Artificial Intelligence Research Articles

Does artificial intelligence improve enterprise carbon emission performance? Evidence from an intelligent transformation policy in China

In the pursuit of climate change mitigation and carbon neutrality, climate policy uncertainty (CPU) poses a threat to enterprises' green, low-carbon, and sustainable development. The intelligent transformation of enterprises is a crucial strategy for addressing climate risks and enhancing energy efficiency. However, there is a lack of research on how intelligent transformation impacts low-carbon development from the perspective of micro-enterprises. Based on this gap, we analyze data from Shanghai and Shenzhen A-share listed manufacturing enterprises from 2010 to 2022 to empirically test the impact of intelligent manufacturing (IM) on enterprise carbon emission performance (ECEP) using a difference-in-differences model. We also explore the moderating effect of IM on the relationship between CPU and ECEP. Our findings indicate that IM significantly enhances ECEP. IM boosts the ECEP of enterprises in the eastern region, state-owned enterprises, and capital- and technology-intensive sectors. Green technological innovation, human capital, and organizational resilience are key mechanisms through which IM enhances ECEP. Further analysis reveals that CPU significantly inhibits ECEP, whereas IM positively moderates the impact of CPU. In the context of external environmental uncertainty, this study offers crucial insights into how intelligent technology can strengthen the real economy and facilitate the low-carbon transformation of manufacturing enterprises.

A collaborative-learning multi-agent reinforcement learning method for distributed hybrid flow shop scheduling problem

As the increasing level of implementation of artificial intelligence technology in solving complex engineering optimization problems, various learning mechanisms, including deep learning (DL) and reinforcement learning (RL), have been developed for manufacturing scheduling. In this paper, a collaborative-learning multi-agent RL method (CL-MARL) is proposed for solving distributed hybrid flow-shop scheduling problem (DHFSP), minimizing both makespan and total energy consumption. First, the DHFSP is formulated as the Markov decision process, the features of machines and jobs are represented as state and observation matrixes according to their characteristics, the candidate operation set is used as action space, and a reward mechanism is designed based on the machine utilization. Next, a set of critic networks and actor networks, consist of recurrent neural networks and fully connected networks, are employed to map the states and observations into the output values. Then, a novel distance matching strategy is designed for each agent to select the most appropriate action at each scheduling step. Finally, the proposed CL-MARL model is trained through multi-agent deep deterministic policy gradient algorithm in collaborative-learning manner. The numerical results prove the effectiveness of the proposed multi-agent system, and the comparisons with existing algorithms demonstrate the high-potential of CL-MARL in solving DHFSP.

Rapid detection and interpretation of heart murmurs using phonocardiograms, transfer learning and explainable artificial intelligence.

Cardiovascular disease, which remains one of the main causes of death, can be prevented by early diagnosis of heart sounds. Certain noisy signals, known as murmurs, may be present in heart sounds. On auscultation, the degree of murmur is closely related to the patient's clinical condition. Computer-aided decision-making systems can help doctors to detect murmurs and make faster decisions. The Mel spectrograms were generated from raw phonocardiograms and then presented to the OpenL3 network for transfer learning. In this way, the signals were classified to predict the presence or absence of murmurs and their level of severity. Pitch level (healthy, low, medium, high) and Levine scale (healthy, soft, loud) were used. The results obtained without prior segmentation are very impressive. The model used was then interpreted using an Explainable Artificial Intelligence (XAI) method, Occlusion Sensitivity. This approach shows that XAI methods are necessary to know the features used internally by the artificial neural network then to explain the automatic decision taken by the model. The averaged image of the occlusion sensitivity maps can give us either an overview or a precise detail per pixel of the features used. In the field of healthcare, particularly cardiology, for rapid diagnostic and preventive purposes, this work could provide more detail on the important features of the phonocardiogram.

How Might Artificial Intelligence Influence Human Evolution?

How Might Artificial Intelligence Influence Human Evolution?

IMFL-AIGC: Incentive Mechanism Design for Federated Learning Empowered by Artificial Intelligence Generated Content

IMFL-AIGC: Incentive Mechanism Design for Federated Learning Empowered by Artificial Intelligence Generated Content

The impact of blockchain and artificial intelligence technologies in network security for e-voting

The impact of blockchain and artificial intelligence technologies in network security for e-voting

Overcoming Low Adherence to Chronic Medications by Improving their Effectiveness using a Personalized Second-generation Digital System.

