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Articles de revues sur le sujet « Hybrid Analytical Intelligence »

Auteur : Grafiati
Publié le 2 juin 2025
Mis à jour le 31 juillet 2025

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1

Shiriaev, Е. М., А. S. Nazarov, N. N. Kucherov, and М. G. Babenko. "Analytical review of confidential artificial intelligence: methods and algorithms for deployment in cloud computing." Programmirovanie, no. 4 (December 2, 2024): 27–40. https://doi.org/10.31857/s0132347424040036.

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The technologies of artificial intelligence and cloud systems have recently been actively developed and implemented. In this regard, the issue of their joint use, which has been topical for several years, has become more acute. The problem of data privacy preservation in cloud computing acquired the status of critical long before the necessity of their joint use with artificial intelligence, which made it even more complicated. This paper presents an overview of both the artificial intelligence and cloud computing techniques themselves, as well as methods to ensure data privacy. The review con
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Manoilo, A. V. ""Hybrid diplomacy": on the production of personnel in the field of countering foreign information and hybrid wars." Diplomaticheskaja sluzhba (Diplomatic Service), no. 2 (March 31, 2023): 130–39. http://dx.doi.org/10.33920/vne-01-2302-05.

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This article is devoted to the issues of personnel training to counter the infected and hybrid free war against the Russian Federation in connection with the start of the Special Military Operation in Ukraine. It is noted that programs such as "Information and hybrid wars" are a structure for the formation in the Russian Federation of a nationwide system to counter information and hybrid war. In modern programs for training specialists in countering information and hybrid wars of great importance for international and analytical work, the main justifications and methods are disclosed in the co
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Yu, Ming, Haotian Lu, Hai Wang, Chenyu Xiao, Dun Lan, and Junjie Chen. "Computational Intelligence-Based Prognosis for Hybrid Mechatronic System Using Improved Wiener Process." Actuators 10, no. 9 (2021): 213. http://dx.doi.org/10.3390/act10090213.

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In this article, a fast krill herd algorithm is developed for prognosis of hybrid mechatronic system using the improved Wiener degradation process. First, the diagnostic hybrid bond graph is used to model the hybrid mechatronic system and derive global analytical redundancy relations. Based on the global analytical redundancy relations, the fault signature matrix and mode change signature matrix for fault and mode change isolation can be obtained. Second, in order to determine the true faults from the suspected fault candidates after fault isolation, a fault estimation method based on adaptive
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Elling, John W., Sharbari Lahiri, Jason P. Luck, et al. "Peer Reviewed: Hybrid Artificial Intelligence Tools for Assessing GC Data." Analytical Chemistry 69, no. 13 (1997): 409A—415A. http://dx.doi.org/10.1021/ac971691t.

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Banerjee, Debalina, Jagadeesh Putta, and Rama Mohan Rao P. "Heuristic Driven Hybrid Analytical-Artificial Intelligence Concept for Risk Prediction in Construction Megaprojects." International Review of Civil Engineering (IRECE) 12, no. 6 (2021): 398. http://dx.doi.org/10.15866/irece.v12i6.20864.

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Hingant, Javier, Marcelo Zambrano, Francisco J. Pérez, Israel Pérez, and Manuel Esteve. "HYBINT: A Hybrid Intelligence System for Critical Infrastructures Protection." Security and Communication Networks 2018 (August 27, 2018): 1–13. http://dx.doi.org/10.1155/2018/5625860.

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Cyberattacks, which consist of exploiting security vulnerabilities of computer networks and systems for any kind of malicious purpose (e.g., extortion, data steal, assets hijacking), have been continuously increasing worldwide in recent years. Cyberspace appears today as a new battlefield, along with physical world scenarios (land, sea, air, and space), for the organizations defence and security. Besides, by the fact that attacks from the physical world may have significant implications in the cyber world and vice versa, these dimensions cannot be understood independently. However, the most co
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ZHANG, Y. Y., and P. S. P. WANG. "ANALYTICAL COMPARISON OF THINNING ALGORITHMS." International Journal of Pattern Recognition and Artificial Intelligence 07, no. 05 (1993): 1227–46. http://dx.doi.org/10.1142/s0218001493000601.

