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1
Zhang, Nian, Yunpeng Han, Quanshen Si e Guiwu Wei. "A novel method for multi-attribute risk decision-making based on regret theory and hybird information". Journal of Intelligent & Fuzzy Systems 39, n. 5 (19 novembre 2020): 6955–64. http://dx.doi.org/10.3233/jifs-200081.
Abstract (sommario):
To consider the decision makers’ regret behavior and describe the hybrid evolution information in the risk decision-making problem, a new approach is proposed based on regret theory in this paper. Firstly, the probable value of different states are calculated by Pignistic probability transformation method. Secondly, the relative closeness formula of hybrid information are established and the utility values of alternatives are computed. Then, decision makers’ utility values are obtained according to the regret theory. Moreover, the overall perceived utility values of alternatives are obtained by weighted arithmetic mean and got the optimal one by the ranking order. Finally, an numerical example is illustrated the method and comparative analysis are offered between the proposed approach and other existed methods to show that is feasible and usable.
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Douzi, Samira, Feda A. AlShahwan, Mouad Lemoudden e Bouabid El Ouahidi. "Hybrid Email Spam Detection Model Using Artificial Intelligence". International Journal of Machine Learning and Computing 10, n. 2 (febbraio 2020): 316–22. http://dx.doi.org/10.18178/ijmlc.2020.10.2.937.
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Chen, Liming, Huansheng Ning, Chris D. Nugent e Zhiwen Yu. "Hybrid Human-Artificial Intelligence". Computer 53, n. 8 (agosto 2020): 14–17. http://dx.doi.org/10.1109/mc.2020.2997573.
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Abubakar, A. Mohammed. "Using hybrid SEM – artificial intelligence". Personnel Review 49, n. 1 (19 novembre 2019): 67–86. http://dx.doi.org/10.1108/pr-06-2017-0180.
Abstract (sommario):
Purpose Boreout is a psychological state of intense boredom and apathy. Characterized by the absence of mental stimuli (i.e. menial tasks) required to keep employees conscious about their environment, and this incessant decline in mental stimuli may turn employees into “professional zombies.” The diversity in work needs and preferences across generations has become a key organizational factor, generational differences have been studied in Western countries, not much information is available about generational cohorts and satisfaction (i.e. career, life and job satisfaction) in developing countries. The purpose of this paper is to provide more insights on these phenomena. Design/methodology/approach Drawing upon conservation of resources theory, this paper examines the potential effects of boreout on important job outcomes (i.e. career, life and job satisfaction) conditioned by generation (Gen-Xers and Gen-Yers) in the service industry. Data analyses with Artificial Intelligence technique (i.e. artificial neural network) and structural equation modeling were conducted with data collated from Nigerian service employees. Findings Results revealed that boreout has a negative impact on career, life and job satisfaction. The hypothesized relationships were significantly moderated by generation cohorts as Gen-Xers and Gen-Yers were found to be distinct cohorts. Originality/value This paper advocates that non-western organizations should avoid utmost service standardization and rigid stylization of work processes and procedures.
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Feuerecker, Benedikt, Maurice M. Heimer, Thomas Geyer, Matthias P. Fabritius, Sijing Gu, Balthasar Schachtner, Leonie Beyer et al. "Artificial Intelligence in Oncological Hybrid Imaging". Nuklearmedizin - NuclearMedicine 62, n. 05 (ottobre 2023): 296–305. http://dx.doi.org/10.1055/a-2157-6810.
Abstract (sommario):
Background Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications are desirable for lesion detection and characterization in primary staging, therapy monitoring, and recurrence detection. Given the rapid developments in machine learning (ML) and deep learning (DL) methods, the role of AI will have significant impact on the imaging workflow and will eventually improve clinical decision making and outcomes. Methods and Results The first part of this narrative review discusses current research with an introduction to artificial intelligence in oncological hybrid imaging and key concepts in data science. The second part reviews relevant examples with a focus on applications in oncology as well as discussion of challenges and current limitations. Conclusion AI applications have the potential to leverage the diagnostic data stream with high efficiency and depth to facilitate automated lesion detection, characterization, and therapy monitoring to ultimately improve quality and efficiency throughout the medical imaging workflow. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based therapy guidance in oncology. However, significant challenges remain regarding application development, benchmarking, and clinical implementation. Key Points:
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González Quirós, José Luis, e David Díaz Pardo de Vera. "Theory of mind: from artificial intelligence to hybrid intelligence". TECHNO REVIEW. International Technology, Science and Society Review 9, n. 2 (18 gennaio 2021): 103–19. http://dx.doi.org/10.37467/gka-revtechno.v9.2816.
Abstract (sommario):
Philosophy of mind has long ceased to be, if indeed it ever was, the exclusive domain of philosophers. In contemporary thought there is increasing interest in these matters from the point of view of technology. This paper gives a critique of the ideas of Ray Kurzweil and briefly reviews some of the recent trends in the treatment of these questions.
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Jarrahi, Mohammad Hossein, Christoph Lutz e Gemma Newlands. "Artificial intelligence, human intelligence and hybrid intelligence based on mutual augmentation". Big Data & Society 9, n. 2 (luglio 2022): 205395172211428. http://dx.doi.org/10.1177/20539517221142824.
Abstract (sommario):
There is little consensus on what artificial intelligence (AI) systems may or may not embrace. Although this may point to multiplicity of interpretations and backgrounds, a lack of conceptual clarity could thwart the development of common ground around the concept among researchers, practitioners and users of AI and pave the way for misinterpretation and abuse of the concept. This article argues that one of the effective ways to delineate the concept of AI is to compare and contrast it with human intelligence. In doing so, the article broaches the unique capabilities of humans and AI in relation to one another (human and machine tacit knowledge), as well as two types of AI systems: one that goes beyond human intelligence and one that is necessarily and inherently tied to it. It finally highlights how humans and AI can augment their capabilities and intelligence through synergistic human–AI interactions (i.e., human-augmented AI and augmented human intelligence), resulting in hybrid intelligence, and concludes with a future-looking research agenda.
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Gudova, M. Yu, E. V. Rubtsova e N. A. Simbirtseva. "Communication Trends in the Post-Literacy Era: From Human Creativity to the Creativity of Artificial Intelligence and Human-Machine Hybrids". Izvestia Ural Federal University Journal Series 1. Issues in Education, Science and Culture 27, n. 2 (2021): 235–49. http://dx.doi.org/10.15826/izv1.2021.27.2.048.
