strawberries on community farms and gardens in the Detroit metropolitan area. Strawberries are one of few fruits that can provide an abundant harvest just after the first season of being planted, which is ideal for urban gardeners in developed areas. In the spring of 2016, we planted six different strawberry cultivars (three everbearing and three June bearing varieties) at five farm sites in Wayne and Oakland County (six replicate plants per cultivar per site). We surveyed flower and fruit phenology and production for everbearing varieties weekly (flowers for June bearing varieties were removed to enhance productivity in the coming growing season). Additionally, we conducted one initial 36hr pollinator survey in mid-September during peak fruit production and characterized local and landscape scale land-cover data. Preliminary results and observations from this first year of our study revealed that strawberry production varied significantly by site. Specifically, productivity at our most northern site appeared to suffer from delayed phenology and early frost damage to ripening strawberries. Bee abundance and diversity also differed among farms, though further surveys are needed to adequately inventory the pollinator community. Finally, strawberry cultivars demonstrated significant differences in the number and size of fruits produced. We plan to continue this study in the coming years, increasing the number of sites surveyed and number of pollinator sampling events. Our study aims to inform strategies for enhancing crop productivity on urban and peri-urban farms.

Keywords: insect pollination, strawberry productivity, sustainable agriculture, urban gardening

Procedia PDF Downloads 275

Authors: Ruangdech Sirikit

Abstract:

The purposes of this study were analyzed differential item functioning and differential test functioning of SWUSAT aptitude test classification by sex variable. The data used in this research is the secondary data from Srinakharinwirot University Scholastic Aptitude Test 2011 (SWUSAT 2011) SWUSAT test consists of four subjects. There are verbal ability test, number ability test, reasoning ability test and spatial ability test. The data analysis was carried out in 2 steps. The first step was analyzing descriptive statistics. In the second step were analyzed differential item functioning (DIF) and differential test functioning (DTF) by using the DIFAS program. The research results were as follows: The results of data analysis for all 10 tests in year 2011. Sex was the characteristic that found DIF all 10 tests. The percentage of item number that found DIF was between 10% - 46.67%. There are 4 tests that most of items favors female group. There are 3 tests that most of items favors male group and there are 3 tests that the number of items favors female group equal favors male group. For Differential test functioning (DTF), there are 8 tests that have small DIF effect variance.

Keywords: differential item functioning, differential test functioning, SWUSAT, aptitude test

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Authors: Bandar Alahmadi, Lethia Jackson

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Machine Learning model is used today in many real-life applications. The safety and security of such model is important, so the results of the model are as accurate as possible. One challenge of machine learning model security is the adversarial examples attack. Adversarial examples are designed by the attacker to cause the machine learning model to misclassify the input. We propose a method to generate adversarial examples to attack image classifiers. We are modifying the successfully classified images, so a classifier misclassifies them after the modification. In our method, we do not update the whole image, but instead we detect the important region, modify it, place it back to the original image, and then run it through a classifier. The algorithm modifies the detected region using two methods. First, it will add abstract image matrix on back of the detected image matrix. Then, it will perform a rotation attack to rotate the detected region around its axes, and embed the trace of image in image background. Finally, the attacked region is placed in its original position, from where it was removed, and a smoothing filter is applied to smooth the background with foreground. We test our method in cascade classifier, and the algorithm is efficient, the classifier confident has dropped to almost zero. We also try it in CNN (Convolutional neural network) with higher setting and the algorithm was successfully worked.

Keywords: adversarial examples, attack, computer vision, image processing

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Authors: Malarvizhi Selvaraj, Paul Fergus, Andy Shaw

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Uterine contractions produced during labour have the potential to damage the foetus by diminishing the maternal blood flow to the placenta. In order to observe this phenomenon labour and delivery are routinely monitored using cardiotocography monitors. An obstetrician usually makes the diagnosis of foetus hypoxia by interpreting cardiotocography recordings. However, cardiotocography capture and interpretation is time-consuming and subjective, often lead to misclassification that causes damage to the foetus and unnecessary caesarean section. Both of these have a high impact on the foetus and the cost to the national healthcare services. Automatic detection of foetal heart rate may be an objective solution to help to reduce unnecessary medical interventions, as reported in several studies. This paper aim is to provide a system for better identification and interpretation of abnormalities of the fetal heart rate using RStudio. An open dataset of 552 Intrapartum recordings has been filtered with 0.034 Hz filters in an attempt to remove noise while keeping as much of the discriminative data as possible. Features were chosen following an extensive literature review, which concluded with FIGO features such as acceleration, deceleration, mean, variance and standard derivation. The five features were extracted from 552 recordings. Using these features, recordings will be classified either normal or abnormal. If the recording is abnormal, it has got more chances of hypoxia.

Keywords: cardiotocography, foetus, intrapartum, hypoxia

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Authors: Claudia Valderrama-Ulloa, Cristian Schmitt, Juan Pablo Marchetti, Viviana Bucarey

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Access to adequate housing is a fundamental human right and a crucial factor for health. Housing should be inclusive, accessible, and able to meet the needs of all its inhabitants at every stage of their lives without hindering their health, autonomy, or independence. This article addresses the importance of designing housing for people with disabilities, which varies depending on individual abilities, preferences, and cultural considerations. Based on the components of the International Classification of Functioning, Disability and Health, wheelchair users, little people (achondroplasia), children with autism spectrum disorder and Down syndrome were characterized, and six domains of activities related to daily life inside homes were defined. The article describes the main barriers homes present for this group of people. It proposes a list of architectural and design aspects to reduce barriers to housing use. The aspects are divided into three main groups: space management, building services, and supporting facilities. The article emphasizes the importance of consulting professionals and users with experience designing for diverse needs to create inclusive, safe, and supportive housing for people with disabilities.

Keywords: achondroplasia, autism spectrum disorder, disability, down syndrome, wheelchair user

Procedia PDF Downloads 110