Search results for: artificial oil bodies

Authors: Joshua Osondu

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This research explores how Artificial Intelligence (AI) can be utilized as a policy response to address teaching and learning (TL) issues in education in Ghana. The dual (AI and human) instructor model is used as a theoretical framework to examine how AI can be employed to improve teaching and learning processes and to equip learners with the necessary skills in the emerging AI society. A qualitative research design was employed to assess the impact of AI on various TL issues, such as teacher workloads, a lack of qualified educators, low academic performance, unequal access to education and educational resources, a lack of participation in learning, and poor access and participation based on gender, place of origin, and disability. The study concludes that AI can be an effective policy response to TL issues in Ghana, as it has the potential to increase students’ participation in learning, increase access to quality education, reduce teacher workloads, and provide more personalized instruction. The findings of this study are significant for filling in the gaps in AI research in Ghana and other developing countries and for motivating the government and educational institutions to implement AI in TL, as this would ensure quality, access, and participation in education and help Ghana industrialize.

Keywords: artificial intelligence, teacher, learner, students, policy response

Procedia PDF Downloads 70

Authors: Boris M. Fedorov, Liudmila P. Goncharenko, Sergey A. Sybachin, Natalia A. Mamedova, Ekaterina V. Makarenkova, Saule Rakhimova

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In modern economic conditions, the question of the possibility of correct recognition of a signature on digital documents in order to verify the expression of will or confirm a certain operation is relevant. The additional complexity of processing lies in the dynamic variability of the signature for each individual, as well as in the way information is processed because the signature refers to biometric data. The article discusses the issues of using artificial intelligence models in order to improve the quality of signature confirmation in document recognition. The analysis of several possible options for using the model is carried out. The results of the study are given, in which it is possible to correctly determine the authenticity of the signature on small samples.

Keywords: signature recognition, biometric data, artificial intelligence, neural networks

Procedia PDF Downloads 123

Authors: Abbas Ahmadi, Bijan Raie, Mohammad Reza Neyshabouri, Mohammad Ali Ghorbani, Farrokh Asadzadeh

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In recent years, vast areas of Urmia Lake in Dasht-e-Tabriz has dried up leading to saline sediments exposure on the surface lake coastal areas being highly susceptible to wind erosion. This study was conducted to investigate wind erosion and its relevance to soil physicochemical properties and also modeling of wind erodibility (WE) using artificial intelligence techniques. For this purpose, 96 soil samples were collected from 0-5 cm depth in 414000 hectares using stratified random sampling method. To measure the WE, all samples (<8 mm) were exposed to 5 different wind velocities (9.5, 11, 12.5, 14.1 and 15 m s-1 at the height of 20 cm) in wind tunnel and its relationship with soil physicochemical properties was evaluated. According to the results, WE varied within the range of 76.69-9.98 (g m-2 min-1)/(m s-1) with a mean of 10.21 and coefficient of variation of 94.5% showing a relatively high variation in the studied area. WE was significantly (P<0.01) affected by soil physical properties, including mean weight diameter, erodible fraction (secondary particles smaller than 0.85 mm) and percentage of the secondary particle size classes 2-4.75, 1.7-2 and 0.1-0.25 mm. Results showed that the mean weight diameter, erodible fraction and percentage of size class 0.1-0.25 mm demonstrated stronger relationship with WE (coefficients of determination were 0.69, 0.67 and 0.68, respectively). This study also compared efficiency of multiple linear regression (MLR), gene expression programming (GEP), artificial neural network (MLP), artificial neural network based on genetic algorithm (MLP-GA) and artificial neural network based on whale optimization algorithm (MLP-WOA) in predicting of soil wind erodibility in Dasht-e-Tabriz. Among 32 measured soil variable, percentages of fine sand, size classes of 1.7-2.0 and 0.1-0.25 mm (secondary particles) and organic carbon were selected as the model inputs by step-wise regression. Findings showed MLP-WOA as the most powerful artificial intelligence techniques (R2=0.87, NSE=0.87, ME=0.11 and RMSE=2.9) to predict soil wind erodibility in the study area; followed by MLP-GA, MLP, GEP and MLR and the difference between these methods were significant according to the MGN test. Based on the above finding MLP-WOA may be used as a promising method to predict soil wind erodibility in the study area.

