Field Of Intelligence Research Articles

Exploring explainable AI features in the vocal biomarkers of lung disease

This review delves into the burgeoning field of explainable artificial intelligence (XAI) in the detection and analysis of lung diseases through vocal biomarkers. Lung diseases, often elusive in their early stages, pose a significant public health challenge. Recent advancements in AI have ushered in innovative methods for early detection, yet the black-box nature of many AI models limits their clinical applicability. XAI emerges as a pivotal tool, enhancing transparency and interpretability in AI-driven diagnostics. This review synthesizes current research on the application of XAI in analyzing vocal biomarkers for lung diseases, highlighting how these techniques elucidate the connections between specific vocal features and lung pathology. We critically examine the methodologies employed, the types of lung diseases studied, and the performance of various XAI models. The potential for XAI to aid in early detection, monitor disease progression, and personalize treatment strategies in pulmonary medicine is emphasized. Furthermore, this review identifies current challenges, including data heterogeneity and model generalizability, and proposes future directions for research. By offering a comprehensive analysis of explainable AI features in the context of lung disease detection, this review aims to bridge the gap between advanced computational approaches and clinical practice, paving the way for more transparent, reliable, and effective diagnostic tools.

India's Use of Artificial Intelligence in Healthcare

By completing tasks that would normally be completed by people in a fraction of the time and at a fraction of the expense, artificial intelligence makes life easier for patients, physicians, and hospital managers. AI is used in healthcare in a variety of ways, including the discovery of new genetic connections, the operation of robots that assist during surgery, the automation of administrative processes, the customization of treatment plans, and much more. India has witnessed an exponential rise in the field of artificial intelligence (AI). A revolution in cost reduction, efficiency, quality, and accessibility of healthcare services for millions of users is anticipated with the integration of artificial intelligence into the healthcare system. The focus of the majority of AI-based proposals in India, according to the literature currently accessible on AI and healthcare systems, has been to provide AI-based medical services to underserved remote rural communities where people cannot afford high-quality medical facilities.

Generative Artificial Intelligence: Analyzing Its Future Applications in Additive Manufacturing

New developments in the field of artificial intelligence (AI) are increasingly finding their way into industrial areas such as additive manufacturing (AM). Generative AI (GAI) applications in particular offer interesting possibilities here, for example, to generate texts, images or computer codes with the help of algorithms and to integrate these as useful supports in various AM processes. This paper examines the opportunities that GAI offers specifically for additive manufacturing. There are currently relatively few publications that deal with the topic of GAI in AM. Much of the information has only been published in preprints. There, the focus has been on algorithms for Natural Language Processing (NLP), Large Language Models (LLMs) and generative adversarial networks (GANs). This summarised presentation of the state of the art of GAI in AM is new and the link to specific use cases is this first comprehensive case study on GAI in AM processes. Building on this, three specific use cases are then developed in which generative AI tools are used to optimise AM processes. Finally, a Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis is carried out on the general possibilities of GAI, which forms the basis for an in-depth discussion on the sensible use of GAI tools in AM. The key findings of this work are that GAI can be integrated into AM processes as a useful support, making these processes faster and more creative, as well as to make the process information digitally recordable and usable. This current and future potential, as well as the technical implementation of GAI into AM, is also presented and explained visually. It is also shown where the use of generative AI tools can be useful and where current or future potential risks may arise.

Reinforcement learning from suboptimal demonstrations based on Reward Relabeling

Deep reinforcement learning (DRL) has achieved remarkable milestones in the field of artificial intelligence. However, the reward functions for most real-world tasks are characterized by delays and sparsity, posing significant challenges for DRL methods. To tackle the issues of delayed and sparse rewards, there have been many approaches based on the prior knowledge of expert trajectories proposed, such as GAIL and its variants. However, if only suboptimal demonstrations available, they usually struggle to overcome the performance disadvantage due to the complexity and fragility of adversarial training. To address these problems, this paper introduces a novel framework combining Self-Imitation learning with Reward Relabeling based Reinforcement learning, thus dubbed SIR3. It is capable of accelerating online learning using suboptimal demonstrations in environments even with extremely sparse rewards and meanwhile encouraging exploration of better policies. SIR3 devises a task-independent reward relabeling mechanism to generate reward signals for both the expert examples and online experience. This design provides the agent with more informative guidance, even when the number of suboptimal demonstrations is minimal. During the training process, the integration of imitation learning and RL losses enables the agent to dynamically mimic rewarding trajectories, possibly collected from experts or self-explored. Experimental findings on widely recognized MuJoCo benchmarks reveal that SIR3 can efficiently learn excellent policies surpassing suboptimal demonstrations, achieving superior training efficiency and performance relative to SOTA methods. Notably, in some environments, it secures a performance edge over an order of magnitude.