Low adherence to chronic treatment regimens is a significant barrier to improving clinical outcomes in patients with chronic diseases. Low adherence is a result of multiple factors. We review the relevant studies on the prevalence of low adherence and present some potential solutions. This review presents studies on the current measures taken to overcome low adherence, indicating a need for better methods to deal with this problem. The use of first-generation digital systems to improve adherence is mainly based on reminding patients to take their medications, which is one of the reasons they fail to provide a solution for many patients. The establishment of a second-generation artificial intelligence system, which aims to improve the effectiveness of chronic drugs, is described. Improving clinically meaningful outcome measures and disease parameters may increase adherence and improve patients' response to therapy.

Emerging multifaceted application of artificial intelligence in chest radiography: a narrative review

Emerging multifaceted application of artificial intelligence in chest radiography: a narrative review

Artificial intelligence detection of refractive eye diseases using certainty factor and image processing

Refractive errors are defined as an impairment in the eye’s capacity to focus light, resulting in the formation of blurred or unfocused images. These issues arise from alterations in the shape of the cornea, the length of the eyeball, or the aging of the crystalline lens. It is anticipated that the prevalence of visual impairment will increase in conjunction with global population growth. At present, a significant number of countries have not yet accorded sufficient priority to eye health within their healthcare systems. This has resulted in insufficient awareness and reluctance to seek costly specialized care. This study proposes the development of an advanced refractive eye disease detection system with the objective of improving diagnostic accuracy, disseminating disease information, and reducing financial barriers to specialist consultation. The research employs certainty factor (CF) methods and image processing with feature extraction. The initial results demonstrate the potential for identifying specific refractive eye diseases with high certainty through the analysis of symptoms and the examination of photographs of the eye. The proposed approach provides an alternative method for diagnosing refractive eye diseases, which could enhance access to refractive eye care services and reduce the economic burden on patients.

Applications of artificial intelligence for membrane separation: A review

Applications of artificial intelligence for membrane separation: A review

Eighth IEEE IoT Vertical and Topical Summit at RWW 2025—“Impact of Artificial Intelligence on Wireless IoT Systems: From Design to Application” [IEEE Radio & Wireless Week 2025

Eighth IEEE IoT Vertical and Topical Summit at RWW 2025—“Impact of Artificial Intelligence on Wireless IoT Systems: From Design to Application” [IEEE Radio & Wireless Week 2025

A machine learning based framework for breast cancer prediction using biomarkers

<p>Breast cancer is the most frequent cancer in women and the second-leading cause of cancer-related deaths globally. The main problems in managing breast cancer are high heterogeneity and the formation of therapeutic resistance. White blood cells, omics and large Wisconsin diagnostic breast cancer datasets present the three-decade genomic revolution and advance the understanding of cellular function. The precision of cancer diagnosis has also increased over the past decades. High throughput sequencing, screening, and artificial intelligence technologies have significantly improved and increased the methodologies used for diagnosis, prognosis, and therapy. This paper follows several phases of breast cancer, studies datasets and evaluate many algorithms of machine learning (ML) used for analysis and feature selection i.e. k-means, similarity correlation, genetic algorithm, and principal component analysis, have been used to recognize the subset of proteins with the highest significance for breast cancer prediction by using different biomarkers. The best correlation, as determined by Pearson correlation, between copy number and protein is 0.014, and the accuracy achieved by the genetic algorithm is 93.5% using multi-omics datasets.</p>

Concordance of clinician, Chat-GPT4, and ORAD diagnoses against histopathology in Odontogenic Keratocysts and tumours: a 15-Year New Zealand retrospective study.

This research aimed to investigate the concordance between clinical impressions and histopathologic diagnoses made by clinicians and artificial intelligence tools for odontogenic keratocyst (OKC) and Odontogenic tumours (OT) in a New Zealand population from 2008 to 2023. Histopathological records from the Oral Pathology Centre, University of Otago (2008-2023) were examined to identify OKCs and OT. Specimen referral details, histopathologic reports, and clinician differential diagnoses, as well as those provided by ORAD and Chat-GPT4, were documented. Data were analyzed using SPSS, and concordance between provisional and histopathologic diagnoses was ascertained. Of the 34,225 biopsies, 302 and 321 samples were identified as OTs and OKCs. Concordance rates were 43.2% for clinicians, 45.6% for ORAD, and 41.4% for Chat-GPT4. Corresponding Kappa value against histological diagnosis were 0.23, 0.13 and 0.14. Surgeons achieved a higher concordance rate (47.7%) compared to non-surgeons (29.82%). Odds ratio of having concordant diagnosis using Chat-GPT4 and ORAD were between 1.4 and 2.8 (p < 0.05). ROC-AUC and PR-AUC were similar between the groups (Clinician 0.62/0.42, ORAD 0.58/0.28, Char-GPT4 0.63/0.37) for ameloblastoma and for OKC (Clinician 0.64/0.78, ORAD 0.66/0.77, Char-GPT4 0.60/0.71). Clinicians with surgical training achieved higher concordance rate when it comes to OT and OKC. Chat-GPT4 and Bayesian approach (ORAD) have shown potential in enhancing diagnostic capabilities.

