Hasil untuk "artificial intelligence"

Semonti Banik, Sajal Chandra Banik, Sarker Safat Mahmud

ABSTRACT The essential factor in developing multi‐robot systems is the generation of an optimal path for task completion by multiple robots. To ensure effective path planning, this paper studies the recent publications and provides a detailed review of the path planning approaches to avoid collisions in uncertain environments. In this article, path‐planning approaches for multiple robots are categorized primarily into classical, heuristic, and artificial intelligence‐based methods. Among the heuristic approaches, bio‐inspired approaches are mostly employed to optimize the classical approaches to enhance their adaptability. The articles are analyzed based on static and dynamic scenarios, real‐time experiments, and simulations involving hybrid solutions. The increasing focus on using hybrid approaches in dynamic environments is found mostly in the papers employing heuristic and AI‐based approaches. In real‐time applications, AI‐based approaches are highly implemented in comparison to heuristic and classical approaches. Moreover, the findings from this review, highlighting the strengths and drawbacks of each algorithm, can help researchers select the appropriate approach to overcome the limitations in designing efficient multi‐robot systems.

Selin Erel, Aslıhan G. Kılıç

Background: Existing studies on anesthesiologists’ attitudes toward artificial intelligence (AI) leave a global understanding underexplored. This cross-sectional study aims to investigate Turkish anesthesiologists’ attitudes toward AI, examining its perceived benefits, limitations, and associated ethical concerns. Insights from this study aim to enhance understanding of AI’s role in anesthesiology within a cultural and ethical context. Methods: This nationwide study surveyed Turkish anesthesiologists. Descriptive statistics summarized categorical variables, Pearson’s Chi-square test compared variables between groups. Binary logistic regression analyzed associations between demographic factors and positive attitudes toward AI. Results: Among 293 valid responses, 69.6% of participants expressed positive attitudes toward AI. Gender (P = 0.01), employment setting (P < 0.001), and prior AI experience (P < 0.001) were significant predictors of positive attitudes. AI applications most frequently endorsed included preoperative assessments (93.1%), academic support (95.2%), and medical education (91.2%). Ethical concerns were prominent, with liability ambiguity (87.3%) and privacy issues (62.8%) being the most cited. Logistic regression revealed that participants aged 46–55 were significantly more likely to exhibit positive attitudes (OR = 3.744, P = 0.03), while those with over 15 years of experience were less likely to do so (OR = 0.105, P = 0.04). Conclusions: Turkish anesthesiologists exhibited predominantly positive attitudes toward AI, with prior experience playing a significant role in shaping perceptions. While AI was embraced for academic, educational, and noninvasive tasks, skepticism was present toward its application in invasive procedures. These findings highlight AI’s potential to enhance efficiency and patient safety while underscoring the need for comprehensive legal and ethical frameworks.

Xinyue Wang, Weifan Lin, Weiting Zhang et al.

In this paper, the Merkle-Transformer model is introduced as an innovative approach designed for financial data processing, which combines the data integrity verification mechanism of Merkle trees with the data processing capabilities of the Transformer model. A series of experiments on key tasks, such as financial behavior detection and stock price prediction, were conducted to validate the effectiveness of the model. The results demonstrate that the Merkle-Transformer significantly outperforms existing deep learning models (such as RoBERTa and BERT) across performance metrics, including precision, recall, accuracy, and F1 score. In particular, in the task of stock price prediction, the performance is notable, with nearly all evaluation metrics scoring above 0.9. Moreover, the performance of the model across various hardware platforms, as well as the security performance of the proposed method, were investigated. The Merkle-Transformer exhibits exceptional performance and robust data security even in resource-constrained environments across diverse hardware configurations. This research offers a new perspective, underscoring the importance of considering data security in financial data processing and confirming the superiority of integrating data verification mechanisms in deep learning models for handling financial data. The core contribution of this work is the first proposition and empirical demonstration of a financial data analysis model that fuses data integrity verification with efficient data processing, providing a novel solution for the fintech domain. It is believed that the widespread adoption and application of the Merkle-Transformer model will greatly advance innovation in the financial industry and lay a solid foundation for future research on secure financial data processing.

