Field Of Intelligence Research Articles

Review: Deep Learning and Fuzzy Logic Applications

The modeling and prediction field bosses a variety of practical applications, deep learning is a powerful tool used in this field. It has been proved that deep learning is a useful technique for extracting extremely accurate predictions from complex data sources, and also Recursive neural networks have demonstrated their usefulness in language translation and caption production, but convolutional neural networks remain the dominant solution for image classification tasks. In addition, deep learning, also known as deep neural networks, involves training models with multiple layers of interconnected artificial neurons. The primary idea of deep learning is to learn data representations through raising levels of abstraction. These strategies are effective, but they don't explain how the result is produced. Without knowing how a solution is arrived at using deep learning. In the field of artificial intelligence, deep learning and fuzzy logic are two powerful techniques. In addition, fuzzy logic combines deep learning will help deep learning select the desired features and work without supervision, this will make it possible to develop reliable systems with rich DL information even in the absence of hand-labeled data. Fuzzy logic that interpreted these features will subsequently provide explanations for the system's choice of classification label. This survey highlights the various applications which use fuzzy logic to improve deep learning.

Theological Anthropology Facing Artificial Intelligence. The Investigation of an Intersection Based on Treatises

Abstract For the novelty, the research fields of Artificial Intelligence and Theological Anthropology are creatively confronted with each other, especially the emerging ideas of perfection, salvation, mind and corporeality in comparison with the verbatim meaning of theos and logos. For this purpose, both fields of research, which – historically marked – became virulent at about the same time in (ca. 1960s), are theoretically framed, systematically introduced, and classified. Afterwards, a text-critical appraisal is made based on current European institutional publications on Artificial Intelligence regarding concepts, representations and ways of speaking about the hu_man, about der mensch being beyond neuronal networks. Commonalities and challenges through the juxtaposition and found synergies are not only making a valuable contribution to subjecting concepts of hu_mankind for a sustainable relecture. Furthermore, it provides relevant insights and findings about the positioning of the new technology within (ethical) discourses about human dignity and rights, as well philosophical debates about freedom.

Design and Implementation of an Intelligent Ultrasonic System based on 51 Microcontroller

With the rapid development of microelectronics and intelligent technology, microcontroller units (MCUs) are increasingly applied in various fields. This paper proposes the design and implementation of an intelligent ultrasonic system based on a 51 microcontroller, integrating ultrasonic sensors, an LCD display, and a 51 MCU to achieve real-time distance measurement, data display, and intelligent control functions. The system utilizes a CD4069 ultrasonic sensor to transmit and receive ultrasonic waves, measuring the distance between the sensor and an object by calculating the time difference between transmission and reception. The measured distance data is processed and displayed on an LCD screen in real-time. Furthermore, the system features intelligent control capabilities, automatically adjusting and controlling devices based on the measured data, thereby enhancing the system's level of intelligence. The intelligent ultrasonic system based on the 51 microcontroller offers advantages such as low cost, high precision, and strong versatility. It can be widely used in various fields, including intelligent robots, obstacle avoidance systems, and automatic parking systems. The design and implementation of this system not only demonstrate innovative applications of ultrasonic ranging technology but also provide valuable references for the extensive application of microcontroller units in intelligent technology fields.

Advantages and Constraints from the Perspective of Adopting the AI Act for the Public Administration in Romania

The AI Act is a proposal with an exclusively innovative character in the legislative field, being the first law developed by a major regulatory body in the field of artificial intelligence (AI). The law operationalizes AI applications in three risk categories. First, it considers applications and systems that create an unacceptable risk. Second, high-risk applications are subject to specific legal requirements. Lastly, applications not explicitly banned or listed as high-risk are largely left unregulated. If the path of this act was, without a doubt, a particularly difficult one, the practical application will certainly raise several practical problems, depending on the states that will implement it. We propose, in this work, an analysis of the European normative act, from the perspective, on the one hand of the intentions pursued by the European legislator and, on the other hand, of how Romania will implement the AI legislation. We refer to possible situations in which the Romanian public administration will find itself through the prism of the advantages it can obtain, on the one hand, but also, on the other by the prism of the constraints imposed by the regulations in force. We conclude with some proposals to the legislator in the perspective of obtaining concrete advantages following the implementation of this act.