Keywords: wind erosion, erodible fraction, gene expression programming, artificial neural network

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Authors: Ge Zheng, Jun Peng

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Unbalanced power output from individual cells of Proton Exchange Membrane Fuel Cell (PEMFC) has direct effects on PEMFC stack performance, in particular under transient operation. In the paper, a multi-layer ANN (Artificial Neural Network) model Radial Basis Functions (RBF) has been developed for predicting cells' output profiles by applying gas supply parameters, cooling conditions, temperature measurement of individual cells, etc. The feed-forward ANN model was validated with experimental data. Influence of relevant parameters of RBF on the network accuracy was investigated. After adequate model training, the modelling results show good correspondence between actual measurements and reconstructed output profiles. Finally, after the model was used to optimize the stack output performance under steady-state and transient operating conditions, it suggested that the developed ANN control model can help PEMFC stack to have obvious improvement on power output under fast acceleration process.

Keywords: proton exchange membrane fuel cell, PEMFC, artificial neural network, ANN, cell output profile, transient

Procedia PDF Downloads 146

Authors: Sirilak Areerachakul

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Water quality has initiated serious management efforts in many countries. Artificial Neural Network (ANN) models are developed as forecasting tools in predicting water quality trend based on historical data. This study endeavors to automatically classify water quality. The water quality classes are evaluated using 6 factor indices. These factors are pH value (pH), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Nitrate Nitrogen (NO3N), Ammonia Nitrogen (NH3N) and Total Coliform (T-Coliform). The methodology involves applying data mining techniques using multilayer perceptron (MLP) neural network models. The data consisted of 11 sites of Saen Saep canal in Bangkok, Thailand. The data is obtained from the Department of Drainage and Sewerage Bangkok Metropolitan Administration during 2007-2011. The results of multilayer perceptron neural network exhibit a high accuracy multilayer perception rate at 94.23% in classifying the water quality of Saen Saep canal in Bangkok. Subsequently, this encouraging result could be combined with GIS data improves the classification accuracy significantly.

Keywords: artificial neural network, geographic information system, water quality, computer science

Procedia PDF Downloads 320

Authors: Angelis P. Barlampas

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Target: The (SARS-CoV-2) is still a threat. AI software could be useful, categorizing the disease into different severities and indicate the extent of the lesions. Materials and methods: AI is a new revolutionary technique, which uses powered computerized systems, to do what a human being does more rapidly, more easily, as accurate and diagnostically safe as the original medical report and, in certain circumstances, even better, saving time and helping the health system to overcome problems, such as work overload and human fatigue. Results: It will be given an effort to describe to the inexperienced reader (see figures), as simple as possible, how an artificial intelligence system diagnoses computed tomography pictures. First, the computerized machine learns the physiologic motives of lung parenchyma by being feeded with normal structured images of the lung tissue. Having being used to recognizing normal structures, it can then easily indentify the pathologic ones, as their images do not fit to known normal picture motives. It is the same way as when someone spends his free time in reading magazines with quizzes, such as <> and <>. General conclusion: The AI mimics the physiological processes of the human mind, but it does that more efficiently and rapidly and provides results in a few seconds, whereas an experienced radiologist needs many days to do that, or even worse, he is unable to accomplish such a huge task.

Keywords: covid-19, artificial intelligence, automated imaging, CT, chest imaging

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Authors: Agada N. Ihuoma, Nagata Yasunori

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Artificial intelligence (AI) techniques play a crucial role in predicting the expected energy outcome and its performance, analysis, modeling, and control of renewable energy. Renewable energy is becoming more popular for economic and environmental reasons. In the face of global energy consumption and increased depletion of most fossil fuels, the world is faced with the challenges of meeting the ever-increasing energy demands. Therefore, incorporating artificial intelligence to predict solar radiation outcomes from the intermittent sunlight is crucial to enable a balance between supply and demand of energy on loads, predict the performance and outcome of solar energy, enhance production planning and energy management, and ensure proper sizing of parameters when generating clean energy. However, one of the major problems of forecasting is the algorithms used to control, model, and predict performances of the energy systems, which are complicated and involves large computer power, differential equations, and time series. Also, having unreliable data (poor quality) for solar radiation over a geographical location as well as insufficient long series can be a bottleneck to actualization. To overcome these problems, this study employs the anaconda Navigator (Jupyter Notebook) for machine learning which can combine larger amounts of data with fast, iterative processing and intelligent algorithms allowing the software to learn automatically from patterns or features to predict the performance and outcome of Solar Energy which in turns enables the balance of supply and demand on loads as well as enhance production planning and energy management.

