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Articles 301 - 330 of 1390
Full-Text Articles in Artificial Intelligence and Robotics
Educating With Ai, Grace Seo, David Wicks
Educating With Ai, Grace Seo, David Wicks
AI and the Future of Work
This conference track explores the integration of AI within teaching and learning, with a focus on practical approaches that leverage AI technologies to optimize teaching practices and enhance students’ learning experience. The topics include the essential AI literacies in educational contexts, collaborative learning with AI, and the use of AI for enhanced learning assessments.
A Generalized Machine Learning Model For Long-Term Coral Reef Monitoring In The Red Sea, Justin J. Gapper, Surendra Maharjan, Wenzhao Li, Erik Linstead, Surya Prakash Tiwari, Mohamed A. Qurban, Hesham El-Askary
A Generalized Machine Learning Model For Long-Term Coral Reef Monitoring In The Red Sea, Justin J. Gapper, Surendra Maharjan, Wenzhao Li, Erik Linstead, Surya Prakash Tiwari, Mohamed A. Qurban, Hesham El-Askary
Mathematics, Physics, and Computer Science Faculty Articles and Research
Coral reefs, despite covering less than 0.2 % of the ocean floor, harbor approximately 35 % of all known marine species, making their conservation critical. However, coral bleaching, exacerbated by climate change and phenomena such as El Niño, poses a significant threat to these ecosystems. This study focuses on the Red Sea, proposing a generalized machine learning approach to detect and monitor changes in coral reef cover over an 18-year period (2000–2018). Using Landsat 7 and 8 data, a Support Vector Machine (SVM) classifier was trained on depth-invariant indices (DII) derived from the Gulf of Aqaba and validated against ground …
An Automated Machine Learning Approach To The Retrieval Of Daily Soil Moisture In South Korea Using Satellite Images, Meteorological Data, And Digital Elevation Model, Nari Kim, Soo-Jin Lee, Eunha Sohn, Mija Kim, Seonkyeong Seong, Seung Hee Kim, Yangwon Lee
An Automated Machine Learning Approach To The Retrieval Of Daily Soil Moisture In South Korea Using Satellite Images, Meteorological Data, And Digital Elevation Model, Nari Kim, Soo-Jin Lee, Eunha Sohn, Mija Kim, Seonkyeong Seong, Seung Hee Kim, Yangwon Lee
Institute for ECHO Articles and Research
Soil moisture is a critical parameter that significantly impacts the global energy balance, including the hydrologic cycle, land–atmosphere interactions, soil evaporation, and plant growth. Currently, soil moisture is typically measured by installing sensors in the ground or through satellite remote sensing, with data retrieval facilitated by reanalysis models such as the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) and the Global Land Data Assimilation System (GLDAS). However, the suitability of these methods for capturing local-scale variabilities is insufficiently validated, particularly in regions like South Korea, where land surfaces are highly complex and heterogeneous. In contrast, artificial intelligence …
The Aimag Project: Using Machine Learning To Predict Crustal Magnetic Anomaly Values, Xavier Gobble, Marlie Mollett, Dr. Dawn King, Dr. Cory Reed, Erin Knese
The Aimag Project: Using Machine Learning To Predict Crustal Magnetic Anomaly Values, Xavier Gobble, Marlie Mollett, Dr. Dawn King, Dr. Cory Reed, Erin Knese
Undergraduate Research Symposium
A detailed model of the Earth’s total magnetic field is important for acquiring the means for GPS-alternative, magnetic anomaly-based navigation. The Earth’s total magnetic field is an amalgam of 5 mechanisms: the geodynamo generated by the rotation of the Earth’s molten iron core, the fields induced by the flows of electric current in the atmosphere and oceans, the disturbance of the ionosphere by solar wind, and local anomalies attributable to ferromagnetic minerals present in the crust; the lattermost compose the crustal magnetic field. The EMAG2v3 dataset comprises a compilation of satellite, shipborne, and airborne magnetic measurements differenced from the Comprehensive …
Fostering Trust Through User Interface Design In Multi-Drone Search And Rescue, Johanna Ahlskog, Maria Theresa Bahodi, Artur Lugmayr, Timothy Merritt