Philosophical Underpinnings of Artificial Intelligence and the Concept of Human Soul in Islam: Some Issues at the Interface

Can machines think? Are humans and their minds essentially physical entities? Particular views about the nature of intelligence and the human mind, i.e. the computational theory of mind, often appear either as the basis of or having taken inspiration from the field of Artificial Intelligence. It aims to explain intelligence by only resorting to physical explanation. From the Islamic standpoint, this might induce philosophical issues since Islam acknowledges in man the existence of spiritual substance, i.e. nafs (soul), in addition to the existence of the body and affirms its role in explaining human intelligence. Therefore, this article discusses some issues stemming from the interface between the philosophical underpinnings of Artificial Intelligence and Islam. Three issues are discussed: visual perception and abstraction, language and understanding, and knowledge and inference. The Islamic conception of human soul is taken from the works of Syed Muhammad Naquib Al-Attas. It is shown that intelligence is an aspect of the soul and its nature is spiritual and non-physical. Furthermore, intelligible forms imprinted upon the soul are also non-physical. This can also explain why the current AI lacks understanding, as shown in the ‘adversarial examples’ in visual abstraction, syntax/semantics distinction, and abduction as a ‘leap’ in reasoning.

Philosophical Underpinnings of Artificial Intelligence and the Concept of Human Soul in Islam: Some Issues at the Interface

Can machines think? Are humans and their minds essentially physical entities? Particular views about the nature of intelligence and the human mind, i.e. the computational theory of mind, often appear either as the basis of or having taken inspiration from the field of Artificial Intelligence. It aims to explain intelligence by only resorting to physical explanation. From the Islamic standpoint, this might induce philosophical issues since Islam acknowledges in man the existence of spiritual substance, i.e. nafs (soul), in addition to the existence of the body and affirms its role in explaining human intelligence. Therefore, this article discusses some issues stemming from the interface between the philosophical underpinnings of Artificial Intelligence and Islam. Three issues are discussed: visual perception and abstraction, language and understanding, and knowledge and inference. The Islamic conception of human soul is taken from the works of Syed Muhammad Naquib Al-Attas. It is shown that intelligence is an aspect of the soul and its nature is spiritual and non-physical. Furthermore, intelligible forms imprinted upon the soul are also non-physical. This can also explain why the current AI lacks understanding, as shown in the ‘adversarial examples’ in visual abstraction, syntax/semantics distinction, and abduction as a ‘leap’ in reasoning.

Evaluating the Effectiveness of Panoptic Segmentation Through Comparative Analysis

Image segmentation method is extensively used in the fields of computer vision, machine learning, and artificial intelligence. The task of segmentation is to distinguish objects in images either by their boundaries or as entire objects from the entire image. Image segmentation methods are implemented as instance, semantic, and panoptic segmentation. In this article, the panoptic segmentation method, seen as an advanced stage of instance and semantic segmentation, has been applied to three datasets and compared with the instance segmentation method. Experimental results are presented visually. Numerical results have been analyzed with the Panoptic Quality (PQ) and Semantic Quality (SQ) metrics. It has been observed that the segmentation outcome was best for the CityScapes dataset for panoptic segmentation.

Two-stage stock portfolio optimization based on AI-powered price prediction and mean-CVaR models

With the advancement of prediction methods in the field of artificial intelligence, accurate price predictions can effectively support financial portfolio selection. This paper proposes an intelligent stock portfolio selection method based on a prediction neural network, incorporating signal processing and hyperparameter optimization techniques. The method is divided into two key stages: stock price prediction and portfolio selection. In the first stage, we apply Savitzky–Golay filtering to denoise price data and reveal its patterns, and optimize the hyperparameters of the long short-term memory network using the sparrow search algorithm to achieve high-precision stock price predictions. In the second stage, we use the mean-Conditional Value-at-Risk (mean-CVaR) model to select the optimal stock allocation, considering factors such as potential returns, prediction accuracy, and growth rate. Numerical comparisons based on multiple public financial datasets demonstrate that the proposed two-stage method significantly outperforms seven benchmark methods. Specifically, on the Shanghai and Shenzhen 300 (CSI 300) Index dataset, the proposed method achieves a determination coefficient of 0.9980 and an accuracy rate of 97.05%. Additionally, its cumulative returns reach 9.38%, 8.63%, and 7.54% at different confidence levels.