Keywords: artificial Intelligence, backward elimination, linear regression, solar energy

Procedia PDF Downloads 141

Authors: Abouzar Kaboudian, Ravi Chaithanya Mysa, Boo Cheong Khoo, Rajeev Kumar Jaiman

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Vortex induced vibrations can significantly affect the effectiveness of structures in aerospace as well as offshore marine industries. The oscillatory nature of the forces resulting from the vortex shedding around bluff bodies can result in undesirable effects such as increased loading, stresses, deflections, vibrations and noise in the structures, and also reduced fatigue life of the structures. To date, most studies concentrate on either the free oscillations or the prescribed motion of the bluff bodies. However, the structures in operation are usually subject to the external oscillatory forces (e.g. due to the platform motions in offshore industries). In this work, we present the effects of the external cross-flow forces on the vortex-induced vibrations of an oscillating cylinder. The effects of the amplitude, as well as the frequency of the external force on the fluid-forces on the oscillating cylinder are carefully studied and presented. Moreover, we present the transition of the response to be dominated by the vortex-induced-vibrations to the range where it is mostly dictated by the external oscillatory forces. Furthermore, we will discuss how the external forces can affect the flow structures around a cylinder. All results are compared against free oscillations of the cylinder.

Keywords: circular cylinder, external force, vortex-shedding, VIV

Procedia PDF Downloads 348

Authors: Jui-Ting Hsu, Heng-Li Huang, Ming-Tzu Tsai, Kuo-Chih Su, Lih-Jyh Fuh

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The objectives of this study were to measure the initial stability of the dental implant (ISQ and PTV) in the artificial foam bone block with three different quality levels. In addition, the 3D bone to implant contact percentage (BIC%) was measured based on the micro-computed tomography images. Furthermore, the relation between the initial stability of dental implant (ISQ and PTV) and BIC% were calculated. The experimental results indicated that enhanced the material property of the artificial foam bone increased the initial stability of the dental implant. The Pearson’s correlation coefficient between the BIC% and the two approaches (ISQ and PTV) were 0.652 and 0.745.

Keywords: dental implant, implant stability quotient, peak insertion torque, bone-implant contact, micro-computed tomography

Procedia PDF Downloads 557

Authors: A. Pesin, D. Pustovoytov, M. Sverdlik

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Traditionally, the need in items represents a large body of rotation (e.g. shrouds of various process units: a converter, a mixer, a scrubber, a steel ladle and etc.) is satisfied by using them at engineering enterprises. At these enterprises large parts of bodies of rotation are made on stamping units or bending and forming machines. In Nosov Magnitogorsk State Technical University in alliance with JSC "Magnitogorsk Metal and Steel Works" there was suggested and implemented the technology for producing such items based on a combination of asymmetric rolling processes and plastic bending under conditions of the plate mill. In this paper, based on finite elemental mathematical simulation in technology of a combined process of asymmetric rolling and bending plastic has been improved. It is shown that for the same curvature along the entire length of the metal sheet it is necessary to introduce additional asymmetry speed when rolling front end and tape trailer. Production of large bodies of rotation at mill 4500 JSC "Magnitogorsk Metal and Steel Works" showed good convergence of theoretical and experimental values of the curvature of the metal. Economic effect obtained more than 1.0 million dollars.

Keywords: asymmetric rolling, plastic bending, combined process, FEM

Procedia PDF Downloads 295

Authors: Kasun Gayashan Samarawickrama

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Bone diseases, defects, and fractions are commonly seen in modern life. Since bone is regenerating dynamic living tissue, it will undergo healing process naturally, it cannot recover from major bone injuries, diseases and defects. In order to overcome them, bone grafting technique was introduced. Gold standard was the best method for bone grafting for the past decades. Due to limitations of gold standard, alternative methods have been implemented. Apart from them artificial bone substitutes and bone tissue engineering have become the emerging methods with technology for bone grafting. Many bone diseases and defects will be healed permanently with these promising techniques in future.