Fostering Trust Through User Interface Design In Multi-Drone Search And Rescue, Johanna Ahlskog, Maria Theresa Bahodi, Artur Lugmayr, Timothy Merritt
Research outputs 2022 to 2026
Unmanned Aerial Vehicles (UAVs), or drones, are increasingly used in search and rescue (SAR) missions, with pilots transitioning from manual control of single drones to more collaborative tasks orchestrating semi-autonomous fleets. Designing user interfaces to support UAV pilots effectively is crucial to improving the success of search missions. We developed two versions of a multi-drone SAR system prototype to simulate SAR missions and evaluated them with professional UAV SAR pilots in Sweden. Both versions showed the flight paths of the UAVs, yet in one version, a heatmap was overlayed to provide information from a lost person model. We evaluated situational …
Research On Simulation Resource Management Based On Graph Association Organization, Zewei Liu, Yishan Ding, Tingyu Lin, Mingxing Ke, Liqing Guo, Yingying Xiao, Zhilong Zhao, Yan Li, Xuan Lü
Research On Simulation Resource Management Based On Graph Association Organization, Zewei Liu, Yishan Ding, Tingyu Lin, Mingxing Ke, Liqing Guo, Yingying Xiao, Zhilong Zhao, Yan Li, Xuan Lü
Journal of System Simulation
Abstract: The simulation test and evaluation of intelligent system of systems, systems and single equipment is a complex system engineering, which requires effective management of multi-source, heterogeneous and distributed massive simulation resources scattered in cloud test centers and test sites of various units; and good control of dynamically generated tasks, assumptions, configurations, results, evaluations and other data and files. The traditional way of managing and querying simulation resources by category is inefficient and difficult to meet the requirements of large-scale intelligent simulation test and evaluation activities. An overall framework for simulation resource management based on graph association organization, defines a …
Path Planning Based On Improved A* And Dynamic Window Approach, Peng Ji, Xinyuan Zhang, Shuaixuan Gao, Shuorang Wei
Path Planning Based On Improved A* And Dynamic Window Approach, Peng Ji, Xinyuan Zhang, Shuaixuan Gao, Shuorang Wei
Journal of System Simulation
Abstract: In response to the low efficiency, redundant turning points, and collision issues of the traditional A* algorithm, a smart vehicle path planning algorithm that integrates an improved A* algorithm with a dynamic window approach has been proposed. The algorithm has enhanced the search point selection method, optimized the evaluation function, selected key turning points based on the slope values between turning points, and removed redundant turning points. Between every two optimized key turning points, a dynamic window approach that balances speed and safety is used for local obstacle avoidance. Experiments show that compared to the traditional A* algorithm, this …
Research On Digital Twin System Of Rockshaft Hoist, Baiting Zhao, Jianguo Shi, Xiaofen Jia
Research On Digital Twin System Of Rockshaft Hoist, Baiting Zhao, Jianguo Shi, Xiaofen Jia
Journal of System Simulation
Abstract: In order to solve the problem of low intelligence and digitization of the current mine hoisting system, a method based on DT for digital modeling, 3D visualization, and virtual real interaction of shaft hoisting machines is proposed. Aiming at the rockshaft hoist system, based on the digital twin five dimensional model framework, we analyze the operating mechanism of the equipment, and model the fully physical digital system of the rockshaft hoist. By constructing multidimensional multi-scale models and multidimensional heterogeneous data models, twin digital scenes are constructed, and virtual real mapping technology is combined to achieve dynamic mapping and virtual …
A Multimodal Residual Spatial-Temporal Fusion Model Based On Automatic Sleep Classification, Yecai Guo, Shuang Tong
A Multimodal Residual Spatial-Temporal Fusion Model Based On Automatic Sleep Classification, Yecai Guo, Shuang Tong
Journal of System Simulation