Abstract (sommario):
The article is based on the materials of the Fifth International Theoretical Scientific Conference “Communication trends in the post-literacy era: polylingualism, multimodality and polyculturalism as preconditions for new creativity”, which took place at the Institute of Humanities in November 26–28, 2020. The authors analyze the main communication trends that have developed under the influence of the Covid-2019 pandemic in the sociocultural space in 2020. The main trend is the use of artificial intelligence in such areas of socioculture as communication, media, education. The concept of creativity is clarified, the creative possibilities and limits of human and artificial intelligence are considered, the threats and dangers of the artificial intelligence‘s development and its implementation in various spheres of human life are analyzed, such as education, socialization and inculturation, journalism and mass information, contemporary art, museum and exhibition activity. The conclusion is made about the need for further interdisciplinary research of artificial intelligence in the humanitarian sphere.
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Fox, Stephen. "Beyond AI: Multi-Intelligence (MI) Combining Natural and Artificial Intelligences in Hybrid Beings and Systems". Technologies 5, n. 3 (22 giugno 2017): 38. http://dx.doi.org/10.3390/technologies5030038.
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Lukyanova, Ekaterina D. "Artificial Intelligence: Achievements and Postponed Risks". Sociologicheskaja nauka i social naja praktika 7, n. 1 (2019): 142–48. http://dx.doi.org/10.19181/snsp.2019.7.1.6275.
Abstract (sommario):
The article considers the problem of the development of artificial intelligence, its ambivalent impact on society and humanity. There are the description and definition of artificial intelligence, the problem field was defined. We conducted a study of the manifest and latent functions of artificial intelligence, which can provoke postponed risks. We considered the vision of artificial intelligence as a complex socio-biotechnological hybrid and a qualitatively new mechanism of social control. The achievements and risk-based role of artificial intelligence in modern complex society was estimated. Particular attention is paid to the nature of knowledge about the phenomenon of artificial intelligence, acquiring an interdisciplinary character. Some deferred risks related to the functioning of artificial intelligence are indicated. The factors contributing to the creation of a socio-biotechnological hybrid in the form of artificial intelligence are considered in the context of the riskological turn. The risks of a new type of irrational rationality are considered, the cause of which, according to the author, is the introduction of IT and artificial intelligence, the consequences of rationality are given. The problem of the risk of deintimization, depriving a person of opportunities and the right to personal information is noted. That problem may entail new manifestations of alienation in the form of the dehumanization of social and natural realities. due to the introduction in many programs of artificial intelligence. The article deals with the problem of the pluralization of knowledge about artificial intelligence, the use of performance mechanisms that produce fakes, absolutizing positive or negative consequences of the functioning of artificial intelligence. The article summarizes the state of the risks of artificial intelligence at the moment. Recommendations for further interdisciplinary study of artificial intelligence are presented. The article substantiates the importance of the implementation of the risk assessment of the emerging programs based on artificial intelligence.
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Farooq, Omer, e Jasmeen Gill. "Vegetable Grading and Sorting using Artificial Intelligence". International Journal for Research in Applied Science and Engineering Technology 10, n. 3 (31 marzo 2022): 13–21. http://dx.doi.org/10.22214/ijraset.2022.40407.
Abstract (sommario):
Abstract: Agriculture and food industry are the backbone of any country. Food industry is the prime contributor in agricultural sector. Thus, automation of vegetable grading and sorting is the need of the hour. Since, artificial neural networks are best suited for automated pattern recognition problems; they are used as a classification tool for this research. Back propagation is the most important algorithm for training neural networks. But, it easily gets trapped in local minima leading to inaccurate solutions. Therefore, some global search and optimization techniques were required to hybridize with artificial neural networks. One such technique is Genetic algorithms that imitate the principle of natural evolution. So, in this article, a hybrid intelligent system is proposed for vegetable grading and sorting in which artificial neural networks are merged with genetic algorithms. Results show that proposed hybrid model outperformed the existing back propagation based system. Keywords: Vegetable grading and sorting; artificial neural networks; Particle Swarm Optimization; Hybrid intelligent system; Pattern recognition
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ȘUȘNEA, Elena, e Ionuț-Cosmin BUȚĂ. "ARTIFICIAL INTELLIGENCE IN HYBRID WARFARE: A LITERATURE REVIEW AND CLASSIFICATION". STRATEGIES XXI - Security and Defense Faculty 17, n. 1 (1 novembre 2021): 294–302. http://dx.doi.org/10.53477/2668-2001-21-37.
Abstract (sommario):
Artificial intelligence contributes greatly to enhance situational awareness, providing early warning and contributing to the decision making process in the hybrid warfare context. Artificial intelligence brings a paradigm shift to “new” wars and threats, powered by increasing availability of military data and rapid progress of artificial intelligence techniques. The purpose of this paper is to identify researchers’ interest in the use of “artificial intelligence” in the “hybrid warfare" environment and to establish the topics they approach. In this respect, the aim of this paper is to produce a literature review by accessing a scientific database in order to perform an analysis on how connected topics, such as: machine learning, data mining, deep learning and artificial neural network are integrated in the military domain.
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Feng, Shan, e Li D. Xu. "Hybrid artificial intelligence approach to urban planning". Expert Systems 16, n. 4 (novembre 1999): 248–61. http://dx.doi.org/10.1111/1468-0394.00117.
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Vinnik, Dmitriy Vladimirovich. "ON THE POSSIBILITY OF HYBRID ARTIFICIAL INTELLIGENCE". Философия науки, n. 3 (2022): 92–109. http://dx.doi.org/10.15372/ps20220308.
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Liu, Yuqi, e Zhiyong Fu. "Hybrid Intelligence: Design for Sustainable Multiverse via Integrative Cognitive Creation Model through Human–Computer Collaboration". Applied Sciences 14, n. 11 (29 maggio 2024): 4662. http://dx.doi.org/10.3390/app14114662.