Keywords: bone grafting, gold standard, bone substitutes, bone tissue engineering

Procedia PDF Downloads 279

Authors: Begum Koru, Isik Aybay, Demet Celik Ulusoy

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The discipline of law is quite complex and has its own terminology. Apart from written legal rules, there is also living law, which refers to legal practice. Basic legal rules aim at the happiness of individuals in social life and have different characteristics in different branches such as public or private law. On the other hand, law is a national phenomenon. The law of one nation and the legal system applied on the territory of another nation may be completely different. People who are experts in a particular field of law in one country may have insufficient expertise in the law of another country. Today, in addition to the local nature of law, international and even supranational law rules are applied in order to protect basic human values and ensure the protection of human rights around the world. Systems that offer algorithmic solutions to legal problems using artificial intelligence (AI) tools will perhaps serve to produce very meaningful results in terms of human rights. However, algorithms to be used should not be developed by only computer experts, but also need the contribution of people who are familiar with law, values, judicial decisions, and even the social and political culture of the society to which it will provide solutions. Otherwise, even if the algorithm works perfectly, it may not be compatible with the values of the society in which it is applied. The latest developments involving the use of AI techniques in legal systems indicate that artificial law will emerge as a new field in the discipline of law. More AI systems are already being applied in the field of law, with examples such as predicting judicial decisions, text summarization, decision support systems, and classification of documents. Algorithms for legal systems employing AI tools, especially in the field of prediction of judicial decisions and decision support systems, have the capacity to create automatic decisions instead of judges. When the judge is removed from this equation, artificial intelligence-made law created by an intelligent algorithm on its own emerges, whether the domain is national or international law. In this work, the aim is to make a general analysis of this new topic. Such an analysis needs both a literature survey and a perspective from computer experts' and lawyers' point of view. In some societies, the use of prediction or decision support systems may be useful to integrate international human rights safeguards. In this case, artificial law can serve to produce more comprehensive and human rights-protective results than written or living law. In non-democratic countries, it may even be thought that direct decisions and artificial intelligence-made law would be more protective instead of a decision "support" system. Since the values of law are directed towards "human happiness or well-being", it requires that the AI algorithms should always be capable of serving this purpose and based on the rule of law, the principle of justice and equality, and the protection of human rights.

Keywords: AI and law, artificial law, protection of human rights, AI tools for legal systems

Procedia PDF Downloads 51

Authors: Zouhour Neji Ben Salem

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Microscopic evacuation focuses on the evacuee behavior and way of search of safety place in an egress situation. In recent years, several models handled microscopic evacuation problem. Among them, we have proposed Artificial Neural Network (ANN) as an alternative to mathematical models that can deal with such problem. In this paper, we present two ANN models: SOM map and Hopfield Network used to predict the evacuee behavior in a disaster situation. These models are tested in a real case, the second floor of Tunisian children hospital evacuation in case of fire. The two models are studied and compared in order to evaluate their performance.

Keywords: artificial neural networks, self-organization map, hopfield network, microscopic evacuation, fire building evacuation

Procedia PDF Downloads 376

Authors: Giuseppina Settanni, Antonio Panarese, Raffaele Vaira, Maurizio Galiano

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Nowdays, artificial intelligence is used successfully in academia and industry for its ability to learn from a large amount of data. In particular, in recent years the use of machine learning algorithms in the field of e-commerce has spread worldwide. In this research study, a prototype software platform was designed and implemented in order to suggest to users the most suitable products for their needs. The platform includes a chatbot and a recommender system based on artificial intelligence algorithms that provide suggestions and decision support to the customer. The recommendation systems perform the important function of automatically filtering and personalizing information, thus allowing to manage with the IT overload to which the user is exposed on a daily basis. Recently, international research has experimented with the use of machine learning technologies with the aim to increase the potential of traditional recommendation systems. Specifically, support vector machine algorithms have been implemented combined with natural language processing techniques that allow the user to interact with the system, express their requests and receive suggestions. The interested user can access the web platform on the internet using a computer, tablet or mobile phone, register, provide the necessary information and view the products that the system deems them most appropriate. The platform also integrates a dashboard that allows the use of the various functions, which the platform is equipped with, in an intuitive and simple way. Artificial intelligence algorithms have been implemented and trained on historical data collected from user browsing. Finally, the testing phase allowed to validate the implemented model, which will be further tested by letting customers use it.

Keywords: machine learning, recommender system, software platform, support vector machine

Procedia PDF Downloads 112

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 105

Authors: Sayor Ajfar Aaron, Sajjat Hossain Abir, Ashif Newaz, Mushfiqur Rahman

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Investigating the convergence of blockchain technology and artificial intelligence, this paper examines their synergistic effects on data integrity and decision-making within smart contracts. By implementing AI-driven analytics on blockchain-based platforms, the research identifies improvements in automated contract enforcement and decision accuracy. The paper presents a framework that leverages AI to enhance transparency and trust while blockchain ensures immutable record-keeping, culminating in significantly optimized operational efficiencies in various industries.