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This paper deals with analyzing and comparing several key thinning algorithms in terms of various different methodologies. From these analyses and comparisons, a new sequential model thinning algorithm using heuristic, hybrid methods is presented. It intends to produce, based on past experience, thinned skeletons of thickness one from input patterns, keep connectivity, and eliminate unnecessary pixels. Several illustrative examples on various patterns were tested and the results compared. The parallel model thinning algorithm is still open.
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Moser, Benjamin L., Joshua A. Gordon, and Andrew J. Petruska. "Unified Parameterization and Calibration of Serial, Parallel, and Hybrid Manipulators." Robotics 10, no. 4 (2021): 124. http://dx.doi.org/10.3390/robotics10040124.

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In this work, we present methods allowing parallel, hybrid, and serial manipulators to be analyzed, calibrated, and controlled with the same analytical tools. We introduce a general approach to describe any robotic manipulator using established serial-link representations. We use this framework to generate analytical kinematic and calibration Jacobians for general manipulator constructions using null space constraints and extend the methods to hybrid manipulator types with complex geometry. We leverage the analytical Jacobians to develop detailed expressions for post-calibration pose uncertain
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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, no. 05 (2021): 1–13. http://dx.doi.org/10.5121/ijwmn.2021.13501.

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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
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Akinyede, Akinyede, Josephine Adenike, Akinyede Akinyede, Temitope Temitope, Alake Alake, and Temitope John. "A Review of Analytical and Intelligent Methods for Optimal Distributed Generation Placement in Modern Distribution Networks: Techniques, Challenges, and Future Directions." International Journal of Advances in Engineering and Management 6, no. 11 (2024): 108–17. https://doi.org/10.35629/5252-0611108117.

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This comprehensive review examines the state-ofthe-art methodologies for optimal Distributed Generation (DG) placement in modern distribution networks, addressing the critical challenges of power quality enhancement and system reliability. The study systematically analyses various approaches including analytical methods, optimization techniques, artificial intelligence algorithms, graph theory applications, simulation-based methods, and hybrid solutions. Recent research indicates that optimized DG placement can achieve up to 60% reduction in system losses and 15% improvement in voltage profile
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Sonar, Rajendra M. "Towards Automation of Business Intelligence Services Using Hybrid Intelligent System Approach." International Journal of Business Intelligence Research 4, no. 4 (2013): 61–92. http://dx.doi.org/10.4018/ijbir.2013100105.

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Business Intelligence (BI) includes many tools, techniques and technologies. BI processes often involve team of human decision makers and end-users to extract, explore and analyse the data. The results, decisions or models after analysis need to be implemented into operational systems. There can be considerable time delay between business events happening and action taken thus loosing opportunities. Intelligent techniques such as rule-based reasoning and case-based reasoning have been used extensively to address wide range of intelligent tasks including personalisation and recommendation. Some
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Zarichuk, Oleksii. "Hybrid approaches to machine learning in software development: Applying artificial intelligence to automate and improve processes." DEVELOPMENT MANAGEMENT 21, no. 4 (2023): 53–60. http://dx.doi.org/10.57111/devt/4.2023.53.

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The study on hybrid machine learning approaches is relevant because these approaches have great potential to improve predictive accuracy and software automation, and their use is becoming more widespread. The purpose of this study was to provide recommendations for the use of hybrid machine learning methods and analyse the areas of application of artificial intelligence, which is used to automate and improve processes. Problems related to hybrid approaches to machine learning were identified using the analytical method. The use of the statistical method allowed assessing the development of sta
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Nozari, Hamed. "Green Supply Chain Management based on Artificial Intelligence of Everything." Journal of Economics and Management 46 (2024): 171–88. http://dx.doi.org/10.22367/jem.2024.46.07.

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Aim/purpose – This research aims to design an analytical framework to investigate the dimensions, factors, and key indicators affecting the green supply chain based on the innovative technology of Artificial Intelligence of Everything (AIoE). Understanding the cause-and-effect relationships of all actors in this smart and sustainable system is also one of the critical goals of this research. Also, examining the key features of AIoE tech- nology as a new hybrid technology is one of this research’s most essential features. Design/methodology/approach – This research has tried to extract and refi
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Qu, Jiachen, and Jingtong Wang. "Real-time data warehousing in the big data environment: A comprehensive review of implementation in the internet industry." Applied and Computational Engineering 88, no. 1 (2024): 110–19. http://dx.doi.org/10.54254/2755-2721/88/20241643.