An AI model for predicting the spatiotemporal evolution process of coastal waves by using the Improved-STID algorithm

Accurate and rapid prediction of water wave evolution is crucial for ensuring the safe and healthy operation of ocean and coastal engineering. Using hourly wave data from the coastal areas of the Hawaiian Islands from 2019 to 2021, this study developed an artificial intelligence model for rapidly predicting the spatiotemporal evolution of coastal waves by improving the STID algorithm. In the study, the SWAN wave model was used to generate an initial training dataset for driving Improved-STID algorithm, where the Improved-STID algorithm and SWAN model share the same unstructured computational grid with good adaptability to complex topography. The proposed Improved-STID algorithm takes into account the overall spatiotemporal correlations of wave evolution across different regions. The results show that on the basis of prediction efficiency on a par with the original STID, the Improved-STID can be more accurately applied to the spatiotemporal evolution process of coastal waves. The prediction errors in this study are: the RMSE of 0.23 m, MAE of 0.14 m, MAPE of 17.96 %, and the calculation time of the proposed AI model is only about 1.8 % of that of the SWAN wave model.

Application of artificial intelligence in the field of breast pathology diagnosis: narrative review

Application of artificial intelligence in the field of breast pathology diagnosis: narrative review

Asistencia de la inteligencia artificial generativa como herramienta pedagógica en la educación superior

This study examines the implementation of generative artificial intelligence (GAI) tools in higher education, focusing on the University of Guayaquil. The objective was to identify the most commonly used tools by faculty and assess the benefits and challenges of their adoption. The methodology included a literature review and surveys administered to 334 faculty members across various departments, using a structured questionnaire covering demographic aspects, knowledge, usage, and perceptions of GAI integration in teaching. The analysis revealed that ChatGPT is the most widely used tool, although its adoption varies significantly among faculties. Despite advantages such as personalized materials and improved educational planning, key challenges were identified, including resistance to change and inadequate training. The study concludes that the integration of generative AI has great potential to enhance education but requires strategic planning, ongoing faculty training, and clear guidelines to ensure its ethical and pedagogical use.

Evolution of the concept of device moving from internet of things to artificial intelligence of things

Evolution of the concept of device moving from internet of things to artificial intelligence of things

Long-term deep reinforcement learning for real-time economic generation control of cloud energy storage systems with varying structures

Energy storage systems play a crucial role in modern power systems. Consequently, a mixed cloud energy storage (CES) system is proposed. The mixed CES system comprises consumers and prosumers. The consumers can only consume energy. The prosumers can either produce or consume energy at different time intervals. The proposed mixed CES system is designed for investigating the generation control challenges in mixed interconnected power systems. To optimize the active power balance and economic efficiency of the mixed CES system, a long-term deep reinforcement learning (LDRL) artificial intelligence approach is proposed as the real-time economic generation controller to control the mixed CES system. The LDRL consists of a reinforcement mechanism and two models: a long short-term memory model for economic dispatch and a deep neural networks model for smart generation control. The reinforcement framework updates policies for the prosumers. The efficacy of proposed method is validated across three mixed systems, i.e., the improved IEEE 300-bus, Polish 2383-bus, and mixed systems with varying structures. The numerical simulations verify that the LDRL method can efficiently and economically control the mixed CES systems with diverse structures.

Artificial Intelligence Enabled Future Wireless Electric Vehicles with Multi-Model Learning and Decision Making Models

Artificial Intelligence Enabled Future Wireless Electric Vehicles with Multi-Model Learning and Decision Making Models

A novel incremental ensemble learning for real-time explainable forecasting of electricity price

The development of a stable, safe, secure and sustainable energy future is a challenge for all countries these days. In terms of electricity price, its volatile nature makes its prediction a complex task. A precise real-time forecast of the electricity price can have significant consequences for the economy and risks faced. This work presents a new ensemble learning algorithm for making real-time predictions of electricity price in Spain. It combines long and short-term behavior patterns following an online incremental learning approach, keeping the model always up to date. The detection of novelties and unexpected behaviors in the time series streams allows the algorithm to provide more accurate predictions than the reference machine learning algorithms with which it is compared. In addition, the proposed algorithm predicts in real-time and the predictions obtained are interpretable, thus contributing to the Explainable Artificial Intelligence.