Keywords: artificial intelligence, blockchain, data integrity, smart contracts

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Authors: Adegboyega Adeolapo Ola, Gerelene Jagganath

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One of the greatest challenges of human right in the world, especially African states is the use of child soldiers in armed conflict, constituting a major source of destruction of lives and properties. Mostly, they are in developing countries with the situation in Sub-Saharan Africa, the abduction and employment of children as soldiers is a form of exploitative labour that is tantamount to slavery. Since the end of cold war, Child soldier has increased in Africa countries like Angola, Liberia, Sierra Leone and Uganda. This study examines the main cause of the recruitment and use of child soldiers and its challenges to human right. It further assesses the role of international regional bodies and various governments in curbing child soldiers with a view to proffer suggestions on how to address some of the resultant threat of human right. The study posits that the control of small arms and light weapons is essential in curtailing the spread of child soldier and abuse of human right. This hopefully should result in the sustainability of human/child right in African continent. It is a recommendation of this study that, in order to sustain human right in the region, all Africa leaders, government and regional bodies; such as African Union, Economic Community of West African States, South African Development Community among others, should cooperate and work together to address the issue of illicit small arms, which could eventually lead to child soldier.

Keywords: arms control, child soldier, human right, small arms

Procedia PDF Downloads 191

Authors: Juan Sebastian Huertas Piedrahita, Jaime Arturo Lopez Duque, Eduardo Luis Perez Londoño, Julián S. Rodríguez

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The process of generating computer vision is called artificial vision. The artificial vision is a branch of artificial intelligence that allows the obtaining, processing, and analysis of any type of information especially the ones obtained through digital images. Actually the artificial vision is used in manufacturing areas for quality control and production, as these processes can be realized through counting algorithms, positioning, and recognition of objects that can be measured by a single camera (or more). On the other hand, the companies use assembly lines formed by conveyor systems with actuators on them for moving pieces from one location to another in their production. These devices must be previously programmed for their good performance and must have a programmed logic routine. Nowadays the production is the main target of every industry, quality, and the fast elaboration of the different stages and processes in the chain of production of any product or service being offered. The principal base of this project is to program a computer that recognizes geometric figures (circle, square, and triangle) through a camera, each one with a different color and link it with a group of conveyor systems to organize the mentioned figures in cubicles, which differ from one another also by having different colors. This project bases on artificial vision, therefore the methodology needed to develop this project must be strict, this one is detailed below: 1. Methodology: 1.1 The software used in this project is QT Creator which is linked with Open CV libraries. Together, these tools perform to realize the respective program to identify colors and forms directly from the camera to the computer. 1.2 Imagery acquisition: To start using the libraries of Open CV is necessary to acquire images, which can be captured by a computer’s web camera or a different specialized camera. 1.3 The recognition of RGB colors is realized by code, crossing the matrices of the captured images and comparing pixels, identifying the primary colors which are red, green, and blue. 1.4 To detect forms it is necessary to realize the segmentation of the images, so the first step is converting the image from RGB to grayscale, to work with the dark tones of the image, then the image is binarized which means having the figure of the image in a white tone with a black background. Finally, we find the contours of the figure in the image to detect the quantity of edges to identify which figure it is. 1.5 After the color and figure have been identified, the program links with the conveyor systems, which through the actuators will classify the figures in their respective cubicles. Conclusions: The Open CV library is a useful tool for projects in which an interface between a computer and the environment is required since the camera obtains external characteristics and realizes any process. With the program for this project any type of assembly line can be optimized because images from the environment can be obtained and the process would be more accurate.