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A real-time data warehouse is a crucial tool for information management and analysis, enabling the capture, processing, and analysis of vast amounts of data from diverse sources in real-time. It offers enterprises enhanced decision support through its efficient processing capabilities and timely data feedback. This paper reviews the technical characteristics and application scenarios of real-time data warehouses, with a particular focus on the Internet sector. It explores the evolution from traditional data warehouses to modern data lake and lakehouse architectures, emphasizing the advancement
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Sarfaraz, Ambreen. "THE COMPETITION MYTH: EXPLORING THE SYMBIOSIS BETWEEN HUMAN AND ARTIFICIAL INTELLIGENCE." Journal of Arts & Social Sciences 11, no. 2 (2024): 63–68. https://doi.org/10.46662/jass.v11i2.488.

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The generally conceived belief that jobs are at danger of being replaced by AI has sparked concern which has resulted in human intelligence being seen as in conflict with AI systems. However, this perceived rivalry obscures a more profound reality: the integration between human beings and artificial intelligence. For that reason, this study refutes the competition myth and shows that people and AI can work together. Human intelligence is superior in adaptability, feelings and the ability to consider the circumstances, whereas AI is faster, flexible at scale, and analytical. Combinatorial of th
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Asad, Bilal, Toomas Vaimann, Anouar Belahcen, Ants Kallaste, Anton Rassõlkin, and M. Naveed Iqbal. "The Cluster Computation-Based Hybrid FEM–Analytical Model of Induction Motor for Fault Diagnostics." Applied Sciences 10, no. 21 (2020): 7572. http://dx.doi.org/10.3390/app10217572.

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This paper presents a hybrid finite element method (FEM)–analytical model of a three-phase squirrel cage induction motor solved using parallel processing for reducing the simulation time. The growing development in artificial intelligence (AI) techniques can lead towards more reliable diagnostic algorithms. The biggest challenge for AI techniques is that they need a big amount of data under various conditions to train them. These data are difficult to obtain from the industries because they contain low numbers of possible faulty cases, as well as from laboratories because a limited number of m
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Malsagov, Bekhan, Fardiana Ketova, and Danila Makienko. "Evolvement of artificial intelligence and hybrid methods for modeling and optimization in complex systems." ITM Web of Conferences 72 (2025): 01004. https://doi.org/10.1051/itmconf/20257201004.

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Artificial intelligence (AI) has rapidly evolved to become an integral part of our daily lives, with its absence potentially causing significant disruptions. This paper examines the current trajectory of AI development and its projected impact on society in the near future. We explore the fundamental aspects of AI technology, its applications across various sectors, and the associated challenges and opportunities. The study also investigates the integration of hybrid methods for modeling and optimization in complex systems, highlighting their synergistic relationship with AI advancements. By c
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Korneeva, A. M. "Conceptual Foundations of Information Modeling Technology in Urban Planning." Journal of Law and Administration 19, no. 3 (2023): 89–97. http://dx.doi.org/10.24833/2073-8420-2023-3-68-89-97.

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Introduction. For the management of complex urban planning systems, the problems of choosing alternatives and finding effective solutions under conditions of risk and uncertainty in the interaction of many exogenous and endogenous factors are of great theoretical and practical importance. A special place in decision-making is occupied by an integrated approach that allows, based on artificial intelligence models, expert assessment, analytical and computational models, modeling methods and a number of other models and methods, to successfully apply various approaches to decision support, to pro
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Correia, António, Andrea Grover, Daniel Schneider, et al. "Designing for Hybrid Intelligence: A Taxonomy and Survey of Crowd-Machine Interaction." Applied Sciences 13, no. 4 (2023): 2198. http://dx.doi.org/10.3390/app13042198.

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With the widespread availability and pervasiveness of artificial intelligence (AI) in many application areas across the globe, the role of crowdsourcing has seen an upsurge in terms of importance for scaling up data-driven algorithms in rapid cycles through a relatively low-cost distributed workforce or even on a volunteer basis. However, there is a lack of systematic and empirical examination of the interplay among the processes and activities combining crowd-machine hybrid interaction. To uncover the enduring aspects characterizing the human-centered AI design space when involving ensembles
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Sieberg, Philipp Maximilian, and Dieter Schramm. "Ensuring the Reliability of Virtual Sensors Based on Artificial Intelligence within Vehicle Dynamics Control Systems." Sensors 22, no. 9 (2022): 3513. http://dx.doi.org/10.3390/s22093513.

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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-base
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Bazyl, Olena, Oryngul Abilova, Olena Karpenko, Hnat Mierienkov, and Anastasiia Poliakova. "Assessing the impact of artificial intelligence integration on educational processes in higher education institutions of Ukraine and Kazakhstan." Sustainable Engineering and Innovation 7, no. 1 (2025): 97–116. https://doi.org/10.37868/sei.v7i1.id418.