Abubakar Salihu, Muhammad Mukhtar Aliyu, Nur Fadillah Nurchalis

Artificial intelligence (AI) has a profound influence on various facets of modern-day society, notably within the realm of education. Its application in educational settings is extensive, primarily aimed at improving the methods of instruction and knowledge acquisition. Thus, this research investigates the impact of AI tools (ChatGPT, Google Bard) on the creative writing skills of Nigerian undergraduates using a pretest-posttest quasi-experimental research design. Eighty (80) third-year undergraduates participated in the study. Data were collected using pretest-posttest writing tasks. The writing scripts were graded using creative writing assessment rubrics. Paired sample t-test analysis was conducted to compare the pretest-posttest writing scores using SPSS. The results of the analysis showed a significant improvement in the participants’ overall writing scores after using the AI tools. The results also show significant improvement in all the aspects of creative writing: image, voice, characterization and story. Finally, the study makes some recommendations for practice and further studies.  

Rory Ward, Dan Bigioi, Shubhajit Basak et al.

While current research predominantly focuses on image-based colorization, the domain of video-based colorization remains relatively unexplored. Many existing video colorization techniques operate frame-by-frame, often overlooking the critical aspect of temporal coherence between successive frames. This approach can result in inconsistencies across frames, leading to undesirable effects like flickering or abrupt color transitions between frames. To address these challenges, we combine the generative capabilities of a fine-tuned latent diffusion model with an autoregressive conditioning mechanism to ensure temporal consistency in automatic speaker video colorization. We demonstrate strong improvements on established quality metrics compared to existing methods, namely, PSNR, SSIM, FID, FVD, NIQE and BRISQUE. Specifically, we achieve an 18% improvement in performance when FVD is employed as the evaluation metric. Furthermore, we performed a subjective study, where users preferred LatentColorization to the existing state-of-the-art DeOldify 80% of the time. Our dataset combines conventional datasets and videos from television/movies. A short demonstration of our results can be seen in some example videos available at <uri>https://youtu.be/vDbzsZdFuxM</uri>.

Jiale Liu, Xinlei Cai, Zijie Meng et al.

Abstract Potential evaluation to assist demand response decisions has garnered significant attention with the development of new power systems. However, existing data‐driven methods are challenging to properly exploit multivariate features and the process of response potential evaluation is unclear. Therefore, the authors propose an evaluation method that fuses expert features with multi‐image inputs and analyses the model evaluation process based on gradient. First, typical load profiles are extracted by the proposed procedure. Next, features derived from expert knowledge are calculated from the perspectives of adjustability, regularity, and sensitivity of electricity usage. Additionally, the typical load profile's recurrence plot, Markov leapfrog field, and Gramian angle field are created and incorporated into the colourful image as inputs. Then, the evaluation results are obtained by a two‐stream neural network fusing multivariate features. In the experiments, the proposed method is validated and discussed by comparing with many existing methods using London household users' data under the time‐of‐use price, providing new insights for demand response potential evaluation.

Kristina Spasić, Bojana Novićević Čečević, Ljilja Antić

The new industrial era has brought new opportunities and chances for the entire business development. Smart machines, artificial intelligence, cloud computing, the Internet of Things, big data are taking over many jobs and roles, thus leaving room for the development of new skills and abilities. The rapid technological development in terms of automation and digitization has made machines replace human work. In this sense, it is a matter of time when technology will replace traditional accountants. (Management) accountants who want to adapt and survive in the digital world have to improve their offer and change the focus from data calculation to interpretation of results and business management. Thus, by applying new digital information technology tools, management accounting can provide quality information for determining the costs of products and services, performance measurement, planning and control, strategic and operational decision-making and the like. The general objective of this paper is to review the potential impact of digital information technologies on the usefulness of cost accounting systems and organizational performance in healthcare institutions in the Republic of Serbia with the help of statistical analysis of the relationship between the selected variables. The results of the analysis show that digital technologies have a great impact on the usefulness of the cost accounting system. Also, the largest number of respondents pointed out that improved IT systems have a positive effect on increasing organizational performance.

Changui Lee, Seojeong Lee

The maritime sector is increasingly integrating Information and Communication Technology (ICT) and Artificial Intelligence (AI) technologies to enhance safety, environmental protection, and operational efficiency. With the introduction of the MASS Code by the International Maritime Organization (IMO), which regulates Maritime Autonomous Surface Ships (MASS), ensuring the safety of AI-integrated systems on these vessels has become critical. To achieve safe navigation, it is essential to identify potential risks during the system planning stage and design systems that can effectively address these risks. This paper proposes RA4MAIS (Risk Assessment for Maritime Artificial Intelligence Safety), a risk identification method specifically useful for developing AI-integrated maritime systems. RA4MAIS employs a systematic approach to uncover potential risks by considering internal system failures, human interactions, environmental conditions, AI-specific characteristics, and data quality issues. The method provides structured guidance to identify unknown risk situations and supports the development of safety requirements that guide system design and implementation. A case study on an Electronic Chart Display and Information System (ECDIS) with an AI-integrated collision avoidance function demonstrates the applicability of RA4MAIS, highlighting its effectiveness in identifying specific risks related to AI performance and reliability. The proposed method offers a foundational step towards enhancing the safety of software systems, contributing to the safe operation of autonomous ships.