Abstract (sommario):
The unprecedented development of artificial intelligence (AI) makes it possible for computers to imitate and surpass human intelligence (HI). Hybrid intelligence is the result of the co-evolution of AI and HI and has huge application potential in promoting the sustainable development of human society. This study starts from the similarities and differences between biological neural networks and artificial neural networks, compares the cognitive foundations of human intelligence and artificial intelligence, highlights the difference and connection between AI and HI, and puts forward the necessity and inevitability of their co-evolution to achieve hybrid intelligence with complementary advantages. Hybrid intelligence stands to become the pivotal force driving purposeful and planned sustainable creative behavior in the artificial intelligence era. This study proposes a design cognitive creation model based on human–computer collaboration that considers computational design thinking as the central concept. Moreover, the paradigm shift of design under hybrid intelligence intervention are explored from five aspects: “tool evolution”, “response mode”, “output result”, “iterative optimization” and “system innovation”. Finally, this article constructs a creative intervention mechanism of design creation driven by hybrid intelligence and discusses its role playing in the design activities of sustainable multiverse construction in the future. The proposal of the multiverse model transcends the confines of the metaverse’s virtual worldview and embraces sustainable development for value guidance. It advocates a future trajectory for humanity that hinges on technological progress, fostering a prosperous, balanced, and harmonious coexistence between the natureverse, socialverse, and digitalverse. This approach is not only rational and scientific, but also inherently sustainable.
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Vityaev, Evgeny E., Sergey S. Goncharov e Dmitry I. Sviridenko. "On the task approach in artificial intelligence". Siberian Journal of Philosophy 17, n. 4 (2019): 5–25. http://dx.doi.org/10.25205/2541-7517-2019-17-4-5-25.
Abstract (sommario):
The authors discuss the problem of the integration approach to artificial intelligence, analyzing the content and positive aspects of the integration agent approach. It is noted that this approach implicitly follows the task approach. The paper gives answers to the questions that make up the essence of the task approach - where do the tasks come from, what is the task, what should be considered a solution to the problem. It also discusses the classification of intellectual problems into direct, inverse, and hybrid. It is noted that modern artificial intelligence focuses mainly on solving direct and inverse problems, leaving a huge and important class of hybrid problems outside its scope of attention. The paper describes the theoretical model approach to solving the whole variety of intellectual problems, called semantic modeling. It analyzes the advantages of the proposed conception, including the possibility of a flexible combination when solving hybrid problems of tools already created in artificial intelligence. It also discusses the problem of creating a “strong” / “general” artificial intelligence (AGI) in the framework of the task approach.
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Kshirsagar, Pravin R., D. B. V. Jagannadham, Hamed Alqahtani, Quadri Noorulhasan Naveed, Saiful Islam, M. Thangamani e Minilu Dejene. "Human Intelligence Analysis through Perception of AI in Teaching and Learning". Computational Intelligence and Neuroscience 2022 (11 giugno 2022): 1–9. http://dx.doi.org/10.1155/2022/9160727.
Abstract (sommario):
Instructional practices have undergone a drastic change as a result of the development of new educational technology. Artificial intelligence (AI) as a teaching and learning technology will be examined in this theoretical review study. To enhance the quality of teaching and learning, the use of artificial intelligence approaches is being studied. Artificial intelligence integration in educational institutions has been addressed, though. Students’ assistance, teaching, learning, and administration are also addressed in the discussion of students’ adoption of artificial intelligence. Artificial intelligence has the potential to revolutionize our social interactions and generate new teaching and learning methods that may be evaluated in a variety of contexts. New educational technology can help students and teachers better accomplish and manage their educational objectives. Artificial intelligence algorithms are used in a hybrid teaching mode in this work to examine students’ attributes and introduce predictions of future learning success. The teaching process may be carried out in a more efficient manner using the hybrid mode. Educators and scientists alike will benefit from artificial intelligence algorithms that may be used to extract useful information from the vast amounts of data collected on human behavior.
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Dushkin, Roman. "On the path towards strong artificial intelligence: cognitive architecture based on a psychophysiological foundation and hybrid principles". Программные системы и вычислительные методы, n. 1 (gennaio 2021): 22–34. http://dx.doi.org/10.7256/2454-0714.2021.1.34243.
Abstract (sommario):
This article describes the author's proposal of cognitive architecture for the development of artificial intelligence agent of the general level (“strong" artificial intelligence”). The new principles for the development of such architecture are offered: hybrid approach in artificial intelligence and psychophysiological foundations. The scheme of architecture of the proposed solution, as well as the descriptions of possible areas of implementation are given. Strong artificial intelligence represents a technical solution that can solve arbitrary cognitive tasks accessible to humans (human level intelligence), and even beyond the capabilities of human intelligence (artificial superintelligence). The areas of application of strong artificial intelligence are limitless – from solving the current problems faced by humans to completely new tasks that are yet inaccessible to human civilization or expect for their groundbreaker. This study would be interested to the scholars, engineers and researchers dealing with artificial intelligence, as well as to the readers who want to keep in step with modern technologies. The novelty consists in the original approach towards building a cognitive architecture that has absorbed the results of previous research in the area of artificial intelligence. The relevance of this work is based on the indisputable fact that currently, the research in the area of weak artificial intelligence begin to slow down due to the inability to solve general problems, and the majority of national strategies of the advanced countries in the area of artificial intelligence declare the need for the development of new artificial intelligence technologies, including the artificial intelligence of general level.
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Ali, Jarinah Mohd, e M. A. Hussain. "Artificial Intelligence Based State Observer in Polymerization Process". ASEAN Journal of Chemical Engineering 13, n. 2 (17 settembre 2014): 50. http://dx.doi.org/10.22146/ajche.49731.
Abstract (sommario):
Observers or state estimators are devices used to estimate immeasurable key parameters that are due to noise, disturbances and mismatch. It is important to identify those variables prior to construct a control system and avoid fault or process disruption. In certain chemical processes, such observer usage produced unsatisfactory results therefore hybrid approached is the appropriate solution. Hybrid observers are combination of two or more conventional observers mainly to enhance the estimator’s performance and overcoming their limitations. In advanced cases, Artificial Intelligence algorithm is applied. This paper develops two hybrid observers namely sliding mode and extended Luenberger observers with fuzzy logic for approximating the monomer concentration in a polymerization reactor. It was found that the sliding mode observer- fuzzy combination is better based on noise handling with less oscillation.
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Althoff, Daniel, Helizani Couto Bazame e Jessica Garcia Nascimento. "Untangling hybrid hydrological models with explainable artificial intelligence". H2Open Journal 4, n. 1 (1 gennaio 2021): 13–28. http://dx.doi.org/10.2166/h2oj.2021.066.