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The study focuses on assessing the impact of artificial intelligence on educational processes in higher education institutions. It considers aspects of administrative task automation, personalization of learning, and ethical challenges. The topic's relevance is driven by global trends in the digital transformation of education and the need to adapt systems to modern challenges. A descriptive approach was used, using secondary data from scientific publications, statistical reports, and analytical studies. The data were analyzed using statistical and correlation methods, allowing us to identify
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Vourganas, Ioannis, Vladimir Stankovic, and Lina Stankovic. "Individualised Responsible Artificial Intelligence for Home-Based Rehabilitation." Sensors 21, no. 1 (2020): 2. http://dx.doi.org/10.3390/s21010002.

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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 comorbidit
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Jha, Anjaneya, and Bimlesh Kumar. "Particle Swarm Optimization Neural Network for Flow Prediction in Vegetative Channel." Journal of Intelligent Systems 22, no. 4 (2013): 487–501. http://dx.doi.org/10.1515/jisys-2013-0003.

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AbstractFlow prediction in a vegetated channel has been extensively studied in the past few decades. A number of equations that essentially differ from each other in derivation and form have been developed. Because the process is extremely complex, getting the deterministic or analytical form of the process phenomena is too difficult. Hybrid neural network model (combining particle swarm optimization with neural network) is particularly useful in modeling processes where an adequate knowledge of the physics is limited. This hybrid model is presented here as a complementary tool to model channe
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Ragab, Abdul Hamid M., Amin Y. Noaman, Ahmed A. A. Gad-Elrab, and Ezz H. Abdul Fatah. "A New Self-Assessment “TQM Hybrid MCDM Fuzzy Model” For Enhancing the KPIs in Mega Universities." Applied Computational Intelligence and Soft Computing 2022 (June 20, 2022): 1–18. http://dx.doi.org/10.1155/2022/4540875.

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The key performance indicators (KPIs) are an effective part of benchmarking for measuring the performance of universities aiming to raise the university quality services level. The problem is that there is no general standard analytical evaluation model for assessing KPIs criteria in higher education institutes for evaluating mega universities, and there are inadequacies in the treatment of specific current educational quality standards. In this paper, a hybrid fuzzy analytical model for TQM self-assessment to enhance KPIs in mega universities is proposed. The proposed model produces important
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Petrina, Denis. "THE PROBLEMATIC OF ARTIFICIAL INTELLIGENCE (AI) IN COGNITIVE CAPITALISM: TOWARD POSTHUMANIST CONTOURS OF THE NEW COGNITARIAT." Topos 1, no. 1-2025 (2025): 175–203. https://doi.org/10.61095/815-0047-2025-1-175-203.

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This paper examines the problematic of artificial intelligence (AI) through the lens of contemporary critical theory, offering a dual analytical perspective. On one hand, AI is explored as a symptom of cognitive capitalism, which operates through its key mechanisms, such as modulation, control, and the generation of informational surplus value. On the other hand, the paper raises the issue of exploiting hybrid human-machine labor, emphasizing the need to transcend anthropocentric models and reconceptualize solidarity in a posthumanist framework. In the first section, AI is analyzed as a form o
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Abu Sarhan, Mohammad, Andrzej Bien, and Szymon Barczentewicz. "Use of analytical hierarchy process for selecting and prioritizing islanding detection methods in power grids." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 3 (2024): 2422. http://dx.doi.org/10.11591/ijece.v14i3.pp2422-2435.

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One of the problems that are associated to power systems is islanding condition, which must be rapidly and properly detected to prevent any negative consequences on the system's protection, stability, and security. This paper offers a thorough overview of several islanding detection strategies, which are divided into two categories: classic approaches, including local and remote approaches, and modern techniques, including techniques based on signal processing and computational intelligence. Additionally, each approach is compared and assessed based on several factors, including implementation
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Hirunyatrakul, Putthiphan. "Hybrid Intersection: Navigating Context and Constraint in AI for Social Good Among Thailand’s Smallholder Farmers." Sustainability 17, no. 13 (2025): 5792. https://doi.org/10.3390/su17135792.