Shalindu Malshan Jayawickrama, Piyumi Madhushani Ranaweera, Ratupaskatiye Gedara Gunaratnege Roshan Pradeep et al.

Abstract Background Precision healthcare has entered a new era because of the developments in personalized medicine, especially in the diagnosis and treatment of head and neck squamous cell carcinoma (HNSCC). This paper explores the dynamic landscape of personalized medicine as applied to HNSCC, encompassing both current developments and future prospects. Recent Findings The integration of personalized medicine strategies into HNSCC diagnosis is driven by the utilization of genetic data and biomarkers. Epigenetic biomarkers, which reflect modifications to DNA that can influence gene expression, have emerged as valuable indicators for early detection and risk assessment. Treatment approaches within the personalized medicine framework are equally promising. Immunotherapy, gene silencing, and editing techniques, including RNA interference and CRISPR/Cas9, offer innovative means to modulate gene expression and correct genetic aberrations driving HNSCC. The integration of stem cell research with personalized medicine presents opportunities for tailored regenerative approaches. The synergy between personalized medicine and technological advancements is exemplified by artificial intelligence (AI) and machine learning (ML) applications. These tools empower clinicians to analyze vast datasets, predict patient responses, and optimize treatment strategies with unprecedented accuracy. Conclusion The developments and prospects of personalized medicine in HNSCC diagnosis and treatment offer a transformative approach to managing this complex malignancy. By harnessing genetic insights, biomarkers, immunotherapy, gene editing, stem cell therapies, and advanced technologies like AI and ML, personalized medicine holds the key to enhancing patient outcomes and ushering in a new era of precision oncology.

Tong Liu, Xueping Gu, Shaoyan Li et al.

Abstract The existing static voltage stability margin evaluation methods cannot meet the actual demand of current power grid well in terms of calculation speed and accuracy. Thus, this paper proposes a static voltage stability margin prediction method based on a graph attention network (GAT) and a long short‐term memory network (LSTM) to predict the static voltage stability margin of a power system accurately, fast, and effectively, considering new energy uncertainty. First, an innovative machine learning framework named the GAT‐LSTM is designed to extract highly representative power grid operation features considering the spatial‐temporal correlation of the power grid operation. Then, a static voltage stability margin prediction method based on the GAT‐LSTM is developed. Particularly, considering the influence of new energy power uncertainty, two loss functions of certainty and uncertainty are used in the proposed method to predict the voltage stability margin and voltage fluctuation range. Finally, the IEEE39‐bus power system and a practical power system are employed to verify the proposed method. The results show that the computational speed of the proposed method is greatly improved compared to the traditional methods not based on machine learning; the computation results are more accurate and reliable than the existing machine learning methods. Compared with the existing methods, the proposed method has higher scalability and applicability.

Dapai Shi, Jingyuan Zhao, Chika Eze et al.

As the popularity of electric vehicles (EVs) and smart grids continues to rise, so does the demand for batteries. Within the landscape of battery-powered energy storage systems, the battery management system (BMS) is crucial. It provides key functions such as battery state estimation (including state of charge, state of health, battery safety, and thermal management) as well as cell balancing. Its primary role is to ensure safe battery operation. However, due to the limited memory and computational capacity of onboard chips, achieving this goal is challenging, as both theory and practical evidence suggest. Given the immense amount of battery data produced over its operational life, the scientific community is increasingly turning to cloud computing for data storage and analysis. This cloud-based digital solution presents a more flexible and efficient alternative to traditional methods that often require significant hardware investments. The integration of machine learning is becoming an essential tool for extracting patterns and insights from vast amounts of observational data. As a result, the future points towards the development of a cloud-based artificial intelligence (AI)-enhanced BMS. This will notably improve the predictive and modeling capacity for long-range connections across various timescales, by combining the strength of physical process models with the versatility of machine learning techniques.