Artificial Bee Colony Algorithm to Optimize the Safety Distance of Workers in Construction Projects

This paper presents the results of a simulation model regarding the productivity and safety working space for construction workers through the floors of a building using swarm intelligence (SI), a field of artificial intelligence (AI), and specifically using artificial bee colony (ABC) optimization. After designing the algorithm used to build the simulation model, the simulation was used in an actual building project by comparing the travel times of workers conventionally transporting material with another group working on routes optimized by the algorithm. Thus, the proposed algorithm provides routes combining shorter travel times and correct distances between workers when transporting materials in a construction site, handling the interference between crews. After validating the algorithm on-site, no statistically significant differences were found between the travel times of workers and the times delivered by the algorithm. Additionally, the travel times using the routes obtained through the algorithm were significantly lower than those made by workers who moved freely without a predefined route. In summary, the algorithm proposed may help construction practitioners maintain safe movements that respond to hazard contexts imposed by any restriction that demands a safety distance.

Modelling constructivist language acquisition through syntactico-semantic pattern finding.

The constructivist acquisition of language by children has been elaborately documented by researchers in psycholinguistics and cognitive science. However, despite the centrality of human-like communication in the field of artificial intelligence, no faithful computational operationalizations of the mechanisms through which children learn language exist to date. In this article, we fill part of this void by introducing a mechanistic model of the constructivist acquisition of language through syntactico-semantic pattern finding. Concretely, we present a methodology for learning grammars based on similarities and differences in the form and meaning of linguistic observations alone. The resulting grammars consist of form-meaning mappings of variable extent and degree of abstraction, called constructions, which facilitate both language comprehension and production. Applying our methodology to the CLEVR benchmark dataset, we provide a proof of concept that demonstrates the online, incremental, data-efficient, transparent and effective learning of item-based construction grammars from utterance-meaning pairs.

Lightweight, robust and conductive MXene/SiO2 nanofiber aerogels for excellent sensing and thermal management properties

Flexible pressure sensors embody outstanding mechanical and sensing performance, particularly in the fields of personal health monitoring and artificial intelligence, while catering to multifunctional applications resilient against harsh weather conditions. Among these, Ti3C2Tx MXene-based aerogel materials with high surface area and excellent conductivity have attracted a lot of attention. However, there are big challenges in improving the mechanical properties at low densities and designing multi-level pore structures. Herein, the prepared silane monomers (MP) are firstly synthesized by 3-(Trimethoxysilyl) propyl methacrylate (MPTES) and polyethylene glycol diacrylate (PEGDA), then polydopamine-modified silicon dioxide (PDA-SiO2) nanofibers employed with MXene nanosheets form covalent cross-linking nanofiber aerogels. The multi-level stable structure improves both mechanical strength and elasticity. The assembled MXene/MP/PDA-SiO2 pressure sensors exhibit a high sensitivity of −0.83 kPa−1, a rapid response time of 48 ms, a minimum 1 % strain detection limit, and reliable stability over 10,000 cycles. Furthermore, these sensors can be used for human motion detection and health diagnostics, and show good personal thermal management performance. This work holds a promising candidate for the next generation of multifunctional flexible electronic devices.

Lightweight Deep Learning: An Overview

With the recent success of the deep neural networks (DNNs) in the field of artificial intelligence, the urge of deploying DNNs has drawn tremendous attention because it can benefit a wide range of applications on edge or embedded devices. Lightweight deep learning (DL) indicates the procedures of compressing DNN models into more compact ones, which are suitable to be executed on edge devices due to their limited resources and computational capabilities while maintaining comparable performance as the original. Currently, the approaches of model compression include but not limited to network pruning, quantization, knowledge distillation, neural architecture search. In this work, we present a fresh overview to summarize recent development and challenges for model compression.