Abstract: Highly accurate sleep staging plays a crucial role in correctly assessing sleep conditions. Aiming at the problem that the existing convolutional network cannot obtain the topological characteristics of physiological signals, a sleep staging algorithm based on multi-modal residual spatio-temporal fusion is proposed. Time-frequency images and spatio-temporal images are obtained using short-time Fourier transform and adaptive map convolution, which are converted into high-dimensional feature vectors; lightweight interaction of feature information flow is realized through time-frequency feature and spatiotemporal feature extraction modules; the feature enhancement fusion module fuses feature information to outputs sleep staging results. The results show that the model …
Research On Autonomous Decision-Making In Air-Combat Based On Improved Proximal Policy Optimization, Dianwei Qian, Hongmin Qi, Zhen Liu, Zhiming Zho, Jianqiang Yi
Research On Autonomous Decision-Making In Air-Combat Based On Improved Proximal Policy Optimization, Dianwei Qian, Hongmin Qi, Zhen Liu, Zhiming Zho, Jianqiang Yi
Journal of System Simulation
Abstract: To address the problems of high information redundancy and slow convergence speed of traditional reinforcement learning in air-combat autonomous decision-making applications, a proximal policy optimization air-combat autonomous decision-making method, based on dual observation and composite reward is proposed. A dual observation space, which contains interaction information as the main information and individual feature information as a supplement, was designed to reduce the influence of redundant battlefield information on the training efficiency of the decision model. A composite reward function combining result reward and process reward was designed to improve convergence speed. The generalized advantage estimator was applied in the …
An Intelligent Adversaries Behavior Simulation Technology Based On Improved Behavior Trees, Fang Zhou, Bo Fan, Xiaoyi Liu, Yishan Ding, Ningxin Zhang, Yachao Shao, Xiaoyu Zhai
An Intelligent Adversaries Behavior Simulation Technology Based On Improved Behavior Trees, Fang Zhou, Bo Fan, Xiaoyi Liu, Yishan Ding, Ningxin Zhang, Yachao Shao, Xiaoyu Zhai
Journal of System Simulation
Abstract: Intelligent algorithm/intelligent platform/intelligent system intelligence capability testing and evaluation need to solve high-level intelligent opponent simulation problems, an intelligent opponent behavior simulation technology based on improved behavior tree is proposed. Four types of behavior tress nodes are designed, including behavior control, combat tasks, behavior actions, and execution condition node. Five atomic behavior actions and parameters are established, including maneuver, reconnaissance and early warning, command and decision-making, firepower strike, and electronic interference node. Five atomic condition nodes are provided, including target selection, weapon launch, and incoming weapon judgment node. The intelligent adversarial behavior simulation system is designed, including a behavior …
Study On Invulnerability Of Urban Rail Network Considering Sum Of The Neighbors Degree, Shuqing Li, Yixiao Song, Guojian Zhong
Study On Invulnerability Of Urban Rail Network Considering Sum Of The Neighbors Degree, Shuqing Li, Yixiao Song, Guojian Zhong
Journal of System Simulation
Abstract: In order to solve the problem of network cascade paralysis caused by urban rail station or line failure, considering the influence of the first-order neighborhood of network nodes, the load distribution impedance coefficient is proposed based on the nonlinear capacity load model, and a nonlinear capacity load optimization model considering the sum of the neighbors degree is constructed. By optimizing load structure, the alternative probability of nodes during load redistribution is adjusted to reduce the number of node failures in the cascading process, thereby the rail network invulnerability is improved. Taking Chongqing rail network as an example, the rail …
Multi-Step Information Aided Q-Learning Path Planning Algorithm, Yuelong Wang, Songyan Wang, Tao Chao
Multi-Step Information Aided Q-Learning Path Planning Algorithm, Yuelong Wang, Songyan Wang, Tao Chao
Journal of System Simulation