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Artificial intelligence is increasingly deployed as a vehicle for “social good” in agriculture, ostensibly advancing the UN Sustainable Development Goals whilst uplifting smallholders. This study examines how such claims materialise through a selective case study analysis of eleven Thai Agricultural AI providers, analysing governance practices and impact framing. The research develops the “hybrid intersection” concept as an analytical lens for understanding how Agricultural AI simultaneously delivers genuine social benefits whilst reproducing structural constraints that limit transformative ch
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Finet, Alain, Kevin Kristoforidis, and Julie Laznicka. "The Limits of AI in Understanding Emotions: Challenges in Bridging Human Experience and Machine Perception." F1000Research 14 (June 13, 2025): 582. https://doi.org/10.12688/f1000research.164796.1.

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Background This article explores the potential of Artificial Intelligence (AI) to replace the human factor in the analysis of emotions within financial decision-making contexts. The background of this study is as follows: growing reliance on Artificial Intelligence for text analysis, understanding its capacity to detect emotional patterns is particularly relevant in fields where emotional dynamics significantly influence behavior, such as trading. Method Our research method is based on a three-day trading experiment involving students, during which participants made decisions under conditions
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Kazemipoor, Mahnaz, Mehdi Rezaeian, Maryam Kazemipoor, Sareena Hamzah, and Shishir Kumar Shandilya. "Computational Intelligence Techniques for Assessing Anthropometric Indices Changes in Female Athletes." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 4 (2020): 288–95. http://dx.doi.org/10.2174/1573405614666180905111814.

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Background: Physical characteristics including body size and configuration, are considered as one of the key influences on the optimum performance in athletes. Despite several analyzing methods for modeling the slimming estimation in terms of reduction in anthropometric indices, there are still weaknesses of these models such as being very demanding including time taken for analysis and accuracy. Objective: This research proposes a novel approach for determining the slimming effect of a herbal composition as a natural medicine for weight loss. Methods: To build an effective prediction model, a
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Preeta Pillai. "Cloud vs. On-Premise Data Warehousing: A Strategic Analysis for Financial Institutions." Journal of Computer Science and Technology Studies 7, no. 3 (2025): 503–13. https://doi.org/10.32996/jcsts.2025.7.3.57.

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The transformation of data warehousing in financial services marks a pivotal shift in how institutions manage and utilize data assets. Financial organizations navigate complex decisions between cloud-based, on-premise, and hybrid solutions, each offering distinct advantages and challenges. The evolution encompasses enhanced security protocols, improved regulatory compliance mechanisms, and advanced analytical capabilities. Modern implementations demonstrate substantial improvements in operational efficiency, cost optimization, and system performance. The integration of artificial intelligence
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Naga Swetha, D., and Savadam Balaji. "Agriculture Cloud System based Emphatic Data Analysis and Crop Yield Prediction Using Hybrid Artificial Intelligence." Journal of Physics: Conference Series 2040, no. 1 (2021): 012010. http://dx.doi.org/10.1088/1742-6596/2040/1/012010.

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Abstract In agricultural nations, such as India, where agriculture leads more to India’s Economic growth, it plays a significant part. The prediction of the crop is one of the main tasks in agriculture. Crop prediction methods are employed by detecting different soil parameters and factors connected to the atmosphere for predicting the appropriate crop. The unstable climate exposes farmers to danger in the environment. Therefore the correct history data must be maintained is essential. The data stored may be evaluated to predict agricultural production. In a cloud server, experts analyze sense
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Wen, Kefei, and Clement Gosselin. "Kinematically Redundant Hybrid Robots With Simple Singularity Conditions and Analytical Inverse Kinematic Solutions." IEEE Robotics and Automation Letters 4, no. 4 (2019): 3828–35. http://dx.doi.org/10.1109/lra.2019.2928756.

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Fakir, Kossai, Chouaib Ennawaoui, and Mahmoud El Mouden. "Deep Learning Algorithms to Predict Output Electrical Power of an Industrial Steam Turbine." Applied System Innovation 5, no. 6 (2022): 123. http://dx.doi.org/10.3390/asi5060123.

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Among the levers carried in the era of Industry 4.0, there is that of using Artificial Intelligence models to serve the energy interests of industrial companies. The aim of this paper is to estimate the active electrical power generated by industrial units that self-produce electricity. To do this, we conduct a case study of the historical data of the variables influencing this parameter to support the construction of three analytical models three analytical models based on Deep Learning algorithms, which are Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), as well as the hyb
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Pałczyński, Krzysztof, Sandra Śmigiel, Marta Gackowska, Damian Ledziński, Sławomir Bujnowski, and Zbigniew Lutowski. "IoT Application of Transfer Learning in Hybrid Artificial Intelligence Systems for Acute Lymphoblastic Leukemia Classification." Sensors 21, no. 23 (2021): 8025. http://dx.doi.org/10.3390/s21238025.