Rise of the Machines: Exploring the Emergence of Machine Consciousness

Inquiry into the field of artificial intelligence (machines) and its potential to develop consciousness is presented in this study. This investigation explores the complex issues surrounding machine consciousness at the nexus of AI, neuroscience, and philosophy as we delve into the fascinating world of artificial intelligence (AI) and investigate the intriguing question: are machines on the verge of becoming conscious beings? The study considers the likelihood of machines displaying self-awareness and the implications thereof through an analysis of the current state of AI and its limitations. However, with advancements in machine learning and cognitive computing, AI systems have made significant strides in emulating human-like behavior and decision-making. Furthermore, the emergence of machine consciousness raises questions about the blending of human and artificial intelligence, and ethical considerations are also considered. The study provides a glimpse into a multidisciplinary investigation that questions accepted theories of consciousness, tests the limits of what is possible with technology, and do these advancements signify a potential breakthrough in machine consciousness.

Multi-Task AI-driven undetermined blind source identification for modal identification of large scale structures

The effective management of aging infrastructure represents a critical concern globally, exerting profound socio-economic influences and occupying a prominent position on the political agenda. The escalating occurrences of catastrophic collapses in recent years underscore the pressing requirement for efficient Structural Health Monitoring (SHM) strategies. These strategies play a pivotal role in aiding decision-making processes related to prioritizing interventions and rehabilitation efforts. Among SHM systems, those utilizing Operational Modal Analysis (OMA) through vibration-based techniques have gained popularity due to their non-destructive nature, comprehensive damage assessment capabilities, and relative ease of automation. Despite their advantages, current OMA methods encounter significant scalability challenges, primarily attributable to extensive computational demands and the necessity for substantial expert involvement. In this context, recent advancements in the field of artificial intelligence (AI) present a promising solution to address these scalability issues, opening the door for the development of next-generation SHM systems. This study introduces an innovative Multitask Learning Deep Neural Network (MTL-DNN) model designed for rapid and automated blind source modal identification of structures. Integrating the concepts of undetermined second-order blind source identification (SOBI) into the architecture of the network, the proposed model can reveal the independent modal components present in the raw response acceleration data with minimum computational burden. The presented results demonstrate the quasi-instantaneous modal identification capabilities of the proposed architecture, enabling the estimation of more components than the number of sensors.

قابلية التحكيم كوسيلة بديلة لفض المنازعات لاستيعاب النظم الذكية:

Arbitration occupies an essential position as an alternative means of resolving disputes between individuals. It is a self-standing mechanism, under which the parties benefit from a set of advantages, such as the advantage of resolving their conflicts confidentially and within limited deadlines compared to the lengthy procedures of the ordinary judiciary. However, the emergence of advanced technology such as artificial intelligence techniques has begun to raise many questions about the possibility of benefiting from them in the future in alternative means of dispute resolution, such as arbitration, especially since the field of artificial intelligence is a highly developed field continuously, which raises a significant number of questions, all of which revolve around ways to benefit Arbitration of these types of advanced technology. Therefore, the study mainly aims to predict the future of arbitration in light of continuous technological development, especially with regard to artificial intelligence techniques. The subject was dealt with according to the coupling between the descriptive method, the analytical method, and the comparative method. The concept of artificial intelligence has also been clarified. Three hypotheses have been put forward that embody the extent of arbitration as an alternative means of dispute resolution to accommodate the artificial intelligence system in a first topic, with an evaluation of this system in arbitration and some applications that devote the tendency to resolve disputes amicably using artificial intelligence techniques in a second topic. The researchers concluded that the position of artificial intelligence in arbitration will inevitably appear clearly in the near future, so it should be dealt with special care so that only the strengths of artificial intelligence can be benefited from without compromising the basic values and principles of arbitration.

The Rise of Digital Twins in Healthcare: A Mapping of the Research Landscape.