Abstract (sommario):
Abstract Hydrological models are valuable tools for developing streamflow predictions in unmonitored catchments to increase our understanding of hydrological processes. A recent effort has been made in the development of hybrid (conceptual/machine learning) models that can preserve some of the hydrological processes represented by conceptual models and can improve streamflow predictions. However, these studies have not explored how the data-driven component of hybrid models resolved runoff routing. In this study, explainable artificial intelligence (XAI) techniques are used to turn a ‘black-box’ model into a ‘glass box’ model. The hybrid models reduced the root-mean-square error of the simulated streamflow values by approximately 27, 50, and 24% for stations 17120000, 27380000, and 33680000, respectively, relative to the traditional method. XAI techniques helped unveil the importance of accounting for soil moisture in hydrological models. Differing from purely data-driven hydrological models, the inclusion of the production storage in the proposed hybrid model, which is responsible for estimating the water balance, reduced the short- and long-term dependencies of input variables for streamflow prediction. In addition, soil moisture controlled water percolation, which was the main predictor of streamflow. This finding is because soil moisture controls the underlying mechanisms of groundwater flow into river streams.
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Albrecht, Stefano, Bruno Bouchard, John S. Brownstein, David L. Buckeridge, Cornelia Caragea, Kevin M. Carter, Adnan Darwiche et al. "Reports of the 2016 AAAI Workshop Program". AI Magazine 37, n. 3 (7 ottobre 2016): 99–108. http://dx.doi.org/10.1609/aimag.v37i3.2680.
Abstract (sommario):
The Workshop Program of the Association for the Advancement of Artificial Intelligence’s Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) was held at the beginning of the conference, February 12-13, 2016. Workshop participants met and discussed issues with a selected focus — providing an informal setting for active exchange among researchers, developers and users on topics of current interest. To foster interaction and exchange of ideas, the workshops were kept small, with 25-65 participants. Attendance was sometimes limited to active participants only, but most workshops also allowed general registration by other interested individuals. The AAAI-16 Workshops were an excellent forum for exploring emerging approaches and task areas, for bridging the gaps between AI and other fields or between subfields of AI, for elucidating the results of exploratory research, or for critiquing existing approaches. The fifteen workshops held at AAAI-16 were Artificial Intelligence Applied to Assistive Technologies and Smart Environments (WS-16-01), AI, Ethics, and Society (WS-16-02), Artificial Intelligence for Cyber Security (WS-16-03), Artificial Intelligence for Smart Grids and Smart Buildings (WS-16-04), Beyond NP (WS-16-05), Computer Poker and Imperfect Information Games (WS-16-06), Declarative Learning Based Programming (WS-16-07), Expanding the Boundaries of Health Informatics Using AI (WS-16-08), Incentives and Trust in Electronic Communities (WS-16-09), Knowledge Extraction from Text (WS-16-10), Multiagent Interaction without Prior Coordination (WS-16-11), Planning for Hybrid Systems (WS-16-12), Scholarly Big Data: AI Perspectives, Challenges, and Ideas (WS-16-13), Symbiotic Cognitive Systems (WS-16-14), and World Wide Web and Population Health Intelligence (WS-16-15).
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Rosienkiewicz, Maria. "Artificial intelligence-based hybrid forecasting models for manufacturing systems". Eksploatacja i Niezawodnosc - Maintenance and Reliability 23, n. 2 (17 febbraio 2021): 263–77. http://dx.doi.org/10.17531/ein.2021.2.6.
Abstract (sommario):
The paper addresses the problem of forecasting in manufacturing systems. The main aim of the research is to propose new hybrid forecasting models combining artificial intelligencebased methods with traditional techniques based on time series – namely: Hybrid econometric model, Hybrid artificial neural network model, Hybrid support vector machine model and Hybrid extreme learning machine model. The study focuses on solving the problem of limited access to independent variables. Empirical verification of the proposed models is built upon real data from the three manufacturing system areas – production planning, maintenance and quality control. Moreover, in the paper, an algorithm for the forecasting accuracy assessment and optimal method selection for industrial companies is introduced. It can serve not only as an efficient and costless tool for advanced manufacturing companies willing to select the right forecasting method for their particular needs but also as an approach supporting the initial steps of transformation towards smart factory and Industry 4.0 implementation.
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Wang, Lingling. "Research on Online and Offline Hybrid Teaching of Introduction to Artificial Intelligence". Advances in Economics and Management Research 4, n. 1 (16 marzo 2023): 261. http://dx.doi.org/10.56028/aemr.4.1.261.2023.
Abstract (sommario):
In view of the current teaching situation and challenges faced by the Introduction to Artificial Intelligence course, this paper analyzes the core teaching objectives of the course, and explores the online and offline hybrid teaching methods of it. This paper analyzes the advantages of online and offline hybrid teaching, and probes into the development mode of online and offline hybrid teaching of Introduction to Artificial Intelligence from the aspects of teaching content, teaching mode, teaching effect, examination method, etc.
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Yildirim, Yetkin, Emin Alp Arslan, Kamil Yildirim e Ibrahim Bisen. "Reimagining Education with Artificial Intelligence". Eurasian Journal of Higher Education 2, n. 4 (24 settembre 2021): 32–46. http://dx.doi.org/10.31039/ejohe.2021.4.52.
Abstract (sommario):
Artificial intelligence (AI) technologies have been implemented successfully in many industries, from virtual hospital assistants to algorithm-based warehouse processing. And now that Covid-19 has forced students and teachers to transition to online or hybrid learning, these technologies could offer new and exciting possibilities for education as well. By incorporating AI and machine learning tools into online classrooms, educators can address many of the challenges that have emerged with the recent loss of face-to-face instruction, including the struggle for students to self-regulate their learning, the burden of curriculum planning and administrative work for teachers, and the loss of personalized interaction between students and teachers. This chapter will explore some of the AI technologies that have been used in educational contexts and describe applications of AI in other industries that could be adapted to create more personalized, flexible, inclusive, and engaging learning experiences. If the future of education is going to include online learning as a substantial component, then AI could be the key to maintaining high levels of motivation and engagement from students and teachers alike.
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Sokolov, I. A. "Theory and practice in artificial intelligence". Вестник Российской академии наук 89, n. 4 (24 aprile 2019): 365–70. http://dx.doi.org/10.31857/s0869-5873894365-370.
