Search results for: artificial intelligent systems

Authors: Hamed Zolfaghari, Mojtaba Kord

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After feasibility study the design phase is started and the rest of other phases are highly dependent on this phase. forecasting the duration of design phase could do a miracle and would save a lot of time. This study provides a fast and accurate Machine learning (ML) and optimization framework, which allows a quick duration estimation of project design phase, hence improving operational efficiency and competitiveness of a design construction company. 3 data sets of three years composed of daily time spent for different design projects are used to train and validate the ML models to perform multiple projects. Our study concluded that Artificial Neural Network (ANN) performed an accuracy of 0.94.

Keywords: time estimation, machine learning, Artificial neural network, project design phase

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Authors: S. M. Anowarul Haque, Md. Asiful Islam

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Electrical load forecasting is considered to be one of the most indispensable parts of a modern-day electrical power system. To ensure a reliable and efficient supply of electric energy, special emphasis should have been put on the predictive feature of electricity supply. Artificial Neural Network-based approaches have emerged to be a significant area of interest for electric load forecasting research. This paper proposed an Artificial Neural Network model based on the particle swarm optimization algorithm for improved electric load forecasting for Mymensingh, Bangladesh. The forecasting model is developed and simulated on the MATLAB environment with a large number of training datasets. The model is trained based on eight input parameters including historical load and weather data. The predicted load data are then compared with an available dataset for validation. The proposed neural network model is proved to be more reliable in terms of day-wise load forecasting for Mymensingh, Bangladesh.

Keywords: load forecasting, artificial neural network, particle swarm optimization

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Authors: Carolina Zambrana, Grover Zurita

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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

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Authors: Hina Iqbal, Mustansar Ali Ghazanfar, Sandor Szedmak

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Recommender systems uses artificial intelligence practices for filtering obscure information and can predict if a user likes a specified item. Kernel mapping Recommender systems have been proposed which are accurate and state-of-the-art algorithms and resolve recommender system’s design objectives such as; long tail, cold-start, and sparsity. The aim of research is to propose hybrid framework that can efficiently integrate different versions— namely item-based and user-based KMR— of KMR algorithm. We have proposed various heuristic algorithms that integrate different versions of KMR (into a unified framework) resulting in improved accuracy and elimination of problems associated with conventional recommender system. We have tested our system on publically available movies dataset and benchmark with KMR. The results (in terms of accuracy, precision, recall, F1 measure and ROC metrics) reveal that the proposed algorithm is quite accurate especially under cold-start and sparse scenarios.

Keywords: Kernel Mapping Recommender Systems, hybrid recommender systems, cold start, sparsity, long tail

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Authors: Supriyadi, Andi Intan Purnamasari, Aminuddin Kasim, Sulbadana, Mohammad Reza

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The development of the Industrial Revolution Era 4.0 brought a significant influence in the administration of countries in all parts of the world, including Indonesia, not only in the administration and economic sectors but the ways and methods of forming laws should also be adjusted. Until now, the process of making laws carried out by the Parliament with the Government still uses the classical method. The law-making process still uses manual methods, such as typing harmonization of regulations, so that it is not uncommon for errors to occur, such as writing errors, copying articles and so on, things that require a high level of accuracy and relying on inventory and harmonization carried out manually by humans. However, this method often creates several problems due to errors and inaccuracies on the part of officers who harmonize laws after discussion and approval; this has a very serious impact on the system of law formation in Indonesia. The use of artificial intelligence in the process of forming laws seems to be justified and becomes the answer in order to minimize the disharmony of various laws and regulations. This research is normative research using the Legislative Approach and the Conceptual Approach. This research focuses on the question of how to use Artificial Intelligence for Harmonization in the Lawmaking Process.

Keywords: artificial intelligence, harmonization, laws, intelligence

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Authors: Manal Bouasria, Mohammed-Hichem Benzaama, Valérie Pralong, Yassine El Mendili

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Reducing the quantity of cement in cementitious composites can help to reduce the environmental effect of construction materials. By-products such as ferronickel slags (FNS), fly ash (FA), and Crepidula fornicata (CR) are promising options for cement replacement. In this work, we investigated the relevance of substituting cement with FNS-CR and FA-CR on the mechanical properties of mortar and on the thermal properties of concrete. Foraging intervals ranging from 2 to 28 days, the mechanical properties are obtained by 3-point bending and compression tests. The chosen mix is used to construct a prototype in order to study the material’s hygrothermal performance. The data collected by the sensors placed on the prototype was utilized to build an artificial neural network.