Keywords: artificial intelligence, artificial vision, binarized, grayscale, images, RGB

Procedia PDF Downloads 359

Authors: Julio Jesus Salazar, Max Jesus De Lama

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The challenges of water management are aggravated by global change, which implies high complexity and associated uncertainty; water management is difficult because water networks cross domains (natural, societal, and political), scales (space, time, jurisdictional, institutional, knowledge, etc.) and levels (area: patches to global; knowledge: a specific case to generalized principles). In this context, we need to apply natural and non-natural measures to manage water and soil. The Basin Management Methodology considers multifunctional measures of natural water retention and erosion control and soil formation to protect water resources and address the challenges related to the recovery or conservation of the ecosystem, as well as natural characteristics of water bodies, to improve the quantitative status of water bodies and reduce vulnerability to floods and droughts. This method of water management focuses on the positive impacts of the chemical and ecological status of water bodies, restoration of the functioning of the ecosystem and its natural services; thus, contributing to both adaptation and mitigation of climate change. This methodology was applied in 7 interventions in the sub-basin of the Shullcas River in Huancayo-Junín-Peru, obtaining great benefits in the framework of the participation of alliances of actors and integrated planning scenarios. To implement the methodology in the sub-basin of the Shullcas River, a process called Climate Smart Territories (CST) was used; with which the variables were characterized in a highly complex space. The diagnosis was then worked using risk management and adaptation to climate change. Finally, it was concluded with the selection of alternatives and projects of this type. Therefore, the CST approach and process face the challenges of climate change through integrated, systematic, interdisciplinary and collective responses at different scales that fit the needs of ecosystems and their services that are vital to human well-being. This methodology is now replicated at the level of the Mantaro river basin, improving with other initiatives that lead to the model of a resilient basin.

Keywords: climate-smart territories, climate change, ecosystem services, natural measures, Climate Smart Territories (CST) approach

Procedia PDF Downloads 119

Authors: Jun-ya Nagase, Toshiyuki Satoh, Norihiko Saga, Koichi Suzumori

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In recent years, Japanese society has been aging, engendering a labour shortage of young workers. Robots are therefore expected to perform tasks such as rehabilitation, nursing elderly people, and day-to-day work support for elderly people. The pneumatic balloon actuator is a rubber artificial muscle developed for use in a robot hand in such environments. This actuator has a long stroke, and a high power-to-weight ratio compared with the present pneumatic artificial muscle. Moreover, the dynamic characteristics of this actuator resemble those of human muscle. This study evaluated characteristics of force control of balloon actuator using a predictive functional control (PFC) system with disturbance observer. The predictive functional control is a model-based predictive control (MPC) scheme that predicts the future outputs of the actual plants over the prediction horizon and computes the control effort over the control horizon at every sampling instance. For this study, a 1-link finger system using a pneumatic balloon actuator is developed. Then experiments of PFC control with disturbance observer are performed. These experiments demonstrate the feasibility of its control of a pneumatic balloon actuator for a robot hand.

Keywords: disturbance observer, pneumatic balloon, predictive functional control, rubber artificial muscle

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Authors: Genady Grabarnik, Serge Yaskolko

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Generative Artificial Intelligence (AI) has significantly expanded its applicability with the incorporation of Large Language Models (LLMs) and become a technology with promise to automate some areas that were very difficult to automate before. The paper describes the introduction of generative Artificial Intelligence into Introductory Computer and Data Science courses and analysis of effect of such introduction. The generative Artificial Intelligence is incorporated in the educational process two-fold: For the instructors, we create templates of prompts for generation of tasks, and grading of the students work, including feedback on the submitted assignments. For the students, we introduce them to basic prompt engineering, which in turn will be used for generation of test cases based on description of the problems, generating code snippets for the single block complexity programming, and partitioning into such blocks of an average size complexity programming. The above-mentioned classes are run using Large Language Models, and feedback from instructors and students and courses’ outcomes are collected. The analysis shows statistically significant positive effect and preference of both stakeholders.

Keywords: introductory computer and data science education, generative AI, large language models, application of LLMS to computer and data science education

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Authors: Kwame Amoah

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Artificial intelligence (AI) has the prospect of transforming project management, significantly improving efficiency and accuracy. By automating specific tasks with defined guidelines, AI can assist project managers in making better decisions and allocating resources efficiently, with possible risk mitigation. This study explores how AI is already impacting project management and likely future AI's impact on the field. The AI's reaction has been a divided opinion; while others picture it as a destroyer of jobs, some welcome it as an innovation advocate. Both sides agree that AI will be disruptive and revolutionize PM's functions. If current research is to go by, AI or some form will replace one-third of all learning graduate PM jobs by as early as 2030. A recent survey indicates AI spending will reach $97.9 billion by the end of 2023. Considering such a profound impact, the project management profession will also see a paradigm shift driven by AI. The study examines what the project management profession will look like in the next 5-10 years after this technological disruption. The research methods incorporate existing literature, develop trend analysis, and conduct structured interviews with project management stakeholders from North America to gauge the trend. PM professionals can harness the power of AI, ensuring a smooth transition and positive outcomes. AI adoption will maximize benefits, minimize adverse consequences, and uphold ethical standards, leading to improved project performance.