The place of digital twin technology in any healthcare system is a truly disruptive innovation that has profound consequences all across medical research and practice. Digital twins represent the virtual replicas that correspond to some physical entity by pulling real-time data streams from different sources to model biological systems for health monitoring and personalization of treatment strategies. This paper presents a detailed review of the current research landscape into digital twins for healthcare. Through bibliometric analysis, we obtained 1,663 publications from 2012 to 2024, basically sourced from the Scopus database, establishing a portion of the trends, productive authors, influential sources, and collaboration networks in this fast-evolving field. Descriptively, our results indicate that although research into this area started way back, the bulk of research began to be realized from 2018 onwards, with appreciable contributions coming in from interdisciplinary fields of artificial intelligence, machine learning, and data analytics. Even with challenges to data interoperability and other privacy concerns, this change brought on by digital twin technology is undoubtedly a considerable promise for chronic disease management, predictive analytics, drug discovery, and surgical planning. This work brings immense insight into this new domain of digital twins in health, which shall set up a strong foundation for future research and innovation in this area.

Artificial Intelligence on Food Vulnerability: Future Implications within a Framework of Opportunities and Challenges

This study explores the field of artificial intelligence (AI) through the lens of Stephen Hawking, who warned of its potential dangers. It aims to provide a comprehensive understanding of AI and its implications for food security using a qualitative approach and offering a contemporary perspective on the topic. The study explores the challenges and opportunities presented by AI in various fields with an emphasis on the global food reality. It also highlights the critical importance of striking a harmonious balance between technological progress and the preservation of local wisdom, cultural diversity, and environmental sustainability. In conclusion, the analysis argues that AI is a transformative force with the potential to address global food shortages and facilitate sustainable food production. However, it is not without significant risks that require rigorous scrutiny and ethical oversight.

Man and society – through the prism of artificial intelligence

Prospects for the introduction of artificial intelligence technologies are discussed through the prism of human and societal imperfection. It is emphasized that artificial intelligence should be considered an essential reason for a kind of full-fledged self-audit by civilization and personality. The reasons for the pessimistic attitude to the possibilities of qualitative social transformations are evaluated, despite the vivid developments in the field of artificial intelligence. Fears of artificial intelligence reveal absurdity of many “basic” mechanisms of society’s existence up to now, including the standard ideas about prime importance of mercantile values. It is stated that there is a constant slip in the use of all without exception overtechnologies with maximum public benefit. Attention is focused on the expediency to implement a social revolution in its personally transformative dimension. At the same time it is noted that human improvement projects are almost unambiguously met with anxiety, distrust and reasonable skepticism.

RICD: Russian Intensive Care Dataset

In the era of healthcare digital transformation, the scientific community faces the need for structured and available datasets for research and technological projects in the field of artificial intelligence, related to the development of new diagnostic and treatment methods.Objective: to develop a dataset containing anonymized medical data of all patients treated at the Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology (FRCCR), and provide access for doctors and scientists of FRCCR and other centers to structured patient data for subsequent analysis and research. Materials and Methods. The FRCCR medical information system and the tools «Asclepius», PL/SQL, Microsoft Office Excel, Power Query M, Microsoft PowerBI, Open data editor, and Python were used for data collection and representation. To provide open access to the dataset and protect the personal data of patients, the information was anonymized.Results. We introduce the RICD (Russian Intensive Care Dataset, https://fnkcrr-database.ru/) — the first dataset of intensive care patients in the Russian Federation, developed at FRCCR based on advanced principles and methods used in international open database projects — «eICU Program» from Philips Healthcare, «MIMIC-IV», and «MIMIC-III». The developed dataset contains information on 7,730 hospitalizations of 5,115 patients (including readmissions), covering data from 3,291 hospitalizations in the intensive care units (ICUs). The total number of records in the RICD exceeds 14 million. The RICD presents medical-anthropometric data, patient movement within the institution, diagnoses, information on therapy provided, results of laboratory tests, scale assessments, and outcomes of hospitalization. RICD also contains data on several vital parameters collected from bedside monitors and other equipment of ICUs, with up to 10 evaluations per hour.Conclusion. The RICD allows for in-depth analysis and research of clinical practices in intensive care, enabling the development of clinical decision support tools and the application of machine learning methods to enhance diagnostic tools and improve patient outcomes. With its accessibility and detailed data structure, the dataset serves as a valuable tool for both scientific research and practical applications in intensive care.