Keywords: artificial neural network, cement, circular economy, concrete, by products

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Authors: Jasmin Cowin

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Augmented reality digital technologies, big data, and the need for a teacher workforce able to meet the demands of a knowledge-based society are poised to lead to major changes in the field of education. This paper explores applications and educational use cases of augmented reality digital technologies for educational organizations during the Fourth Industrial Revolution. The Fourth Industrial Revolution requires vision, flexibility, and innovative educational conduits by governments and educational institutions to remain competitive in a global economy. Educational organizations will need to focus on teaching in and for a digital age to continue offering academic knowledge relevant to 21st-century markets and changing labor force needs. Implementation of contemporary disciplines will need to be embodied through learners’ active knowledge-making experiences while embracing ubiquitous accessibility. The power of distributed ledger technology promises major streamlining for educational record-keeping, degree conferrals, and authenticity guarantees. Augmented reality digital technologies hold the potential to restructure educational philosophies and their underpinning pedagogies thereby transforming modes of delivery. Structural changes in education and governmental planning are already increasing through intelligent systems and big data. Reimagining and redesigning education on a broad scale is required to plan and implement governmental and institutional changes to harness innovative technologies while moving away from the big schooling machine.

Keywords: fourth industrial revolution, artificial intelligence, big data, education, augmented reality digital technologies, distributed ledger technology

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Authors: Julio Macedo

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Several methodologies are now available to conceive the improvements of a process so that it becomes competitive as for example total quality, process reengineering, six sigma, define measure analysis improvement control method. These improvements are of different nature and can be external to the process represented by an optimization model or a discrete simulation model. In addition, the process stakeholders are several and have different desired performances for the process. Hence, the methodologies above do not have a tool to aid in the conception of the required improvements. In order to fill this void we suggest the use of intelligent reference models. A reference model is a set of qualitative differential equations and an objective function that minimizes the gap between the current and the desired performance indexes of the process. The reference models are intelligent so when they receive the current state of the problematic process and the desired performance indexes they generate the required improvements for the problematic process. The reference models are fuzzy cognitive maps added with an objective function and trained using the improvements implemented by the high performance firms. Experiments done in a set of students show the reference models allow them to conceive more improvements than students that do not use these models.

Keywords: continuous improvement, fuzzy cognitive maps, process competitiveness, qualitative simulation, system dynamics

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Authors: Hossain A., Chowdhury S. I.

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Introduction: The use of a gamma camera is a standard procedure in nuclear medicine facilities or hospitals to diagnose chronic kidney disease (CKD), but the gamma camera does not precisely stage the disease. The authors sought to determine whether they could use an artificial neural network to determine whether CKD was in normal or abnormal stages based on GFR values (ANN). Method: The 250 kidney patients (Training 188, Testing 62) who underwent an ultrasonography test to diagnose a renal test in our nuclear medical center were scanned using a gamma camera. Before the scanning procedure, the patients received an injection of ⁹⁹ᵐTc-DTPA. The gamma camera computes the pre- and post-syringe radioactive counts after the injection has been pushed into the patient's vein. The artificial neural network uses the softmax function with cross-entropy loss to determine whether CKD is normal or abnormal based on the GFR value in the output layer. Results: The proposed ANN model had a 99.20 % accuracy according to K-fold cross-validation. The sensitivity and specificity were 99.10 and 99.20 %, respectively. AUC was 0.994. Conclusion: The proposed model can distinguish between normal and abnormal stages of CKD by using an artificial neural network. The gamma camera could be upgraded to diagnose normal or abnormal stages of CKD with an appropriate GFR value following the clinical application of the proposed model.

Keywords: artificial neural network, glomerular filtration rate, stages of the kidney, gamma camera

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Authors: Rupan Preet Kaur

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This article reviewed the potential of artificial intelligence in the field of identity and access management (IAM) and identity governance and administration (IGA), the most critical pillars of any organization. The power of leveraging AI in the most complex and huge user base environment was outlined by simplifying and streamlining the user access approvals and re-certifications without any impact on the user productivity and at the same time strengthening the overall compliance of IAM landscape. Certain challenges encountered in the current state were detailed where majority of organizations are still lacking maturity in the data integrity aspect. Finally, this paper concluded that within the realm of possibility, users and application owners can reap the benefits of unified approach provided by AI to improve the user experience, improve overall efficiency, and strengthen the risk posture.