Keywords: project management, disruptive teacnologies, project management function, AL applications, artificial intelligence

Procedia PDF Downloads 54

Authors: Riona Dighe

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This research attempts to identify an alternative approach to the measurement of Gross National Happiness (GNH). It uses artificial intelligence (AI), incorporating natural language processing (NLP) and sentiment analysis to measure GNH. We use ‘off the shelf’ NLP models responsible for the sentiment analysis of a sentence as a building block for this research. We constructed an algorithm using NLP models to derive a sentiment analysis score against sentences. This was then tested against a sample of 20 respondents to derive a sentiment analysis score. The scores generated resembled human responses. By utilising the MLP classifier, decision tree, linear model, and K-nearest neighbors, we were able to obtain a test accuracy of 89.97%, 54.63%, 52.13%, and 47.9%, respectively. This gave us the confidence to use the NLP models against sentences in websites to measure the GNH of a country.

Keywords: artificial intelligence, NLP, sentiment analysis, gross national happiness

Procedia PDF Downloads 81

Authors: Nantakrit Yodpijit, Teppakorn Sittiwanchai

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The number of older adults is rising rapidly. The world’s population becomes aging. Falls is one of common and major health problems in the elderly. Falls may lead to acute and chronic injuries and deaths. The fall-prone individuals are at greater risk for decreased quality of life, lowered productivity and poverty, social problems, and additional health problems. A number of studies on falls prevention using fall detection system have been conducted. Many available technologies for fall detection system are laboratory-based and can incur substantial costs for falls prevention. The utilization of alternative technologies can potentially reduce costs. This paper presents the new design and development of a wearable-based fall detection system using an Accelerometer and Gyroscope as motion sensors for the detection of body orientation and movement. Algorithms are developed to differentiate between Activities of Daily Living (ADL) and falls by comparing Threshold-based values with Artificial Neural Networks (ANN). Results indicate the possibility of using the new threshold-based method with neural network algorithm to reduce the number of false positive (false alarm) and improve the accuracy of fall detection system.

Keywords: aging, algorithm, artificial neural networks (ANN), fall detection system, motion sensorsthreshold

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Authors: Andrus Pedai, Igor Astrov

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The focus of this paper is to compare common approaches for Systems of Innovation (SI) and identify proactive alternatives for driving the innovation. Proactive approaches will also consider short and medium term perspectives with developments in the field of Computer Technology and Artificial Intelligence. Concerning computer technology and large connected information systems, it is reasonable to predict that during current or the next century, intelligence and innovation will be separated from the constraints of human-driven management. After this happens, humans will no longer be driving the innovation and there is possibility that SI for new intelligent systems will set its own targets and exclude humans. Over long time scale, these developments could result in a scenario, which will lead to the development of larger, cross galactic (universal) proactive SI and Intelligence.

Keywords: artificial intelligence, DARPA, Moore’s law, proactive innovation, singularity, systems of innovation

Procedia PDF Downloads 459

Authors: G. Lancellotta, G. Di Salvo, A. Rigazio, A. Davout, V. Pastore, G. Tonoli, A. Martin, P. Jullien, R. Jagow-Klaff, R. Wernecke

Abstract:

Artificial ground freezing (AGF) technique is a well-proven soil improvement approach used worldwide to construct shafts, tunnels and many other civil structures in difficult subsoil or ambient conditions. As part of the extension of Line 14 of the Paris subway, a passenger interchange tunnel between the new station at Porte de CI ichy and the new Tribunal the Grand Instance has been successfully constructed using this technique. The paper presents the successful application of AGF by Liquid Nitrogen and Brine implemented to provide structural stability and groundwater cut-off around the passenger interchange tunnel. The working conditions were considered to be rather challenging, due to the proximity of a hundred-year-old existing service tunnel of the Line 13, and subsoil conditions on site. Laboratory tests were carried out to determine the relevant soil parameters for hydro-thermal-mechanical aspects and to implement numerical analyses. Monitoring data were used in order to check and control the development and the efficiency of the freezing process as well as to back analyze the parameters assumed for the design, both during the freezing and thawing phases.