Literature Study: Transfer Learning for COVID-19 Disease Analysis Based on Chest X-ray Dataset

The urgency of the impact of the COVID-19 disease that attacks people around the world encourages special research, especially in the field of artificial intelligence. This study aims to conduct a literature study related to the use of artificial intelligence, especially transfer learning in analyzing COVID-19 disease based on chest X-ray datasets. The research method of this research adapts the Preferred Reporting for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The results of the analysis of this data to answer research questions regarding the transfer learning model for the analysis of COVID-19 disease based on the chest X-ray dataset, it is known that the models used are MobileNet, Inception, VGG and ResNet. MobileNetV2 can be optimized by adding a global average pooling layer, dropout layer and dense layer and get an accuracy of 98.65%. InceptionV3 can be combined with Xception and get 98.8% accuracy. VGG-16 can be combined with ResNet-50 Xception and get 98.93% accuracy. ResNet-50 can be optimized by adding a dropout layer and a dense layer and getting an accuracy of 97.65%.

Tongue Disease Prediction Based on Machine Learning Algorithms

The diagnosis of tongue disease is based on the observation of various tongue characteristics, including color, shape, texture, and moisture, which indicate the patient’s health status. Tongue color is one such characteristic that plays a vital function in identifying diseases and the levels of progression of the ailment. With the development of computer vision systems, especially in the field of artificial intelligence, there has been important progress in acquiring, processing, and classifying tongue images. This study proposes a new imaging system to analyze and extract tongue color features at different color saturations and under different light conditions from five color space models (RGB, YcbCr, HSV, LAB, and YIQ). The proposed imaging system trained 5260 images classified with seven classes (red, yellow, green, blue, gray, white, and pink) using six machine learning algorithms, namely, the naïve Bayes (NB), support vector machine (SVM), k-nearest neighbors (KNN), decision trees (DTs), random forest (RF), and Extreme Gradient Boost (XGBoost) methods, to predict tongue color under any lighting conditions. The obtained results from the machine learning algorithms illustrated that XGBoost had the highest accuracy at 98.71%, while the NB algorithm had the lowest accuracy, with 91.43%. Based on these obtained results, the XGBoost algorithm was chosen as the classifier of the proposed imaging system and linked with a graphical user interface to predict tongue color and its related diseases in real time. Thus, this proposed imaging system opens the door for expanded tongue diagnosis within future point-of-care health systems.

The Important Role Of Artificial Intelligence Technology Regulation In Protecting The Public Interest

Artificial intelligence provides both good and bad. For this reason, artificial intelligence must be regulated to protect the public interest. The results of the study show that regulating artificial intelligence is not easy, very complicated, there are many challenges, moreover the development of artificial intelligence technology is very rapid while the law is slow to anticipate it. In 2022, globally there have been 37 regulations regulating artificial intelligence. From the results of the comparison of various best practices and regulations of other countries that are at the forefront in the field of artificial intelligence, such as the European Union, China, and the United States framework approach, it can be an input for the development of artificial intelligence regulations in Indonesia that ensure the responsible use of artificial intelligence, respect human values, and do not hinder the creation of an artificial intelligence development ecosystem.

An adaptive multi-neural network model for named entity recognition of Chinese mechanical equipment corpus

Mining entities from open Chinese mechanical equipment texts have become prevailing in the intelligent manufacturing field. However, compared to named entities in other domains, it is very hard to determine entity boundaries in mining Chinese mechanical equipment texts because there is no unified standard for entity simplification, and digits and units are mixed in the text. To address the issue, this paper presents an entity boundary-define strategy and constructs a mechanical equipment-oriented corpus called MECorpus using open Chinese mechanical equipment texts by combining domain knowledge. A multi-neural network collaboration model Adaptive-BERT-BiLSTM-CRF-Rating (ABBCR) is then proposed for mechanical equipment named entity recognition. The novelty of ABBCR is characterised by its adaptive input mechanism and rating score ability for identified entities. Various experiments about ABBCR model selection, evaluation and application are conducted on MECorpus. Experimental results show that the ABBCR model provides high-quality mechanical equipment entities for constructing the mechanical equipment knowledge graph. ABBCR combined with the large language model has proved to be a promising method to manage complex mechanical equipment expertise.

Balancing Biosafety and Innovation: Navigating Legislative Landscapes in AI-driven Biohacking

Abstract Artificial intelligence and Biohacking prominent fields of emerging technology. Nonetheless, both fields are minimally regulated. Regulating policy is necessary to ensure that AI-Driven Biohacking can safely innovate.