Abstract: To improve the path planning capability of mobile robots in a static environment and solve the problem of slow convergence of the traditional Q-learning algorithm in path planning, this paper proposes a multi-step information-aided Q-learning improvement algorithm. Using the multi-step information of greedy action in ε -greedy strategy and length of the historical optimal path to update the eligibility traces, which makes the effective eligibility traces work continuously in the iteration of the algorithm and solves the loop traps that may fall into with the preserved multi-step information; using the local multiflower pollination algorithm to initialize the Q-value table …
The Synchronous Grasping Method Of Virtual-Real Assembly Robot Based On Digital Twin, Jian Xu, Gaofeng Liu, Yijian Zhao, Zili Zheng, Huanying Yan
The Synchronous Grasping Method Of Virtual-Real Assembly Robot Based On Digital Twin, Jian Xu, Gaofeng Liu, Yijian Zhao, Zili Zheng, Huanying Yan
Journal of System Simulation
Abstract: A method based on digital twin for assembly robot virtual-real synchronization and grasping is proposed to address the issues of poor intelligent grasping accuracy and difficult data processing in assembly tasks for industrial robots. Based on the digital twin, a digital twin assembly robot virtual-real synchronization and grasping architecture is designed. The OPC UA information model is built by classifying multi-source heterogeneous data, and the OPC UA communication protocol is used as a bridge for data communication of the assembly robot, achieving virtual-real synchronization. The convolutional neural network is further trained using the virtual robot to improve the grasping …
Indicator Transfer Learning Based On Cloud Model And Maximum Mean Discrepancy, Lixia Xu, Jilong Zhong, Shaoshi Wu, Yishan Ding, Xiaoyu Zhai, Shizhao Chen, Yizhe Wang, Xue Wen, Juanfang Zeng, Xinwen Hou
Indicator Transfer Learning Based On Cloud Model And Maximum Mean Discrepancy, Lixia Xu, Jilong Zhong, Shaoshi Wu, Yishan Ding, Xiaoyu Zhai, Shizhao Chen, Yizhe Wang, Xue Wen, Juanfang Zeng, Xinwen Hou
Journal of System Simulation
Abstract: In response to the problem of rare data samples in application experiment scenarios, this paper proposes an indicator transfer learning method based on cloud models and Maximum Mean Discrepancy (MMD), which transfers the indicator calculation model from typical simulation experiment scenarios to application experiment scenarios to meet the needs across platform and domain simulation evaluation. Using the maximum mean difference method to align the indicator distribution in the typical simulation experiment scenario to the indicator distribution in the application experiment scenario, thereby achieves indicator transfer, and by using cloud models based on a small number of examples for modeling …
Adversarial Simulation Testing Algorithm For Svm Based On Multi-Objective Evolutionary Optimization, Feixing Li, Lining Xing, Yu Zhou
Adversarial Simulation Testing Algorithm For Svm Based On Multi-Objective Evolutionary Optimization, Feixing Li, Lining Xing, Yu Zhou
Journal of System Simulation
Abstract: Machine learning typically mines underlying patterns and rules from data, making it susceptible to phenomena such as overfitting and underfitting, which in turn affects the generalization and robustness of learning models. This paper explores the potential fragility and instability of SVM from the perspective of adversarial simulation testing. The adversarial simulation strategy employed involves selectively contaminating training sample labels to simulate an attack on the SVM classifier, thereby degrading its performance and testing its dependency on training samples. To explore the ceiling of performance degradation of an SVM classifier under the combination attack of different samples, the contradictory objectives …
Simulation Study Of Personnel Evacuation In Fire Scenarios Of Old School Buildings, Qiankun Zhu, Jiwu Li, Yongfeng Du
Simulation Study Of Personnel Evacuation In Fire Scenarios Of Old School Buildings, Qiankun Zhu, Jiwu Li, Yongfeng Du
Journal of System Simulation
Abstract: In order to improve the emergency evacuation capability of an old school building under fire scenarios, a fire evacuation model of an old school building is developed. The PyroSim software is used to build a fire dispersion model to simulate and analyse the changes of smoke visibility, temperature and CO at the safety exit of the fire floor in the school building under the condition of mechanical smoke exhaust, automatic sprinkler and whether the windows of the fire room are open or not. The simulation of the exit status and evacuation of people has been carried out in conjunction …