Keywords: artificial intelligence, machine learning, user access review, access approval

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Authors: Raimo P. Hämäläinen, Juha Törmänen, Esa Saarinen

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Systems thinking has been acknowledged as an important approach in the strategy and management literature ever since the seminal works of Ackhoff in the 1970´s and Senge in the 1990´s. The early literature was very much focused on structures and organizational dynamics. Understanding systems is important but making improvements also needs ways to understand human behavior in systems. Peter Senge´s book The Fifth Discipline gave the inspiration to the development of the concept of Systems Intelligence. The concept integrates the concepts of personal mastery and systems thinking. SI refers to intelligent behavior in the context of complex systems involving interaction and feedback. It is a competence related to the skills needed in strategy and the environment of modern industrial engineering and management where people skills and systems are in an increasingly important role. The eight factors of Systems Intelligence have been identified from extensive surveys and the factors relate to perceiving, attitude, thinking and acting. The personal self-evaluation test developed consists of 32 items which can also be applied in a peer evaluation mode. The concept and test extend to organizations too. One can talk about organizational systems intelligence. This paper reports the results of an extensive survey based on peer evaluation. The results show that systems intelligence correlates positively with professional performance. People in a managerial role score higher in SI than others. Age improves the SI score but there is no gender difference. Top organizations score higher in all SI factors than lower ranked ones. The SI-tests can also be used as leadership and management development tools helping self-reflection and learning. Finding ways of enhancing learning organizational development is important. Today gamification is a new promising approach. The items in the SI test have been used to develop an interactive card game following the Topaasia game approach. It is an easy way of engaging people in a process which both helps participants see and approach problems in their organization. It also helps individuals in identifying challenges in their own behavior and in improving in their SI.

Keywords: gamification, management competence, organizational learning, systems thinking

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Authors: Rakesh Dubey, Vaishali Bahl, Dalveer Kaur

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The spiralling urge for high rate data transmission over wireless mediums needs intelligent use of electromagnetic resources considering restrictions like power ingestion, spectrum competence, robustness against multipath propagation and implementation intricacy. Orthogonal frequency division multiplexing (OFDM) is a capable technique for next generation wireless communication systems. For such high rate data transfers there is requirement of proper allocation of resources like power and capacity amongst the sub channels. This paper illustrates various available methods of allocating power and the capacity requirement with the constraint of Shannon limit.

Keywords: Additive White Gaussian Noise, Multi-Carrier Modulation, Orthogonal Frequency Division Multiplexing (OFDM), Signal to Noise Ratio (SNR), Water Filling

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Authors: Yusuf Turan

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Definition of Intellectual Property Rights according to the World Intellectual Property Organization: " Intellectual property (IP) refers to creations of the mind, such as inventions; literary and artistic works; designs; and symbols, names and images used in commerce." It states as follows. There are 2 important points in the definition; we can say that it is the result of intellectual activities that occur by one or more than one PERSON and as INNOVATION. When the history and development of the relevant definitions are briefly examined, it is realized that these two points have remained constant and Intellectual Property law and rights have been shaped around these two points. With the expansion of the scope of the term Intellectual Property as a result of the development of technology, especially in the field of artificial intelligence, questions such as "Can "Artificial Intelligence" be an inventor?" need to be resolved within the expanding scope. In the past years, it was ruled that the artificial intelligence named DABUS seen in the USA did not meet the definition of "individual" and therefore would be an inventor/inventor. With the developing technology, it is obvious that we will encounter such situations much more frequently in the field of intellectual property. While expanding the scope, we should definitely determine the boundaries of how we should decide who performs the mental activity or creativity that we call indispensable on the inventor/inventor according to these problems. As a result of all these problems and innovative situations, it is clearly realized that not only Intellectual Property Law and Rights but also their definitions need to be updated and improved. Ignoring the situations that are outside the scope of the current Intellectual Property Term is not enough to solve the problem and brings uncertainty. The fact that laws and definitions that have been operating on the same theories for years exclude today's innovative technologies from the scope contradicts intellectual property, which is expressed as a new and innovative field. Today, as a result of the innovative creation of poetry, painting, animation, music and even theater works with artificial intelligence, it must be recognized that the definition of Intellectual Property must be revised.