A model of variant teaching for basic school students in the field of artificial intelligence

Today, the need to educate schoolchildren in the field of artificial intelligence is emphasized at the state level. However, issues related to the selection of content and organization of teaching in the field of artificial intelligence to students in basic secondary school are still controversial. Basic general education faces the problem of finding ways to improve teaching methods for schoolchildren in the field of artificial intelligence.The scientifically based implementation in the field of artificial intelligence training at the level of basic general education is associated, firstly, with the development of a model for teaching in the field of artificial intelligence in the conditions of variant school education, secondly, with the identification of approaches to organizing such teaching, thirdly, with the selection and structuring of teaching content in the field of artificial intelligence as part of an informatics course in basic secondary school, fourthly, with the integration of the teaching content in the field of artificial intelligence into various organizational forms of school education. This determined the purpose of the study.As a result of the study, approaches to the formation of the content of variant teaching in the field of artificial intelligence were identified, a model of variant teaching in the field of artificial intelligence at the level of basic general education was created, the principles of implementation of the proposed training model was highlighted, thematic modules for basic and in-depth training in the field of artificial intelligence were identified, educational and methodological materials were developed (theoretical educational material, practical tasks, laboratory work, methodological recommendations, etc.) for variant teaching in the field of artificial intelligence in the basic secondary school. Testing of the educational and methodological materials was carried out at sites in certain educational organizations in Moscow.

Exploring Zero-Day Attacks on Machine Learning and Deep Learning Algorithms

In the rapidly evolving field of artificial intelligence, machine learning (ML) and deep learning (DL) algorithms have emerged as powerful tools for solving complex problems in various domains, including cyber security. However, as these algorithms become increasingly prevalent, they also face new security challenges. One of the most significant of these challenges is the threat of zero-day attacks, which exploit unknown and unpredictable vulnerabilities in the algorithms or the data they process. This paper provides a comprehensive overview of zero-day attacks on ML/DL algorithms, exploring their types, causes, effects, and potential countermeasures. The paper begins by introducing the concept and definition of zero-day attacks, providing a clear understanding of this emerging threat. It then reviews the existing research on zero-day attacks on ML/DL algorithms, focusing on three main categories: data poisoning attacks, adversarial input attacks, and model stealing attacks. Each of these attack types poses unique challenges and requires specific countermeasures. The paper also discusses the potential impacts and risks of these attacks on various application domains. For instance, in facial expression recognition, an adversarial input attack could lead to misclassification of emotions, with serious implications for user experience and system integrity. In object classification, a data poisoning attack could cause the algorithm to misidentify critical objects, potentially endangering human lives in applications like autonomous driving. In satellite intersection recognition, a model stealing attack could compromise national security by revealing sensitive information. Finally, the paper presents some possible protection methods against zero-day attacks on ML/DL algorithms. These include anomaly detection techniques to identify unusual patterns in the data or the algorithm’s behaviour, model verification and validation methods to ensure the algorithm’s correctness and robustness, federated learning approaches to protect the privacy of the training data, and differential privacy techniques to add noise to the data or the algorithm’s outputs to prevent information leakage. The paper concludes by highlighting some open issues and future directions for research in this area, emphasizing the need for ongoing efforts to secure ML/DL algorithms against zero-day attacks.

Synthetic and privacy-preserving traffic trace generation using generative AI models for training Network Intrusion Detection Systems

Network Intrusion Detection Systems (NIDS) are crucial tools for protecting networked devices from cyberattacks. Recent development in the field of Artificial Intelligence (AI) has provided tremendous advantages in implementing NIDSs able to monitor network traffic and block cyberattacks in real-time. In the literature, it is widely recognized that the effective training of a NIDS requires a large quantity of labeled traffic, representative of attacks. Nonetheless, the availability of public and abundant datasets remains remarkably restricted due to the cost of gathering and labeling real traffic traces and privacy concerns for sharing them. To tackle these challenges, in this paper we present a generative AI model capable of synthesizing anonymized traffic traces from real ones, thus dealing with privacy, abundance, and representativeness. The proposal is based on a Conditional Variational Autoencoder (CVAE) and a preprocessing procedure specifically designed for the generation of new traffic traces. To validate our solution, we conduct an extensive empirical study leveraging three recent and publicly-available datasets, containing benign and malicious traffic. The validation is carried out from both the perspectives of classification performance of a robust NIDS and the quality of synthetic data, in comparison to the utilization of real data. We compare our CVAE with two state-of-the-art AI-based traffic data generators and prove that, trained with traces emitted by our generative model, a NIDS has a limited F1-score loss compared to training on real data; competing models instead struggle or fail to generate traces that are as effective for NIDS training and as statistically similar to the original. We make the synthetic datasets available in both PCAP and tabular formats, to facilitate the reproducibility of our findings and encourage further exploration in the field of generative AI for networking.