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Acute lymphoblastic leukemia is the most common cancer in children, and its diagnosis mainly includes microscopic blood tests of the bone marrow. Therefore, there is a need for a correct classification of white blood cells. The approach developed in this article is based on an optimized and small IoT-friendly neural network architecture. The application of learning transfer in hybrid artificial intelligence systems is offered. The hybrid system consisted of a MobileNet v2 encoder pre-trained on the ImageNet dataset and machine learning algorithms performing the role of the head. These were the
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Özkuyumcu, Süleyman Aykutalp, and Ayşe Kalaycı Önaç. "PERFORMANCE ANALYSIS OF ARTIFICIAL INTELLIGENCE TOOLS IN DIGITIZATION OF LOST CULTURAL HERITAGE: SARAY-I AMIRE." TURKISH JOURNAL OF FOREST SCIENCE 9, no. 1 (2025): 12–24. https://doi.org/10.32328/turkjforsci.1676049.

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The digitization of cultural heritage plays a critical role in the preservation of historical artifacts and their transmission to future generations. This study focuses on the digital reconstruction of the Saray-ı Amire in Manisa, a lost architectural structure from the Ottoman period, and evaluates the performance of artificial intelligence (AI) tools throughout this process. Traditional modeling techniques are compared with AI-based algorithms in terms of accuracy, speed, and level of detail. Data derived from archival documents, historical maps, engravings, and analogous structures were uti
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Mohammad, Abu Sarhan, Bien Andrzej, and Barczentewicz Szymon. "Use of analytical hierarchy process for selecting and prioritizing islanding detection methods in power grids." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 3 (2024): 2422–35. https://doi.org/10.11591/ijece.v14i3.pp2422-2435.

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One of the problems that are associated to power systems is islanding condition, which must be rapidly and properly detected to prevent any negative consequences on the system's protection, stability, and security. This paper offers a thorough overview of several islanding detection strategies, which are divided into two categories: classic approaches, including local and remote approaches, and modern techniques, including techniques based on signal processing and computational intelligence. Additionally, each approach is compared and assessed based on severa
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S, Subha, Baghavathi Priya Sankaralingam, Anitha Gurusamy, Sountharrajan Sehar, and Durga Prasad Bavirisetti. "Personalization-based deep hybrid E-learning model for online course recommendation system." PeerJ Computer Science 9 (November 27, 2023): e1670. http://dx.doi.org/10.7717/peerj-cs.1670.

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Deep learning, a subset of artificial intelligence, gives easy way for the analytical and physical tasks to be done automatically. There is a less necessity for human intervention while performing these tasks. Deep hybrid learning is a blended approach to combine machine learning with deep learning. A hybrid deep learning (HDL) model using convolutional neural network (CNN), residual network (ResNet) and long short term memory (LSTM) is proposed for better course selection of the enrolled candidates in an online learning platform. In this work, a hybrid framework that facilitates the analysis
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Batistatos, Michael C., Tomaso de Cola, Michail Alexandros Kourtis, Vassiliki Apostolopoulou, George K. Xilouris, and Nikos C. Sagias. "AGRARIAN: A Hybrid AI-Driven Architecture for Smart Agriculture." Agriculture 15, no. 8 (2025): 904. https://doi.org/10.3390/agriculture15080904.

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Modern agriculture is increasingly challenged by the need for scalable, sustainable, and connectivity-resilient digital solutions. While existing smart farming platforms offer valuable insights, they often rely heavily on centralized cloud infrastructure, which can be impractical in rural or remote settings. To address this gap, this paper presents AGRARIAN, a hybrid AI-driven architecture that combines IoT sensor networks, UAV-based monitoring, satellite connectivity, and edge-cloud computing to deliver real-time, adaptive agricultural intelligence. AGRARIAN supports a modular and interoperab
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Zhang, Jincheng, Thada Jantakoon, and Rukthin Laoha. "Meta-Analysis of Artificial Intelligence in Education." Higher Education Studies 15, no. 2 (2025): 189. https://doi.org/10.5539/hes.v15n2p189.