Keywords: artificial intelligence, innovation, the term of intellectual property, right

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Authors: Matthew Holland, Jason Everett, Martin Cox, Iain Suthers

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High spatial-resolution distribution of planktivorous reef fish can reveal behavioural adaptations to optimise the balance between feeding success and predator avoidance. We used a multi-beam echosounder to record bathymetry and the three-dimensional distribution of fish schools associated with natural and artificial reefs. We utilised generalised linear models to assess the distribution, orientation, and aggregation of fish schools relative to the structure, vertical relief, and currents. At artificial reefs, fish schooled more closely to the structure and demonstrated a preference for the windward side, particularly when exposed to strong currents. Similarly, at natural reefs fish demonstrated a preference for windward aspects of bathymetry, particularly when associated with high vertical relief. Our findings suggest that under conditions with stronger current velocity, fish can exercise their preference to remain close to structure for predator avoidance, while still receiving an adequate supply of zooplankton delivered by the current. Similarly, when current velocity is low, fish tend to disperse for better access to zooplankton. As artificial reefs are generally deployed with the goal of creating productivity rather than simply attracting fish from elsewhere, we advise that future artificial reefs be designed as semi-linear arrays perpendicular to the prevailing current, with multiple tall towers. This will facilitate the conversion of dispersed zooplankton into energy for higher trophic levels, enhancing reef productivity and fisheries.

Keywords: artificial reef, current, forage fish, multi-beam, planktivorous fish, reef fish, schooling

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Authors: Reda Abdel Azim, Tariq Shehab

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The main objective of this study is to develop a subsurface fracture map of naturally fractured reservoirs by overcoming the limitations associated with different data sources in characterising fracture properties. Some of these limitations are overcome by employing a nested neuro-stochastic technique to establish inter-relationship between different data, as conventional well logs, borehole images (FMI), core description, seismic attributes, and etc. and then characterise fracture properties in terms of fracture density and fractal dimension for each data source. Fracture density is an important property of a system of fracture network as it is a measure of the cumulative area of all the fractures in a unit volume of a fracture network system and Fractal dimension is also used to characterize self-similar objects such as fractures. At the wellbore locations, fracture density and fractal dimension can only be estimated for limited sections where FMI data are available. Therefore, artificial intelligence technique is applied to approximate the quantities at locations along the wellbore, where the hard data is not available. It should be noted that Artificial intelligence techniques have proven their effectiveness in this domain of applications.

Keywords: naturally fractured reservoirs, artificial intelligence, fracture intensity, fractal dimension

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Authors: Muhammad Faheem Khakhi, Jian Yu Wang, Salman Muhammad, Muhammad Faisal Shabir

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Population growth and Chinese two-child policy will boost pharmaceutical market, and it will continue to maintain the growth for a period of time in the future, the traditional pharmacy dispensary has been unable to meet the growing medical needs of the peoples. Under the strong support of the national policy, the automatic transformation of traditional pharmacies is the inclination of the Times, the new type of intelligent pharmacy system will continue to promote the development of the pharmaceutical industry. Under this background, based on PLC control, the paper proposed an intelligent storage and automatic drug delivery system; complete design of the lower computer's control system and the host computer's software system has been present. The system can be applied to dispensing work for Chinese herbal medicinal and Western medicines. Firstly, the essential of intelligent control system for pharmacy is discussed. After the analysis of the requirements, the overall scheme of the system design is presented. Secondly, introduces the software and hardware design of the lower computer's control system, including the selection of PLC and the selection of motion control system, the problem of the human-computer interaction module and the communication between PC and PLC solves, the program design and development of the PLC control system is completed. The design of the upper computer software management system is described in detail. By analyzing of E-R diagram, built the establish data, the communication protocol between systems is customize, C++ Builder is adopted to realize interface module, supply module, main control module, etc. The paper also gives the implementations of the multi-threaded system and communication method. Lastly, each module of the lower computer control system is tested. Then, after building a test environment, the function test of the upper computer software management system is completed. On this basis, the entire control system accepts the overall test.