Abstract (sommario):
Artificial Intelligence is an interdisciplinary field, and formed about 60 years ago as an interaction between mathematical methods, computer science, psychology, and linguistics. Artificial Intelligence is an experimental science and today features a number of internally designed theoretical methods: knowledge representation, modeling of reasoning and behavior, textual analysis, and data mining. Within the framework of Artificial Intelligence, novel scientific domains have arisen: non-monotonic logic, description logic, heuristic programming, expert systems, and knowledge-based software engineering. Increasing interest in Artificial Intelligence in recent years is related to the development of promising new technologies based on specific methods like knowledge discovery (or machine learning), natural language processing, autonomous unmanned intelligent systems, and hybrid human-machine intelligence.
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Su, Shenglin, Xianglin Yan, Kodjo Agbossou, Richard Chahine e Yi Zong. "Artificial intelligence for hydrogen-based hybrid renewable energy systems: A review with case study". Journal of Physics: Conference Series 2208, n. 1 (1 marzo 2022): 012013. http://dx.doi.org/10.1088/1742-6596/2208/1/012013.
Abstract (sommario):
Abstract In recent years, with the progress of computer technology, artificial intelligence has been rapidly developed and begun to be applied in industry, economy and other aspects. Besides, with the pursuit of green hydrogen, hydrogen-based hybrid renewable energy systems have become the focus of the development of the hydrogen industry. This paper compares different artificial intelligence applications in hydrogen-based hybrid renewable energy systems and carries out a case study in a typical area. Firstly, this paper summarizes important works in literature, which use artificial intelligence methods to predict the supply chain of the renewable energy system, including the prediction of renewable energy system resources, output power, load demand and terminal electricity price. Secondly, main articles about artificial intelligence optimization algorithms used in renewable energy systems are also summarized, including swarm and non-swarm biological heuristics, physical or chemical heuristics and hybrid optimization algorithms. Finally, a case study is carried out in Tikanlik, Xinjiang, China. Tikanlik’s weather and load data train the artificial neural network to predict system output power. It shows that 99.32% of the relative error of the test set is less than 3%, which proves that this model can achieve good prediction results.
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ALkharabsheh, Muyad Mahmmoud, Mohammad Alshraideh e Imad Salah. "Enhancing Cybercrime Deterrence with Artificial Intelligence". International Journal of Advanced Networking and Applications 15, n. 04 (2023): 6015–27. http://dx.doi.org/10.35444/ijana.2024.15404.
Abstract (sommario):
Web phishing poses a significant security challenge for web users owing to three primary factors. First, it is easy to implement and does not require profound technical expertise in programming or networking. Second, it can be executed across various platforms, including the web, SMS, and social media platforms. Finally, this type of attack relies on social engineering, meaning that users' responses are influenced by the content presented to them. Over the past few decades, there has been a proliferation of methods and services designed for phishing detection. In this study, we introduced a novel approach to web phishing detection based on a hybrid weighted machine learning framework. Our method harnesses the capabilities of four distinct machine learning algorithms, including an unsupervised approach (K-means) and three supervised techniques. The outputs of these algorithms were strategically weighted to produce a final decision. To train and evaluate our proposed algorithm, we employed a vast dataset encompassing no content web features, totaling 111 distinct attributes. The correlations between these features and the classification outcomes were leveraged to streamline the feature set, and various correlation values were explored. Our findings from the training and validation phases underscore the significance of the correlation between the chosen features in determining the accuracy of the algorithm. In summary, our research introduces an innovative approach to combat web phishing, showcasing the potential of hybrid machine learning techniques and the critical role of feature selection through correlation analysis to enhance detection accuracy. The accuracy outcomes of the various algorithms exhibited a range of values, ranging from 0.6561 to 0.8833, across different correlation thresholds when considering all features.
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Nedjah, Nadia, Ajith Abraham e Luiza M. Mourelle. "Hybrid artificial neural network". Neural Computing and Applications 16, n. 3 (28 febbraio 2007): 207–8. http://dx.doi.org/10.1007/s00521-007-0083-0.
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Zougagh, Nisrine, Abdelkabir Charkaoui e Abdelwahed Echchatbi. "Artificial intelligence hybrid models for improving forecasting accuracy". Procedia Computer Science 184 (2021): 817–22. http://dx.doi.org/10.1016/j.procs.2021.04.013.
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Haddadian, Hamidreza, Morteza Baky Haskuee e Gholamreza Zomorodian. "A Hybrid Artificial Intelligence Approach to Portfolio Management". Iranian Journal of Finance 6, n. 1 (1 gennaio 2022): 1–27. http://dx.doi.org/10.30699/ijf.2021.287131.1237.
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Corchado, J. M., e J. Aiken. "Hybrid artificial intelligence methods in oceanographic forecast models". IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 32, n. 4 (novembre 2002): 307–13. http://dx.doi.org/10.1109/tsmcc.2002.806072.
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Wen, Chien-Hsien, e C. A. Vassiliadis. "Applying hybrid artificial intelligence techniques in wastewater treatment". Engineering Applications of Artificial Intelligence 11, n. 6 (dicembre 1998): 685–705. http://dx.doi.org/10.1016/s0952-1976(98)00036-0.
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Yan, Guilong. "The impact of Artificial Intelligence on hybrid warfare". Small Wars & Insurgencies 31, n. 4 (7 gennaio 2020): 898–917. http://dx.doi.org/10.1080/09592318.2019.1682908.
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Shi, Feifei, Fang Zhou, Hong Liu, Liming Chen e Huansheng Ning. "Survey and Tutorial on Hybrid Human-Artificial Intelligence". Tsinghua Science and Technology 28, n. 3 (giugno 2023): 486–99. http://dx.doi.org/10.26599/tst.2022.9010022.
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Alyasseri, Zaid Abdi Alkareem, Ali H. Al-Timemy, Ammar Kamal Abasi, Alexandru Lavric, Husam Jasim Mohammed, Hidenori Takahashi, Jose Arthur Milhomens Filho, Mauro Campos, Rossen M. Hazarbassanov e Siamak Yousefi. "A Hybrid Artificial Intelligence Model for Detecting Keratoconus". Applied Sciences 12, n. 24 (17 dicembre 2022): 12979. http://dx.doi.org/10.3390/app122412979.
Abstract (sommario):
Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 and 579 KCN4) from Department of Ophthalmology and Visual Sciences, Paulista Medical School, Federal University of São Paulo, São Paulo in Brazil and 1531 eyes (Healthy = 400, KCN1 = 378, KCN2 = 285, KCN3 = 200, KCN4 = 88) from Department of Ophthalmology, Jichi Medical University, Tochigi in Japan and used several accuracy metrics including Precision, Recall, F-Score, and Purity. We compared the proposed method with three other standard unsupervised algorithms including k-means, Kmedoids, and Spectral cluster. Based on two independent datasets, the proposed model outperformed the other algorithms, and thus could provide improved identification of the corneal status of the patients with keratoconus.