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This meta-analysis examined the effectiveness of artificial intelligence (AI) technologies in educational settings through a systematic review of 13 empirical studies conducted across eight countries. We analysed the impact of various AI technologies on educational outcomes using PRISMA guidelines and multiple analytical approaches, including novel applications of Naive Bayes, TF-IDF, and BERT-based algorithms. The overall analysis revealed a significant positive effect size (Hedges' g = 0.86, 95% CI [0.45, 1.27], p < 0.0001), indicating substantial benefits of AI integration in
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Jiang, Qin, Zhiping Chai, Zisheng Zong, Zhitong Hu, Shuo Zhang, and Zhigang Wu. "Micro/Nano Soft Film Sensors for Intelligent Plant Systems: Materials, Fabrications, and Applications." Chemosensors 11, no. 3 (2023): 197. http://dx.doi.org/10.3390/chemosensors11030197.

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Being abundant as natural intelligence, plants have attracted huge attention from researchers. Soft film sensors present a novel and promising approach to connect plants with artificial devices, helping us to investigate plants’ intelligence further. Here, recent developments for micro/nano soft film sensors that can be used for establishing intelligent plant systems are summarized, including essential materials, fabrications, and application scenarios. Conductive metals, nanomaterials, and polymers are discussed as basic materials for active layers and substrates of soft film sensors. The cor
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Huang, Baoyu, and Eksiri Niyomsilp. "The impact of artificial intelligence on organizational decision-making processes." Edelweiss Applied Science and Technology 9, no. 4 (2025): 794–808. https://doi.org/10.55214/25768484.v9i4.6081.

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Using a mixed-methods approach, data was collected through quantitative surveys (N=258) and qualitative interviews with AI practitioners and decision-makers across multiple industries. Findings indicate that AI significantly improves decision efficiency by automating analytical tasks, reducing human cognitive biases, and enabling real-time insights. However, challenges persist, particularly in algorithmic transparency, ethical governance, and compliance with regulatory standards. Key findings reveal that AI integration positively influences decision effectiveness (β=0.156, p=0.031), but human
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Azamuddin, Wan Muhd Hazwan, Azana Hafizah Mohd Aman, Rosilah Hassan, and Norhisham Mansor. "Comparison of Named Data Networking Mobility Methodology in a Merged Cloud Internet of Things and Artificial Intelligence Environment." Sensors 22, no. 17 (2022): 6668. http://dx.doi.org/10.3390/s22176668.

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In-network caching has evolved into a new paradigm, paving the way for the creation of Named Data Networking (NDN). Rather than simply being typical Internet technology, NDN serves a range of functions, with a focus on consumer-driven network architecture. The NDN design has been proposed as a method for replacing Internet Protocol (IP) addresses with identified content. This study adds to current research on NDN, artificial intelligence (AI), cloud computing, and the Internet of Things (IoT). The core contribution of this paper is the merging of cloud IoT (C-IoT) and NDN-AI-IoT. To be precise
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Kim, Dong-Wook, Hong-Gi Ahn, Jeeyoung Kim, Choon-Sik Yoon, Ji-Hong Kim, and Sejung Yang. "Advanced Kidney Volume Measurement Method Using Ultrasonography with Artificial Intelligence-Based Hybrid Learning in Children." Sensors 21, no. 20 (2021): 6846. http://dx.doi.org/10.3390/s21206846.

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In this study, we aimed to develop a new automated method for kidney volume measurement in children using ultrasonography (US) with image pre-processing and hybrid learning and to formulate an equation to calculate the expected kidney volume. The volumes of 282 kidneys (141 subjects, <19 years old) with normal function and structure were measured using US. The volumes of 58 kidneys in 29 subjects who underwent US and computed tomography (CT) were determined by image segmentation and compared to those calculated by the conventional ellipsoidal method and CT using intraclass correlation coeff
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Waqas Ali and Aakash Ali. "ENHANCING SOFTWARE QUALITY: A NOVEL APPROACH TO BUG LOCALIZATION USING HYBRID AI TECHNIQUES." Kashf Journal of Multidisciplinary Research 1, no. 11 (2024): 42–51. https://doi.org/10.71146/kjmr124.

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This research introduces a novel hybrid Artificial Intelligence (AI) model for bug localization aimed at improving software quality by accurately pinpointing defects in code. By integrating the analytical strengths of a Support Vector Machine (SVM) with the heuristic insight of a rule-based system, our approach seeks to address the intricacies and nuances inherent in software debugging. The proposed model was trained and tested on a synthesized dataset reflecting a diverse range of bug severities and software features, intending to simulate real-world scenarios. The performance was evaluated u
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Huang, Hsu-Chih, Sendren Sheng-Dong Xu, and Huan-Shiuan Hsu. "Hybrid Taguchi DNA Swarm Intelligence for Optimal Inverse Kinematics Redundancy Resolution of Six-DOF Humanoid Robot Arms." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/358269.