Keywords: automatic pharmacy, PLC, control system, management system, communication

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Authors: Carla Aramouny

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Digital fabrication, between advancements in software and machinery, is pushing practice today towards more complexity in design, allowing for unparalleled explorations. It is giving designers the immediate capacity to apply their imagined objects into physical results. Yet at no time have questions of material knowledge become more relevant and crucial, as technological advancements approach a radical re-invention of the design process. As more and more designers look towards tactile crafts for material know-how, an interest in natural behaviors has also emerged trying to embed intelligence from nature into the designed objects. Concerned with enhancing their immediate environment, designers today are pushing the boundaries of design by bringing in natural systems, materiality, and advanced fabrication as essential processes to produce active designs. New Territories, a yearly architecture and design course on digital design and materiality, allows students to explore processes of digital fabrication in intersection with natural systems and hands-on experiments. This paper will highlight the importance of learning from nature and from physical materiality in a digital design process, and how the simultaneous move between the digital and physical realms has become an essential design method. It will detail the work done over the course of three years, on themes of natural systems, crafts, concrete plasticity, and active composite materials. The aim throughout the course is to explore the design of products and active systems, be it modular facades, intelligent cladding, or adaptable seating, by embedding current digital technologies with an understanding of natural systems and a physical know-how of material behavior. From this aim, three main themes of inquiry have emerged through the varied explorations across the three years, each one approaching materiality and digital technologies through a different lens. The first theme involves crossing the study of naturals systems as precedents for intelligent formal assemblies with traditional crafts methods. The students worked on designing performative facade systems, starting from the study of relevant natural systems and a specific craft, and then using parametric modeling to develop their modular facades. The second theme looks at the cross of craft and digital technologies through form-finding techniques and elastic material properties, bringing in flexible formwork into the digital fabrication process. Students explored concrete plasticity and behaviors with natural references, as they worked on the design of an exterior seating installation using lightweight concrete composites and complex casting methods. The third theme brings in bio-composite material properties with additive fabrication and environmental concerns to create performative cladding systems. Students experimented in concrete composites materials, biomaterials and clay 3D printing to produce different cladding and tiling prototypes that actively enhance their immediate environment. This paper thus will detail the work process done by the students under these three themes of inquiry, describing their material experimentation, digital and analog design methodologies, and their final results. It aims to shed light on the persisting importance of material knowledge as it intersects with advanced digital fabrication and the significance of learning from natural systems and biological properties to embed an active performance in today’s design process.

Keywords: digital fabrication, design and craft, materiality, natural systems

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Authors: Raphaël Weuts, Mykyta Petik, Anton Vedder

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Artificial Intelligence has the potential to create and already creates enormous value in healthcare. Prescriptive systems might be able to make the use of healthcare capacity more efficient. Such systems might entail interpretations that exclude the effect of confounders that brings risks with it. Those risks might be mitigated by regulation that prevents systems entailing such risks to come to market. One modality of regulation is that of legislation, and the European AI Act is an example of such a regulatory instrument that might mitigate these risks. To assess the risk mitigation potential of the AI Act for those risks, this research focusses on a case study of a hypothetical application of medical device software that entails the aforementioned risks. The AI Act refers to the harmonised norms for already existing legislation, here being the European medical device regulation. The issue at hand is a causal link between a confounder and the value the algorithm optimises for by proxy. The research identifies where the AI Act already looks at confounders (i.a. feedback loops in systems that continue to learn after being placed on the market). The research identifies where the current proposal by parliament leaves legal uncertainty on the necessity to check for confounders that do not influence the input of the system, when the system does not continue to learn after being placed on the market. The authors propose an amendment to article 15 of the AI Act that would require high-risk systems to be developed in such a way as to mitigate risks from those aforementioned confounders.

Keywords: AI Act, healthcare, confounders, risks

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Authors: Royan Dawud Aldian, Endah Purwanti, Soegianto Soelistiono

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In this research we have been developed an automatic investigation to classify normal children voice or autistic by using modern computation technology that is computation based on artificial neural network. The superiority of this computation technology is its capability on processing and saving data. In this research, digital voice features are gotten from the coefficient of linear-predictive coding with auto-correlation method and have been transformed in frequency domain using fast fourier transform, which used as input of artificial neural network in back-propagation method so that will make the difference between normal children and autistic automatically. The result of back-propagation method shows that successful classification capability for normal children voice experiment data is 100% whereas, for autistic children voice experiment data is 100%. The success rate using back-propagation classification system for the entire test data is 100%.