Keywords: artificial ground freezing, brine method, case history, liquid nitrogen

Procedia PDF Downloads 199

Authors: Carlos Tarmeño Noriega, Igor Aguilar Alonso

Abstract:

Fungal diseases are common in vineyards; they cause a decrease in the quality of the products that can be sold, generating distrust of the customer towards the seller when buying fruit. Currently, technology allows the classification of fruits according to their characteristics thanks to artificial intelligence. This study proposes the implementation of a control system that allows the identification of the main fungal diseases present in the Italia grape, making use of a convolutional neural network (CNN), OpenCV, and TensorFlow. The methodology used was based on a collection of 20 articles referring to the proposed research on quality control, classification, and recognition of fruits through artificial vision techniques.

Keywords: computer vision, convolutional neural networks, quality control, fruit market, OpenCV, TensorFlow

Procedia PDF Downloads 53

Authors: K. Raghuwaiya, B. Sharma, J. Vanualailai

Abstract:

This paper presents a set of artificial potential field functions that improves upon; in general, the motion planning and posture control, with theoretically guaranteed point and posture stabilities, convergence and collision avoidance properties of the general 3-trailer system in a priori known environment. We basically design and inject two new concepts; ghost walls and the distance optimization technique (DOT) to strengthen point and posture stabilities, in the sense of Lyapunov, of our dynamical model. This new combination of techniques emerges as a convenient mechanism for obtaining feasible orientations at the target positions with an overall reduction in the complexity of the navigation laws. Simulations are provided to demonstrate the effectiveness of the controls laws.

Keywords: artificial potential fields, 3-trailer systems, motion planning, posture

Procedia PDF Downloads 405

Authors: G. V. Sai Soumya, Kalpesh Solanki, Sumit K. Choudhary

Abstract:

Necropsy is another term used for an autopsy, which is known as death examination in the case of animals. It is a complete standardized procedure involving dissection, observation, interpretation, and documentation. Government Bodies like National Tiger Conservation Authority (NTCA) have given standard operating procedures for commencing the necropsies. Necropsies are rarely performed as compared to autopsies performed on human bodies. There are no databases which maintain the count of autopsies in wildlife, but the research in this area has shown a very small number of necropsies. Long back, wildlife forensics came into existence but is coming into light nowadays as there is an increase in wildlife crime cases, including the smuggling of trophies, pooching, and many more. Physical examination in cases of animals is not sufficient to yield fruitful information, and thus postmortem examination plays an important role. Postmortem examination helps in the determination of time since death, cause of death, manner of death, factors affecting the case under investigation, and thus decreases the amount of time required in solving cases. Increasing the rate of necropsies will help forensic veterinary pathologists to build standardized provision and confidence within them, which will ultimately yield a higher success rate in solving wildlife crime cases.

Keywords: necropsy, wildlife crime, postmortem examination, forensic application

Procedia PDF Downloads 114

Authors: Carolina Zambrana, Grover Zurita

Abstract:

The food loss production that occurs in deficient agricultural production is one of the major problems worldwide. This puts the population's food security and the efficiency of farming investments at risk. It is to be expected that this food security will be achieved with the own and efficient production of each country. It will have an impact on the well-being of its population and, thus, also on food sovereignty. The production losses in quantity and quality occur due to the lack of efficient detection of diseases at an early stage. It is very difficult to solve the agriculture efficiency using traditional methods since it takes a long time to be carried out due to detection imprecision of the main diseases, especially when the production areas are extensive. Therefore, the main objective of this research study is to perform a systematic literature review, of the latest five years, of Precision Agriculture (PA) to be able to understand the state of the art of the set of new technologies, procedures, and optimization processes with Artificial Intelligence (AI). This study will focus on Corns, Potatoes, and Rice diagnostic diseases. The extensive literature review will be performed on Elsevier, Scopus, and IEEE databases. In addition, this research will focus on advanced digital imaging processing and the development of software and hardware for PA. The convolution neural network will be handling special attention due to its outstanding diagnostic results. Moreover, the studied data will be incorporated with artificial intelligence algorithms for the automatic diagnosis of crop quality. Finally, precision agriculture with technology applied to the agricultural sector allows the land to be exploited efficiently. This system requires sensors, drones, data acquisition cards, and global positioning systems. This research seeks to merge different areas of science, control engineering, electronics, digital image processing, and artificial intelligence for the development, in the near future, of a low-cost image measurement system that allows the optimization of crops with AI.

Keywords: precision agriculture, convolutional neural network, deep learning, artificial intelligence

Procedia PDF Downloads 60