Shuai Wang, Zongbao Zhang, Chao Wang

Abstract The mining of open pit mines is widespread in China, and there are many cases of landslide accidents. Therefore, the problem of slope stability is highlighted. The stability of the slope is a factor that directly affects the mining efficiency and the safety of the entire mining process. According to the statistics, there is a 15 percent chance of finding landslide risk in China’s large-scale mines. And due to the expansion of the mining scale of the enterprise, the problem of slope stability has become increasingly obvious, which has become a major subject in the study of open-pit mine engineering. In order to better predict the slope stability coefficient, this study takes a mine in China as a case to deeply discuss the accuracy of different algorithms in the stability calculation, and then uses a deep learning algorithm to study the stability under rainfall conditions. The change of the coefficient and the change of the stability coefficient before and after the slope treatment are experimentally studied with the displacement of the monitoring point. The result shows that the safety coefficient calculated by the algorithm in this paper is about 7% lower than that of the traditional algorithm. In the slope stability analysis before treatment, the safety factor calculated by the algorithm in this paper is 1.086, and the algorithm in this paper is closer to reality. In the stability analysis of the slope after treatment, the safety factor calculated by the algorithm in this paper is 1.227, and the stability factor meets the requirements of the specification. It also shows that the deep learning algorithm effectively improves the efficiency of the slope stability factor prediction and improves security during project development.

Andrew W. Taylor-Robinson, Andrew W. Taylor-Robinson, Andrew W. Taylor-Robinson et al.

Tala Talaei Khoei, Naima Kaabouch

Intrusion Detection Systems are expected to detect and prevent malicious activities in a network, such as a smart grid. However, they are the main systems targeted by cyber-attacks. A number of approaches have been proposed to classify and detect these attacks, including supervised machine learning. However, these models require large labeled datasets for training and testing. Therefore, this paper compares the performance of supervised and unsupervised learning models in detecting cyber-attacks. The benchmark of CICDDOS 2019 was used to train, test, and validate the models. The supervised models are Gaussian Naïve Bayes, Classification and Regression Decision Tree, Logistic Regression, C-Support Vector Machine, Light Gradient Boosting, and Alex Neural Network. The unsupervised models are Principal Component Analysis, K-means, and Variational Autoencoder. The performance comparison is made in terms of accuracy, probability of detection, probability of misdetection, probability of false alarm, processing time, prediction time, training time per sample, and memory size. The results show that the Alex Neural Network model outperforms the other supervised models, while the Variational Autoencoder model has the best results compared to unsupervised models.

Timothy Sands

Infrastructure monitoring, inspection, repair, and replacement in space is crucial for continued usage and safety, yet it is expensive, time-consuming, and technically very challenging. New robotics technologies and artificial intelligence algorithms are potentially novel approaches that may alleviate such demanding operations using existing or novel sensing technologies. Space structures must necessarily be very light weight due to the high costs of placing robots in space. Several methods are proposed and compared to control highly flexible space robotics, where a key challenge is the presence of flexible resonant modes at frequencies so low as to reside inside typical feedback controller bandwidths. Such conditions imply the very action of sending control signals to the ultra-light weight robotics will cause structural resonance. Implementations of incrementally increasing order are offered, achieving an over ninety percent performance improvement in trajectory tracking errors, while improvement using unshaped methods merely achieve a twenty-four percent improvement in direct comparison (where the only modification is the proposed control methodology). Based on superior performance, single-sinusoidal trajectory shaping is recommended, with a corollary benefit of preparing future research into applying deterministic artificial intelligence whose current instantiation relies on single-sinusoidal, autonomous trajectory generation.

Qianqian Qian, Ke Cheng, Wei Qian et al.

The gradient vector flow (GVF) model has been widely used in the field of computer image segmentation. In order to achieve better results in image processing, there are many research papers based on the GVF model. However, few models include image structure. In this paper, the smoothness constraint formula of the GVF model is re-expressed in matrix form, and the image knot represented by the Hessian matrix is included in the GVF model. Through the processing of this process, the relevant diffusion partial differential equation has anisotropy. The GVF model based on the Hessian matrix (HBGVF) has many advantages over other relevant GVF methods, such as accurate convergence to various concave surfaces, excellent weak edge retention ability, and so on. The following will prove the advantages of our proposed model through theoretical analysis and various comparative experiments.

Rohit Sharma, Raghvendra Kumar, Suresh Chandra Satapathy et al.