Keywords: autism, artificial neural network, backpropagation, linier predictive coding, fast fourier transform

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Authors: Hideo Suzuki, Yuya Kiyonobu, Kotaro Matsushita, Masaki Hanada, Rie Suzuki, Noriko Niijima, Noriko Uosaki, Tadao Nakamura

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People often worry about their elderly family members who are living by themselves or staying alone somewhere. An intelligent watch-over system for such elderly people, using a Raspberry Pi IoT device, has been newly developed to monitor those who live or stay separately from their families and alert them if a problem occurs. The system consists of motion sensors and temperature-humidity combined sensors that are located at seven points within an elderly person's home. The intelligent algorithms of the system detect signs and the possibility of unhealthy situations arising for the elderly relative; e.g., an unusually long bathing time, or a visit to a restroom, too high a room temperature, etc., by using data cached by the sensors above, at seven points within their house. The system gives more consideration to the elderly person's privacy, by using the sensors above, instead of using cameras and microphones placed around the house. The system invented and described here, can send a Twitter direct message to designated family members when an elderly relative is possibly in an unhealthy condition. Thus the system helps decrease family members' anxieties regarding their elderly relatives and increases their sense of security.

Keywords: elderly person, IoT device, Raspberry Pi, watch-over system

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Authors: R. A. Qawasma, F. M. Haddad, H. O. Salhab

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Hemodialysis is a life-preserving treatment for a number of patients with kidney failure. The standard procedure of hemodialysis is three times a week during the hemodialysis procedure, the patient usually suffering from many inconvenient, exhausting feeling and effect on the heart and cardiovascular system are the most common signs. This paper provides a solution to reduce the previous problems by designing a wearable artificial kidney (WAK) taking in consideration a minimization the size of the dialysis machine. The WAK system consists of two circuits: blood circuit and dialysate circuit. The blood from the patient is filtered in the dialyzer before returning back to the patient. Several parameters using an advanced microcontroller and array of sensors. WAK equipped with visible and audible alarm system to aware the patients if there is any problem.

Keywords: artificial kidney, home dialysis, renal failure, wearable kidney

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Authors: Cheng Zeng, Xia Luo

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Passenger organization has always been an essential part of China's metro operation and management. Facing the massive passenger flow, stations need to improve their intelligence and automation degree by an appropriate integrated system. Based on the existing integrated supervisory control system (ISCS) and simulation software (Anylogic), this paper designs an intelligent passenger organization system (IPOS) for metro stations. Its primary function includes passenger information acquisition, data processing and computing, visualization management, decision recommendations, and decision response based on interlocking equipment. For this purpose, the logical structure and intelligent algorithms employed are particularly devised. Besides, the structure diagram of information acquisition and application module, the application of Anylogic, the case library's function process are all given by this research. Based on the secondary development of Anylogic and existing technologies like video recognition, the IPOS is supposed to improve the response speed and address capacity in the face of emergent passenger flow of metro stations.

Keywords: anylogic software, decision-making support system, intellectualization, ISCS, passenger organization

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Authors: Yifan Fan, Xudong Luo, Pingping Lin

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An essential task in the field of artificial intelligence is to allow computers to interact with people through natural language. Therefore, researches such as virtual assistants and dialogue systems have received widespread attention from industry and academia. The response generation plays a crucial role in dialogue systems, so to push forward the research on this topic, this paper surveys various methods for response generation. We sort out these methods into three categories. First one includes finite state machine methods, framework methods, and instance methods. The second contains full-text indexing methods, ontology methods, vast knowledge base method, and some other methods. The third covers retrieval methods and generative methods. We also discuss some hybrid methods based knowledge and deep learning. We compare their disadvantages and advantages and point out in which ways these studies can be improved further. Our discussion covers some studies published in leading conferences such as IJCAI and AAAI in recent years.

Keywords: deep learning, generative, knowledge, response generation, retrieval

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Authors: Kudzanayi Chiteka, Wellington Makondo

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The whole world is on the drive to go green owing to the negative effects of burning fossil fuels. Therefore, there is immediate need to identify and utilise alternative renewable energy sources. Among these energy sources solar energy is one of the most dominant in Zimbabwe. Solar power plants used to generate electricity are entirely dependent on solar radiation. For planning purposes, solar radiation values should be known in advance to make necessary arrangements to minimise the negative effects of the absence of solar radiation due to cloud cover and other naturally occurring phenomena. This research focused on the prediction of Global Horizontal Irradiation values for the sixth day given values for the past five days. Artificial intelligence techniques were used in this research. Three models were developed based on Support Vector Machines, Radial Basis Function, and Feed Forward Back-Propagation Artificial neural network. Results revealed that Support Vector Machines gives the best results compared to the other two with a mean absolute percentage error (MAPE) of 2%, Mean Absolute Error (MAE) of 0.05kWh/m²/day root mean square (RMS) error of 0.15kWh/m²/day and a coefficient of determination of 0.990. The other predictive models had prediction accuracies of MAPEs of 4.5% and 6% respectively for Radial Basis Function and Feed Forward Back-propagation Artificial neural network. These two models also had coefficients of determination of 0.975 and 0.970 respectively. It was found that prediction of GHI values for the future days is possible using artificial intelligence-based predictive models.