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Zhang, Jing. "Applied research on higher English Teaching under the background of artificial intelligence". Tobacco Regulatory Science 7, n. 5 (30 settembre 2021): 4074–79. http://dx.doi.org/10.18001/trs.7.5.1.183.
Abstract (sommario):
During a period of artificial intelligence, all walks of life make full use of Internet technical knowledge to achieve industry upgrading and transformation, and start the “artificial intelligence” mode. Recently the growth of artificial intelligence technical knowledge is becoming increasingly mature, especially in English teaching. College English teaching should seize the opportunity brought by artificial intelligence technical knowledge to realize the improvement of professional courses. And the modern teaching status derived from artificial intelligence and the traditional teaching status will be effectively integrated to build an efficient hybrid teaching mode, which promotes the reform and advancement of English education and the modernization process. This article starts from the disadvantages of higher English education mode, analyzes the opportunities that artificial intelligence technology brings to higher English teaching, and introduces the connotation of artificial intelligence technology. Then this article studies the development of Al technical knowledge in English education, and finally investigates the application of Al technical knowledge from many aspects.
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Sieberg, Philipp Maximilian, e Dieter Schramm. "Ensuring the Reliability of Virtual Sensors Based on Artificial Intelligence within Vehicle Dynamics Control Systems". Sensors 22, n. 9 (5 maggio 2022): 3513. http://dx.doi.org/10.3390/s22093513.
Abstract (sommario):
The use of virtual sensors in vehicles represents a cost-effective alternative to the installation of physical hardware. In addition to physical models resulting from theoretical modeling, artificial intelligence and machine learning approaches are increasingly used, which incorporate experimental modeling. Due to the resulting black-box characteristics, virtual sensors based on artificial intelligence are not fully reliable, which can have fatal consequences in safety-critical applications. Therefore, a hybrid method is presented that safeguards the reliability of artificial intelligence-based estimations. The application example is the state estimation of the vehicle roll angle. The state estimation is coupled with a central predictive vehicle dynamics control. The implementation and validation is performed by a co-simulation between IPG CarMaker and MATLAB/Simulink. By using the hybrid method, unreliable estimations by the artificial intelligence-based model resulting from erroneous input signals are detected and handled. Thus, a valid and reliable state estimate is available throughout.
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Damasevicius, Robertas. "Artificial Intelligence Techniques in Economic Analysis". Economic Analysis Letters 2, n. 2 (23 maggio 2023): 52–59. http://dx.doi.org/10.58567/eal02020007.
Abstract (sommario):
This paper provides an overview of the existing literature on the use of artificial intelligence (AI) in various fields, including economics, finance, mining, manufacturing, and innovation. The paper identifies the drivers and effects of AI deployment in the context of innovation and highlights the challenges and opportunities that arise from the use of AI. The studies reviewed in this paper cover various topics related to forecasting, including the impact of AI on professional skills, hybrid forecasting techniques for predicting commodity prices, and novel deep reinforcement learning algorithms for crude oil price forecasting. The paper’s contribution lies in its systematic and comprehensive approach to reviewing the literature, which allows for a better understanding of the impact of AI on various fields and the identification of strategies to address the challenges that arise from its deployment.
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Menyhárt, József. "Artificial Intelligence Possibilities in Vehicle Industry". International Journal of Engineering and Management Sciences 4, n. 4 (12 dicembre 2019): 148–54. http://dx.doi.org/10.21791/ijems.2019.4.16.
Abstract (sommario):
There have been several attempts during the last decades to extend the ranges of application of artificial intelligence. The aim of the development for AI is to replace human intelligence and experience. The ultimate aim for machines and vehicles is to run much more efficiently and with higher reliability than ever before. The Artificial Techniques (AI) used a wide range of expert systems to optimize problems. Hybrid intelligent management systems have become increasingly influential in artificial intelligence during the last decades. As a result, maintenance and fleet management systems have undergone significant development. By choosing adequate maintenance or operating strategy and taking user behaviour into consideration, these systems can not only increase the reliability and efficiency of vehicles but can also result in financial savings. The paper tries to discusses the applications of AI techniques in predictive maintenance and vehicle industry.
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Sretenovic, Aleksandra, Radisa Jovanovic, Vojislav Novakovic, Natasa Nord e Branislav Zivkovic. "Hybrid artificial intelligence model for prediction of heating energy use". Thermal Science, n. 00 (2021): 152. http://dx.doi.org/10.2298/tsci210303152s.
Abstract (sommario):
Currently, in the building sector there is an increase in energy use due to the increased demand for indoor thermal comfort. Proper energy planning based on a real measurement data is a necessity. In this study, we developed and evaluated hybrid artificial intelligence models for the prediction of the daily heating energy use. Building energy use is defined by significant number of influencing factors, while many of them are hard to define and quantify. For heating energy use modelling, complex relationship between the input and output variables is not strictly linear nor non-linear. The main idea of this paper was to divide the heat demand prediction problem into the linear and the non-linear part (residuals) by using different statistical methods for the prediction. The expectations were that the joint hybrid model, could outperform the individual predictors. Multiple Linear Regression (MLR) was selected for the linear modelling, while the non-linear part was predicted using Feedforward (FFNN) and Radial Basis (RBFN) neural network. The hybrid model prediction consisted of the sum of the outputs of the linear and the non-linear model. The results showed that the hybrid FFNN model and the hybrid RBFN model achieved better results than each of the individual FFNN and RBFN neural networks and MLR on the same dataset. It was shown that this hybrid approach improved the accuracy of artificial intelligence models.
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Kaikova, Olena, Vagan Terziyan, Timo Tiihonen, Mariia Golovianko, Svitlana Gryshko e Liudmyla Titova. "Hybrid Threats against Industry 4.0: Adversarial Training of Resilience". E3S Web of Conferences 353 (2022): 03004. http://dx.doi.org/10.1051/e3sconf/202235303004.