The Yamuna river has become one of the most polluted rivers in India as well as in the world because of the high-density population growth and speedy industrialization. The Yamuna river is severely polluted and needs urgent revival. The Yamuna river in Dehradun is polluted due to exceptional tourist activity, poor sewage facilities, and insufficient wastewater management amenities. The measurement of the quality can be done by water quality assessment. In this study, the water quality index has been calculated for the Yamuna river at Dehradun using monthly measurements of 12 physicochemical parameters. Trend forecasting for river water pollution has been performed using different parameters for the years 2020–2024 at Dehradun. The study shows that the values of four parameters namely, Temperature, Total Coliform, TDS, and Hardness are increasing yearly, whereas the values of pH and DO are not rising heavily. The considered physicochemical parameters for the study are TDS, Chlorides, Alkalinity, DO, Temperature, COD, BOD, pH, Magnesium, Hardness, Total Coliform, and Calcium. As per the results and trend analysis, the value of total coliform, temperature, and hardness are rising year by year, which is a matter of concern. The values of the considered physicochemical parameters have been monitored using various monitoring stations installed by the Central Pollution Control Board (CPCB), India.

Asep Saeful Milak, Eka Wahyu Hidayat, Aldy Putra Aldya

Game adalah salah satu media teknologi yang populer di kalangan masyarakat baik dari anak kecil maupun orang dewasa sebagai media hiburan. game itu sendiri memiliki banyak sekali genre atau jenisnya. Penelitian ini mengembangkan game dengan menerapkan Artificial Intelligence pada game racing. Game sejenis yang telah diobservasi memiliki kelemahan bersifat endless run dan juga tidak memiliki NPC (Non-Player Character) didalamnya. Selain itu, alasan penelitian ini dibuat karena untuk mengangkat permainan tradisional yang hampir dilupakan, untuk itu perlu dibuat game sejenis yang berbeda dari sebelumnya dengan menambahkan NPC didalam game dengan menerapkan beberapa Algoritma. Di dalam penelitian ini menerapkan Algoritma Collision Avoidance System untuk membuat NPC dapat menghindari Obstacle dan Algoritma Random Number Generator untuk membuat Obstacle muncul secara acak. Dalam penelitian ini berhasil membuat “Balap Egrang” dengan Metode Game Development Life Cycle (GDLC). Berdasarkan pengujian yang telah dilakukan, hasil pengujian alpha sudah sesuai secara fungsional dan dari pengujian beta hasil uji fungsionalitas User Acceptence Test (UAT) didapat nilai sebesar 82,58% dinyatakan layak untuk digunakan dengan interpretasi “Baik” yang berarti game ini layak digunakan dan dapat dikembangkan.   Abstract Egrang game is one of the traditional games in Indonesia that uses two meters of bamboo material and is given a footing below. This game can be played by all ages by stepping on the footing and maintaining balance when walking at a certain distance and time. This game has begun to erode the era and needs to be preserved. This study tries to revive traditional games into digital-based games by implementing Artificial Intelligence. Similar games that have been observed, such as the Balap Karungs Game from Playstore which is in the racing genre, have endless run weaknesses and also have no Non-Player Character NPCs. The results of this study are Android-based games by applying the Collision Avoidance System Algorithm to make NPCs avoid the Obstacle and Random Number Generator Algorithms to make the Obstacle appear randomly. The development of the Balap Egrang Game uses the Game Development Life Cycle (GDLC) method. Based on the tests that have been carried out, the alpha test results are functionally appropriate and from the beta testing the results of the User Acceptence Test (UAT) functionalities obtained a value of 82.58% which is declared feasible to use with the interpretation of "Good" which means that the game is feasible developed..

Jian-bo CHANG, Ren-zhi WANG, Ming FENG

Both manifestations and treatments of intracranial hemorrhage (ICH) are varied and the effect meets bottlenecks. Recently, the artificial intelligence (AI) technology has developed rapidly. This review aims to help clinicians understand AI technology regarding its application in ICH by systematically reviewing the historical and current examples. Hope to stimulate the AI progress and enhance the level of treatment in ICH in the future. Ultimately, the treatment of ICH would be precision and individualization. DOI:10.3969/j.issn.1672-6731.2019.09.004

Craig Olson, Yuliya Lierler

In this work we design a narrative understanding tool Text2ALM. This tool uses an action language ALM to perform inferences on complex interactions of events described in narratives. The methodology used to implement the Text2ALM system was originally outlined by Lierler, Inclezan, and Gelfond (2017) via a manual process of converting a narrative to an ALM model. It relies on a conglomeration of resources and techniques from two distinct fields of artificial intelligence, namely, natural language processing and knowledge representation and reasoning. The effectiveness of system Text2ALM is measured by its ability to correctly answer questions from the bAbI tasks published by Facebook Research in 2015. This tool matched or exceeded the performance of state-of-the-art machine learning methods in six of the seven tested tasks. We also illustrate that the Text2ALM approach generalizes to a broader spectrum of narratives.