Keywords: solar energy, global horizontal irradiation, artificial intelligence, predictive models

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Authors: Edward Guillén, Jhordany Rodriguez, Rafael Páez

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Anomaly detection techniques have been focused on two main components: data extraction and selection and the second one is the analysis performed over the obtained data. The goal of this paper is to analyze the influence that each of these components has over the system performance by evaluating detection over network scenarios with different setups. The independent variables are as follows: the number of system inputs, the way the inputs are codified and the complexity of the analysis techniques. For the analysis, some approaches of artificial neural networks are implemented with different number of layers. The obtained results show the influence that each of these variables has in the system performance.

Keywords: network intrusion detection, machine learning, artificial neural network, anomaly detection module

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Authors: D. S. Zhuravkova, A. G. Temnikov, O. S. Belova, L. L. Chernensky, T. K. Gerastenok, I. Y. Kalugina, N. Y. Lysov, A.V. Orlov

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Investigation of the discharges from the artificial charged water aerosol clouds in presence of the arrays of the model hydrometeors could help to receive the new data about the peculiarities of the return stroke formation between the thundercloud and the ground when the large volumes of the hail particles participate in the lightning discharge initiation and propagation stimulation. Artificial charged water aerosol clouds of the negative or positive polarity with the potential up to one million volts have been used. Hail has been simulated by the group of the conductive model hydrometeors of the different form. Parameters of the impulse current of the main stage of the discharge between the artificial positively and negatively charged water aerosol clouds and the ground in presence of the model hydrometeors array and of its corresponding electromagnetic radiation have been determined. It was established that the parameters of the array of the model hydrometeors influence on the parameters of the main stage of the discharge between the artificial thundercloud cell and the ground. The maximal values of the main stage current impulse parameters and the electromagnetic radiation registered by the plate antennas have been found for the array of the model hydrometeors of the cylinder revolution form for the negatively charged aerosol cloud and for the array of the hydrometeors of the plate rhombus form for the positively charged aerosol cloud, correspondingly. It was found that parameters of the main stage of the discharge between the artificial charged water aerosol cloud and the ground in presence of the model hydrometeor array of the different considered forms depend on the polarity of the artificial charged aerosol cloud. In average, for all forms of the investigated model hydrometeors arrays, the values of the amplitude and the current rise of the main stage impulse current and the amplitude of the corresponding electromagnetic radiation for the artificial charged aerosol cloud of the positive polarity were in 1.1-1.9 times higher than for the charged aerosol cloud of the negative polarity. Thus, the received results could indicate to the possible more important role of the big volumes of the large hail arrays in the thundercloud on the parameters of the return stroke for the positive lightning.

Keywords: main stage of discharge, hydrometeor form, lightning parameters, negative and positive artificial charged aerosol cloud

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Authors: Meltem Eryılmaz

Abstract:

With the emergence of the COVID-19 infection outbreak, the socio-cultural, political, economic, educational systems dynamics of the world have gone through a major change, especially in the educational field, specifically dentistry preclinical education, where the students must have a certain amount of real-time experience in endodontics and other various procedures. The totality of the digital and physical elements that make our five sense organs feel as if we really exist in a virtual world is called virtual reality. Virtual reality, which is very popular today, has started to be used in education. With the inclusion of developing technology in education and training environments, virtual learning platforms have been designed to enrich students' learning experiences. The field of health is also affected by these current developments, and the number of virtual reality applications developed for students studying dentistry is increasing day by day. The most widely used tools of this technology are virtual reality glasses. With virtual reality glasses, you can look any way you want in a world designed in 3D and navigate as you wish. With this project, solutions that will respond to different types of dental practices of students who study dentistry with virtual reality applications are produced. With this application, students who cannot find the opportunity to work with patients in distance education or who want to improve themselves at home have unlimited trial opportunities. Unity 2021, Visual Studio 2019, Cardboard SDK are used in the study.