Abstract (sommario):
Industry 4.0 and Smart Manufacturing are associated with the Cyber-Physical-Social Systems populated and controlled by the Collective Intelligence (human and artificial). They are an important component of Critical Infrastructure and they are essential for the functioning of a society and economy. Hybrid Threats nowadays target critical infrastructure and particularly vulnerabilities associated with both human and artificial intelligence. This article summarizes some latest studies of WARN: “Academic Response to Hybrid Threats” (the Erasmus+ project), which aim for the resilience (regarding hybrid threats) of various Industry 4.0 architectures and, especially, of the human and artificial decision-making within Industry 4.0 processes. This study discovered certain analogy between (cognitive) resilience of human and artificial intelligence against cognitive hacks (special adversarial hybrid activity) and suggested the approaches to train the resilience with the special adversarial training techniques. The study also provides the recommendations for higher education institutions on adding such training and related courses to their various programs. The specifics of related courses would be as follows: their learning objectives and related intended learning outcomes are not an update of personal knowledge, skills, beliefs or values (traditional outcomes) but the robustness and resilience of the already available ones.
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Thakker, Dhavalkumar, Bhupesh Kumar Mishra, Amr Abdullatif, Suvodeep Mazumdar e Sydney Simpson. "Explainable Artificial Intelligence for Developing Smart Cities Solutions". Smart Cities 3, n. 4 (13 novembre 2020): 1353–82. http://dx.doi.org/10.3390/smartcities3040065.
Abstract (sommario):
Traditional Artificial Intelligence (AI) technologies used in developing smart cities solutions, Machine Learning (ML) and recently Deep Learning (DL), rely more on utilising best representative training datasets and features engineering and less on the available domain expertise. We argue that such an approach to solution development makes the outcome of solutions less explainable, i.e., it is often not possible to explain the results of the model. There is a growing concern among policymakers in cities with this lack of explainability of AI solutions, and this is considered a major hindrance in the wider acceptability and trust in such AI-based solutions. In this work, we survey the concept of ‘explainable deep learning’ as a subset of the ‘explainable AI’ problem and propose a new solution using Semantic Web technologies, demonstrated with a smart cities flood monitoring application in the context of a European Commission-funded project. Monitoring of gullies and drainage in crucial geographical areas susceptible to flooding issues is an important aspect of any flood monitoring solution. Typical solutions for this problem involve the use of cameras to capture images showing the affected areas in real-time with different objects such as leaves, plastic bottles etc., and building a DL-based classifier to detect such objects and classify blockages based on the presence and coverage of these objects in the images. In this work, we uniquely propose an Explainable AI solution using DL and Semantic Web technologies to build a hybrid classifier. In this hybrid classifier, the DL component detects object presence and coverage level and semantic rules designed with close consultation with experts carry out the classification. By using the expert knowledge in the flooding context, our hybrid classifier provides the flexibility on categorising the image using objects and their coverage relationships. The experimental results demonstrated with a real-world use case showed that this hybrid approach of image classification has on average 11% improvement (F-Measure) in image classification performance compared to DL-only classifier. It also has the distinct advantage of integrating experts’ knowledge on defining the decision-making rules to represent the complex circumstances and using such knowledge to explain the results.
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Goh, Rui Ying, Lai Soon Lee, Hsin-Vonn Seow e Kathiresan Gopal. "Hybrid Harmony Search–Artificial Intelligence Models in Credit Scoring". Entropy 22, n. 9 (4 settembre 2020): 989. http://dx.doi.org/10.3390/e22090989.
Abstract (sommario):
Credit scoring is an important tool used by financial institutions to correctly identify defaulters and non-defaulters. Support Vector Machines (SVM) and Random Forest (RF) are the Artificial Intelligence techniques that have been attracting interest due to their flexibility to account for various data patterns. Both are black-box models which are sensitive to hyperparameter settings. Feature selection can be performed on SVM to enable explanation with the reduced features, whereas feature importance computed by RF can be used for model explanation. The benefits of accuracy and interpretation allow for significant improvement in the area of credit risk and credit scoring. This paper proposes the use of Harmony Search (HS), to form a hybrid HS-SVM to perform feature selection and hyperparameter tuning simultaneously, and a hybrid HS-RF to tune the hyperparameters. A Modified HS (MHS) is also proposed with the main objective to achieve comparable results as the standard HS with a shorter computational time. MHS consists of four main modifications in the standard HS: (i) Elitism selection during memory consideration instead of random selection, (ii) dynamic exploration and exploitation operators in place of the original static operators, (iii) a self-adjusted bandwidth operator, and (iv) inclusion of additional termination criteria to reach faster convergence. Along with parallel computing, MHS effectively reduces the computational time of the proposed hybrid models. The proposed hybrid models are compared with standard statistical models across three different datasets commonly used in credit scoring studies. The computational results show that MHS-RF is most robust in terms of model performance, model explainability and computational time.
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Prem, Mary Jeyanthi, e M. Karnan. "Business Intelligence–Hybrid Metaheuristics Techniques". International Journal of Business Intelligence Research 5, n. 1 (gennaio 2014): 64–70. http://dx.doi.org/10.4018/ijbir.2014010105.
Abstract (sommario):
Business Intelligence (BI) is about getting the right information, to the right decision makers, at the right time. A business intelligence environment offers decision makers information and knowledge derived from data processing, through the application of mathematical models and algorithms. BI systems tend to promote a scientific and rational approach to managing enterprises and complex organizations. Soft computing is a collection of new techniques in artificial intelligence, which exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. The purpose of this article is to provide an overview of soft computing techniques for the optimal and dynamic decision making system in the current business world.
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Schneider, Ingrid. "Democratic Governance of Digital Platforms and Artificial Intelligence?" JeDEM - eJournal of eDemocracy and Open Government 12, n. 1 (16 luglio 2020): 1–24. http://dx.doi.org/10.29379/jedem.v12i1.604.
Abstract (sommario):
The article addresses the digital transformation and new power asymmetries and challenges to democracy by the world’s seven largest digital platforms. Four different governance models are examined: The Chinese authoritarian model, the libertarian US-model, the European regulatory model, and the Mexican hybrid model. The challenges of digital sovereignty and democratic governance of platform capitalism are explored.
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Vourganas, Ioannis, Vladimir Stankovic e Lina Stankovic. "Individualised Responsible Artificial Intelligence for Home-Based Rehabilitation". Sensors 21, n. 1 (22 dicembre 2020): 2. http://dx.doi.org/10.3390/s21010002.