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This paper presents a hybrid Taguchi deoxyribonucleic acid (DNA) swarm intelligence for solving the inverse kinematics redundancy problem of six degree-of-freedom (DOF) humanoid robot arms. The inverse kinematics problem of the multi-DOF humanoid robot arm is redundant and has no general closed-form solutions or analytical solutions. The optimal joint configurations are obtained by minimizing the predefined performance index in DNA algorithm for real-world humanoid robotics application. The Taguchi method is employed to determine the DNA parameters to search for the joint solutions of the six-
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Aleksandrova, Yanka, and Mihail Radev. "Combining Machine Learning with Seasonal-Trend Decomposition using LOESS in Power BI." Izvestia Journal of the Union of Scientists - Varna Economic Sciences Series 13, no. 1 (2024): 81–89. https://doi.org/10.56065/ijusv-ess/2024.13.1.81.

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Time series analysis has been extensively used for forecasting in various industries. A method frequently used for decomposition of time series is Seasonal-Trend decomposition using LOESS (STL). In combination with the machine learning approaches, STL is a helpful method to analyze the seasonal-trend structure of complicated time series. This hybrid approach helps interpret seasonality, trends, and other residual patterns better than when using only predictive machine learning models. The explanation and interpretation of the models can be effectively implemented in the context of Business Int
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Moscoso-Zea, Oswaldo, Jorge Castro, Joel Paredes-Gualtor, and Sergio Luján-Mora. "A Hybrid Infrastructure of Enterprise Architecture and Business Intelligence & Analytics for Knowledge Management in Education." IEEE Access 7 (March 20, 2019): 38778–88. https://doi.org/10.1109/ACCESS.2019.2906343.

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Advances in science and technology, the Internet of Things, and the proliferation of mobile apps are critical factors to the current increase in the amount, structure, and size of information that organizations have to store, process, and analyze. Traditional data storages present technical deficiencies when handling huge volumes of data and are not adequate for process modeling and business intelligence; to cope with these deficiencies, new methods and technologies have been developed under the umbrella of big data. However, there is still the need in higher education institutions (HEIs) of a
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Manimurugan, S., Saad Almutairi, Majed Mohammed Aborokbah, et al. "Two-Stage Classification Model for the Prediction of Heart Disease Using IoMT and Artificial Intelligence." Sensors 22, no. 2 (2022): 476. http://dx.doi.org/10.3390/s22020476.

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Internet of Things (IoT) technology has recently been applied in healthcare systems as an Internet of Medical Things (IoMT) to collect sensor information for the diagnosis and prognosis of heart disease. The main objective of the proposed research is to classify data and predict heart disease using medical data and medical images. The proposed model is a medical data classification and prediction model that operates in two stages. If the result from the first stage is efficient in predicting heart disease, there is no need for stage two. In the first stage, data gathered from medical sensors a
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Liu, Tong, Fariza Sabrina, Julian Jang-Jaccard, Wen Xu, and Yuanyuan Wei. "Artificial Intelligence-Enabled DDoS Detection for Blockchain-Based Smart Transport Systems." Sensors 22, no. 1 (2021): 32. http://dx.doi.org/10.3390/s22010032.

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A smart public transport system is expected to be an integral part of our human lives to improve our mobility and reduce the effect of our carbon footprint. The safety and ongoing maintenance of the smart public transport system from cyberattacks are vitally important. To provide more comprehensive protection against potential cyberattacks, we propose a novel approach that combines blockchain technology and a deep learning method that can better protect the smart public transport system. By the creation of signed and verified blockchain blocks and chaining of hashed blocks, the blockchain in o
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Binsawad, Muhammad, Ghazanfar Ali Abbasi, and Osama Sohaib. "People’s expectations and experiences of big data collection in the Saudi context." PeerJ Computer Science 8 (March 16, 2022): e926. http://dx.doi.org/10.7717/peerj-cs.926.

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Big data and machine learning technologies facilitate various business intelligence activities for businesses. However, personal data collection can generate adverse effects on consumers. Big data collection can compromise people’s sense of autonomy, harming digital privacy, transparency and trust. This research investigates personal data collection, control, awareness, and privacy regulation on people’s autonomy in Saudi. This study used a hybrid analytical model that incorporates symmetrical and asymmetrical analysis via fuzzy set qualitative comparative analysis (fsQCA) to analyze consumer
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