Keywords: dentistry, intelligent tutoring system, virtual reality, online learning, COVID-19

Procedia PDF Downloads 181

Authors: Mihai Caramihai, Dan Vasilescu

Abstract:

The main research objective of the experimental bioprocess analyzed in this paper was to obtain large biomass quantities. The bioprocess is performed in 100 L Bioengineering bioreactor with 42 L cultivation medium made of peptone, meat extract and sodium chloride. The reactor was equipped with pH, temperature, dissolved oxygen, and agitation controllers. The operating parameters were 37 oC, 1.2 atm, 250 rpm and air flow rate of 15 L/min. The main objective of this paper is to present a case study to demonstrate that intelligent control, describing the complexity of the biological process in a qualitative and subjective manner as perceived by human operator, is an efficient control strategy for this kind of bioprocesses. In order to simulate the bioprocess evolution, an intelligent control structure, based on fuzzy logic has been designed. The specific objective is to present a fuzzy control approach, based on human expert’ rules vs. a modeling approach of the cells growth based on bioprocess experimental data. The kinetic modeling may represent only a small number of bioprocesses for overall biosystem behavior while fuzzy control system (FCS) can manipulate incomplete and uncertain information about the process assuring high control performance and provides an alternative solution to non-linear control as it is closer to the real world. Due to the high degree of non-linearity and time variance of bioprocesses, the need of control mechanism arises. BIOSIM, an original developed software package, implements such a control structure. The simulation study has showed that the fuzzy technique is quite appropriate for this non-linear, time-varying system vs. the classical control method based on a priori model.

Keywords: intelligent, control, fuzzy model, bioprocess optimization

Procedia PDF Downloads 293

Authors: Ghofrane Rehaiem, Hamza Gharsellaoui, Samir Benahmed

Abstract:

In this study, Artificial Neural Networks (ANNs) were used for modeling the parameters that allow the real-time scheduling of embedded systems under resources constraints designed for real-time applications running. The objective of this work is to implement a neural networks based approach for real-time scheduling of embedded systems in order to handle real-time constraints in execution scenarios. In our proposed approach, many techniques have been proposed for both the planning of tasks and reducing energy consumption. In fact, a combination of Dynamic Voltage Scaling (DVS) and time feedback can be used to scale the frequency dynamically adjusting the operating voltage. Indeed, we present in this paper a hybrid contribution that handles the real-time scheduling of embedded systems, low power consumption depending on the combination of DVS and Neural Feedback Scheduling (NFS) with the energy Priority Earlier Deadline First (PEDF) algorithm. Experimental results illustrate the efficiency of our original proposed approach.

Keywords: optimization, neural networks, real-time scheduling, low-power consumption

Procedia PDF Downloads 343

Authors: A. G. Temnikov, O. S. Belova, L. L. Chernensky, T. K. Gerastenok, N. Y. Lysov, A. V. Orlov, D. S. Zhuravkova

Abstract:

Hypothesis of the lightning initiation on the arrays of large hydrometeors are in the consideration. There is no agreement about the form the hydrometeors that could be the best for the lightning initiation from the thundercloud. Artificial charged water aerosol clouds of the positive or negative polarity could help investigate the possible influence of the hydrometeor form on the peculiarities and the probability of the lightning discharge initiation between the thundercloud and the ground. Artificial charged aerosol clouds that could create the electric field strength in the range of 5-6 kV/cm to 16-18 kV/cm have been used in experiments. The array of the model hydrometeors of the volume and plate form has been disposed near the bottom cloud boundary. It was established that the different kinds of the discharge could be initiated in the presence of the model hydrometeors array – from the cloud discharges up to the diffuse and channel discharges between the charged cloud and the ground. It was found that the form of the model hydrometeors could significantly influence the channel discharge initiation from the artificial charged aerosol cloud of the negative or positive polarity correspondingly. Analysis and generalization of the experimental results have shown that the maximal probability of the channel discharge initiation and propagation stimulation has been observed for the artificial charged cloud of the positive polarity when the arrays of the model hydrometeors of the cylinder revolution form have been used. At the same time, for the artificial charged clouds of the negative polarity, application of the model hydrometeor array of the plate rhombus form has provided the maximal probability of the channel discharge formation between the charged cloud and the ground. The established influence of the form of the model hydrometeors on the channel discharge initiation and from the artificial charged water aerosol cloud and its following successful propagation has been related with the different character of the positive and negative streamer and volume leader development on the model hydrometeors array being near the bottom boundary of the charged cloud. The received experimental results have shown the possibly important role of the form of the large hail particles precipitated in thundercloud on the discharge initiation.

Keywords: cloud and channel discharges, hydrometeor form, lightning initiation, negative and positive artificial charged aerosol cloud

Procedia PDF Downloads 290