Abstract (sommario):
Socioeconomic reasons post-COVID-19 demand unsupervised home-based rehabilitation and, specifically, artificial ambient intelligence with individualisation to support engagement and motivation. Artificial intelligence must also comply with accountability, responsibility, and transparency (ART) requirements for wider acceptability. This paper presents such a patient-centric individualised home-based rehabilitation support system. To this end, the Timed Up and Go (TUG) and Five Time Sit To Stand (FTSTS) tests evaluate daily living activity performance in the presence or development of comorbidities. We present a method for generating synthetic datasets complementing experimental observations and mitigating bias. We present an incremental hybrid machine learning algorithm combining ensemble learning and hybrid stacking using extreme gradient boosted decision trees and k-nearest neighbours to meet individualisation, interpretability, and ART design requirements while maintaining low computation footprint. The model reaches up to 100% accuracy for both FTSTS and TUG in predicting associated patient medical condition, and 100% or 83.13%, respectively, in predicting area of difficulty in the segments of the test. Our results show an improvement of 5% and 15% for FTSTS and TUG tests, respectively, over previous approaches that use intrusive means of monitoring such as cameras.
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Beloborodov, Dmitri A. "The use of artificial intelligence technologies in enterprise management: Sociological aspect". Alma mater. Vestnik Vysshey Shkoly, n. 7 (luglio 2023): 85–90. http://dx.doi.org/10.20339/am.07-23.085.
Abstract (sommario):
The article presents sociological directions for managing a modern enterprise, justifying the relevance of applying artificial intelligence technologies in enterprise management, classifying artificial intelligence technologies and addressing the risks and problems associated with using artificial intelligence technologies. The relevance of the chosen topic is due to the trend towards digital transformation and the implementation of artificial intelligence in various areas of enterprise management, as well as the role of artificial intelligence technologies as a “driver” of business process modernization. Scientific research methods include the analysis and synthesis of information presented in studies by Russian and foreign scientists, as well as empirical data obtained as a result of implementing artificial intelligence technologies in an IT company. As a result of applied sociological research, the advantages and risks of using artificial intelligence technologies were discovered and explained, and problems related to its integration into enterprise management were identified. The theoretical significance of the work lies in the fact that theoretical and methodological foundations for the application of AI technologies have been proposed. Although there are many materials devoted to defining the significance of artificial intelligence technologies, there is still a lack of research covering the sociological aspect of forming a new “hybrid” social environment and culture and its impact on the development of human resources. The article highlights the sociological aspect of integrating artificial intelligence into business practices, analyzes various approaches to defining the role and significance of human intelligence and “non-human intellectual entities”.
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Bryndin, Evgeny. "Functional and Harmonious Selforganization of Large Intellectual Agent Ensembles with Smart Hybrid Competencies via Wireless and Mobile Networks". International Journal of Wireless & Mobile Networks 13, n. 05 (31 ottobre 2021): 1–13. http://dx.doi.org/10.5121/ijwmn.2021.13501.
Abstract (sommario):
Artificial intelligence of large ensembles of intelligent agents in terms of computing power surpasses human intelligence. He is capable of artificial thinking and understanding. Giant ensembles of intellectual agents with artificial consciousness and intelligence are able, for the results set by the person necessary for him, to find solutions for their obtaining on the basis of professional competence and experience accumulation. The professional competence of artificial intelligence is the ability to use technologies, including computer vision, natural language processing, speech recognition and synthesis, intelligent decision support, as well as the use of synergistic methods, functional self-organizing methods and utility and preference criteria. For artificial intelligence, the functional organization of the system is important. The functionalism of artificial intelligence does not depend on its carrier, allows many methods of its functional implementation, as well as to form the completeness of its functions. Giant ensembles of intellectual agents with a full set of functions gradually and flexibly form events into solutions or rational behavior to obtain a given necessary result. Intelligent artificial intelligence has psychological, analytical, research, language, professional and behavioral hybrid competencies. Each competence is exercised by an intelligent agent with a competent functional professional manner. Intelligent agents form an ensemble with intelligent ethical artificial intelligence. The article is devoted to functional harmonious selforganization of ensembles of intellectual agents.Functional harmonious self-organization of the interaction of intellectual agents in different environments is carried out via wireless and mobile networks on the basis of data of a specific environment obtained by analytical competent intellectual agents. As result of research, the law of the golden section of the functional harmonious self-organization of ensembles of intellectual agents was revealed. Further research will focus on the effective use of wireless and mobile networks in the practical application of smart agent ensembles.
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Kovalev, Maksim. "The Concept of the Cognitive Cycle in tasks of General Artificial Intelligence". Artificial societies 16, n. 2 (2021): 0. http://dx.doi.org/10.18254/s207751800015292-0.
Abstract (sommario):
One of the main problems underlying research programs in the field of general artificial intelligence is the problem of the insufficiency of particular approaches and mathematical models. The paper proposes the concept of creating a hybrid artificial intelligence system for solving practical problems of extracting the meaning of a text that implements the principles of the hermeneutical circle. The concept offers a syncretic-sequential combination of connectionist, symbolic approaches, as well as methods of cluster analysis. The concept is based on the idea of creating hybrid, primarily human-machine systems.
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Gomathy, Dr C. K. "The Precision Agriculture Using Artificial Intelligence". International Journal for Research in Applied Science and Engineering Technology 9, n. 11 (30 novembre 2021): 342–45. http://dx.doi.org/10.22214/ijraset.2021.38779.
Abstract (sommario):
Abstract: Agriculture has been the sector of paramount importance as it feeds the country's population along with contributing to the GDP. Crop yield varies with a combination of factors including soil properties, climate, elevation and irrigation technique. Technological developments have fallen short in estimating the yield based on this joint dependence of the said factors. Hence, in this project a data-driven model that learns by historic soil as well as rainfall data to analyse and predict crop yield over seasons in several districts, has been developed. For this study, a particular crop, Rice, is considered. The designed hybrid neural network model identifies optimal combinations of soil parameters and blends it with the rainfall pattern in a selected region to evolve the expected crop yield. The backbone for the predictive analysis model with respect to the rainfall is based on the TimeSeries approach in Supervised Learning. The technology used for the final prediction of the crop yield is again a branch of Machine Learning, known as Recurrent Neural Networks. With two inter-communicating data-driven models working at the backend, the final predictions obtained were successful in depicting the interdependence between soil parameters for yield and weather attributes. Keywords: Precision agriculture, Artificial intelligence, Crop management, Solutions, Yield, Soil management