MDGRL: Multi-dimensional graph rule learning

Knowledge graph completion is an advanced artificial intelligence (AI) methodology that enables the systematic organization and structuring of data. It can significantly enhance the digital economy by facilitating more accurate and appropriate decision-making processes. Logical rule algorithms, known for their interpretability, have attracted significant attention in the field of explainable artificial intelligence. To harness the interpretability benefits of logical rules, we propose a cross-level position constraint template based on Graph Path Feature Learning (GPFL) and introduce an optimized termination policy for rule generation. To improve prediction performance, we focus significant emphasis on the rule evaluation stage, specifically on how to effectively learn the rules. To this end, we propose a Multi-Dimensional Graph Rule Learning (MDGRL) that calculates features from different dimensions to represent the reasoning ability of the rule. Feature I presents metrics for assessing the similarity into rule’s structure. Feature II proposes calculating the path transfer probability to represent the rule reasoning ability from the path reasoning perspective. Moreover, Feature III incorporates embedding distance with the constraint template to represent the rule reasoning ability in a low-dimensional vector space. Lastly, we assign weights to different dimensional features based on their performance and integrate them into MDGRL. Our experiments demonstrate effectiveness across various datasets, showcasing time-saving benefits in rule generation and improved prediction performance in rule evaluation. The source code and dataset for the MDGRL algorithm are available at https://github.com/csjywu1/MDGRL.

The electronic knowledge publishing based on intellect modeling

This paper focuses on a new concept based on modeling human intelligence for electronic knowledge publishing. It discusses the limitations of current publishing methods in solving real-life problems and proposes the need for a new type of publishing. It identifies two main streams of publishing: Emotional publishing and knowledge texts publishing, highlighting the challenges of the latter. The article evaluates various attempts in the field of Artificial Intelligence, such as expert systems and neural networks, in terms of knowledge transfer and problem-solving. It suggests that understanding the principles of human intelligence, as exemplified by the character Sherlock Holmes, can contribute to the development of the proposed “Intellect Modeling” concept. Overall, the article presents a comprehensive proposal that addresses the shortcomings of existing publishing models and offers a new approach that incorporates advancements in Artificial Intelligence and Knowledge Management.

Class conditioned text generation with style attention mechanism for embracing diversity

In the field of artificial intelligence and natural language processing (NLP), natural language generation (NLG) has significantly advanced. Its primary aim is to automatically generate text in a manner resembling human language. Traditional text generation has mainly focused on binary style transfers, limiting the scope to simple transformations between positive and negative tones or between modern and ancient styles. However, accommodating style diversity in real scenarios presents greater complexity and demand. Existing methods usually fail to capture the richness of diverse styles, hindering their utility in practical applications. To address these limitations, we propose a multi-class conditioned text generation model. We overcome previous constraints by utilizing a transformer-based decoder equipped with adversarial networks and style-attention mechanisms to model various styles in multi-class text. According to our experimental results, the proposed model achieved better performance compared to the alternatives on multi-class text generation tasks in terms of diversity while it preserves fluency. We expect that our study will help researchers not only train their models but also build simulated multi-class text datasets for further research.

A review of research on reinforcement learning algorithms for multi-agents

In recent years, multi-agent reinforcement learning techniques have been widely used and evolved in the field of artificial intelligence. However, traditional reinforcement learning methods have limitations such as long training time, large sample data requirements, and highly delayed rewards. Therefore, this paper systematically and specifically studies the MARL algorithm. Firstly, this paper uses Citespace software to visually analyze the existing literature on multi-agent reinforcement learning and briefly indicates the research hotspots and key research directions in this field. Secondly, the applications of traditional reinforcement learning algorithms under two task objects, namely single-agent and multi-agent systems, are described in detail. Then, the paper highlights the diverse applications, challenges, and corresponding solutions of MARL algorithmic techniques in the field of MAS. Finally, the paper points out future research directions based on the existing limitations of the algorithm. Through this paper, readers will gain a systematic and in-depth understanding of MARL algorithms and how they can be utilized to better address the various challenges posed by MAS.

Using Graphs to Perform Effective Sensor-Based Human Activity Recognition in Smart Homes.

There has been a resurgence of applications focused on human activity recognition (HAR) in smart homes, especially in the field of ambient intelligence and assisted-living technologies. However, such applications present numerous significant challenges to any automated analysis system operating in the real world, such as variability, sparsity, and noise in sensor measurements. Although state-of-the-art HAR systems have made considerable strides in addressing some of these challenges, they suffer from a practical limitation: they require successful pre-segmentation of continuous sensor data streams prior to automated recognition, i.e., they assume that an oracle is present during deployment, and that it is capable of identifying time windows of interest across discrete sensor events. To overcome this limitation, we propose a novel graph-guided neural network approach that performs activity recognition by learning explicit co-firing relationships between sensors. We accomplish this by learning a more expressive graph structure representing the sensor network in a smart home in a data-driven manner. Our approach maps discrete input sensor measurements to a feature space through the application of attention mechanisms and hierarchical pooling of node embeddings. We demonstrate the effectiveness of our proposed approach by conducting several experiments on CASAS datasets, showing that the resulting graph-guided neural network outperforms the state-of-the-art method for HAR in smart homes across multiple datasets and by large margins. These results are promising because they push HAR for smart homes closer to real-world applications.

Survey on Systematic Analysis of Deep Learning Models Compare to Machine Learning

This survey provides a comprehensive analysis of the systematic differences and advancements between deep learning (DL) and traditional machine learning (ML) models. By examining a wide array of research papers, the study highlights the unique strengths and applications of both methodologies. Deep learning, with its multi-layered neural networks, excels in handling large, unstructured datasets, making significant strides in image and speech recognition, natural language processing, and complex pattern recognition tasks. Conversely, traditional machine learning models, which rely on feature extraction and simpler algorithms, remain highly effective in structured data scenarios such as classification, regression, and clustering problems. The survey elucidates the criteria for choosing between DL and ML, focusing on factors like data size, computational resources, and specific application requirements. Furthermore, it discusses the evolving landscape of hybrid models that integrate DL and ML techniques to leverage the strengths of both approaches. This analysis provides valuable insights for researchers and practitioners aiming to deploy the most suitable AI models for their specific needs, emphasizing the importance of contextual understanding in the rapidly advancing field of artificial intelligence.

Cancer and Artificial Intelligence

Cancer is a multifactorial group of diseases that are known to affect human life with incidence and mortality rates. Artificial Intelligence and the development of new strategies in cancer treatment are of great importance in helping physicians apply optimized treatment tailored to the patient, overcoming both physical and psychological difficulties, and preventing the recurrence and spread of the disease. Thanks to the field of health and Artificial Intelligence, which are integrated with current developments in coordination with each other, great advances have been made and continue to be made in the diagnosis, treatment and prognosis of cancer, one of the major problems of our age.

Assessing the assessments: toward a multidimensional approach to AI literacy

This scoping review explores the field of artificial intelligence (AI) literacy, focusing on the tools available for evaluating individuals’ self-perception of their AI literacy. In an era where AI technologies increasingly infiltrate various aspect of daily life, from healthcare diagnostics to personalized digital platforms, the need for a comprehensive understanding of AI literacy has never been more critical. This literacy extends beyond mere technical competence to include ethical considerations, critical thinking, and socio-emotional skills, reflecting the complex interplay between AI technologies and societal norms. The review synthesizes findings from diverse studies, highlighting the development and validation processes of several key instruments designed to measure AI literacy across different dimensions. These tools – ranging from the Artificial Intelligence Literacy Questionnaire (AILQ) to the General Attitudes towards Artificial Intelligence Scale (GAAIS) – embody the nature of AI literacy, encompassing affective, behavioral, cognitive, and ethical components. Each instrument offers unique insights into how individuals perceive their abilities to understand, engage with, and ethically apply AI technologies. By examining these assessment tools, the review sheds light on the current landscape of AI literacy measurement, underscoring the importance of self-perception in educational strategies, personal growth, and ethical decision-making. The findings suggest a critical need for educational interventions and policy formulations that address the gaps between perceived and actual AI literacy, promoting a more inclusive, critically aware, and competent engagement with AI technologies.

Process models: A cornerstone to composites 4.0

Simulation methods for composites manufacturing processes have existed for many years and more recently, with the acceleration of digital technologies, there is increased interest in them. However, industrial uptake and the routine use of these models in the design and manufacture of composite parts has not yet occurred at the same scale as simulation of mechanical performance. This Viewpoint/Perspective article presents and discusses the current state-of-the-art in composites manufacturing simulation and the barriers to their wider adoption, such as large material and process variabilities and the lack of fast numerical models. We also share some personal thoughts on future research directions and how recent academic developments in the field, aiming at increasing model efficiency, leveraging new more efficient hardware (e.g. GPUs) and recent developments in the field of Artificial Intelligence could allow process simulation to become one of the cornerstones of a Composites 4.0 strategy. At the end, the reader should be more familiar with the current state of the art in composites process simulations and have gained an understanding of avenues for progress, in particular their use within a composites 4.0 framework.