Research On Orb-Slam Algorithm Based On Windowed Matching Estimation, Wanye Yao, Zewei Pang, Peijie Sun, Zhu Wang
Research On Orb-Slam Algorithm Based On Windowed Matching Estimation, Wanye Yao, Zewei Pang, Peijie Sun, Zhu Wang
Journal of System Simulation
Abstract: To address unstability of location accuracy of ORB-SLAM system caused by randomness of camera pose solution method, an improved pose solution method based on feature point windowed matching and analytical ICP is proposed, and the mobile robot ORB-SLAM system is constructed. The extracted feature points are windowed to improve matching efficiency while ensuring good feature point matching, the analytical ICP algorithm is used to solve the camera pose for avoiding iteration, and the windowed pose solution with the smallest error is selected for bundle adjustment to reduce the pose errors caused by local information loss or mismatching. The results …
Carbon Footprint Analysis And Low-Carbon Optimization Method Simulation Study Of Power Transformer Based On Digital Twin Technology, Dongxue Li, Yan Liu, Boyao Shen, Yongteng Jing, Qiang Ma, Ran Liu
Carbon Footprint Analysis And Low-Carbon Optimization Method Simulation Study Of Power Transformer Based On Digital Twin Technology, Dongxue Li, Yan Liu, Boyao Shen, Yongteng Jing, Qiang Ma, Ran Liu
Journal of System Simulation
Abstract: Power transformers are the main energy-consuming equipment for substations. According to the goal of “carbon peak, carbon neutralization” in China, it is of great significance to accurately calculate the carbon footprint of transformers and seek low-carbon optimization methods. A method for constructing a digital twin model of power transformer magnetic characteristics is proposed. Based on the three-dimensional electromagnetic time-harmonic field finite element analysis method, a threedimensional model of SZ11-31.5MVA/66kV power transformer is established. The transformer loss map is obtained under fluctuating load condition, and the transformer digital twin model is constructed. The carbon footprint of the transformer is analyzed, …
Uav Online Track Planning Based On Dmoea-Aptc Algorithm, Erchao Li, Shenghui Zhang
Uav Online Track Planning Based On Dmoea-Aptc Algorithm, Erchao Li, Shenghui Zhang
Journal of System Simulation
Abstract: In order to solve the dynamic multi-objective optimization problem with time correlation, this paper introduces the concept of time correlation feature and establishes the model of UAV timecorrelation dynamic multi-objective optimization problem moedl on the basis of UAV online track planning problem, and proposes a dynamic multi-objective double-layer optimization algorithm using adaptive predictive response mechanism and time-correlation optimization mechanism (DMOEA-APTC). The intensity of environmental change was judged according to the correlation of environmental change and different response mechanisms were used to quickly adapt to environmental change. In the optimization process, the least square method was used to learn the …
Edge Surveillance Task Offloading And Resource Allocation Algorithm Based On Drl, Chao Li, Jiabao Li, Caichang Ding, Zhiwei Ye, Fangwei Zuo
Edge Surveillance Task Offloading And Resource Allocation Algorithm Based On Drl, Chao Li, Jiabao Li, Caichang Ding, Zhiwei Ye, Fangwei Zuo
Journal of System Simulation
Abstract: For the resource limitation of intensive surveillance tasks in edge computing, a surveillance task offloading and resource allocation algorithm based on DRL is proposed. With the optimization objectives of surveillance task delay and recognition accuracy, the joint decision objective optimization solution of task offloading, wireless channel allocation, and image compression rate was modeled as a Markov decision process. To address the problem of slow and unstable algorithm convergence due to the high volatility of training samples caused by the dynamic nature of wireless channels and the randomness of surveillance tasks, an attention mechanism is used to jointly encode channel …
Modeling And Simulation Of Pipeline Cable Inspection Robot Based On Omnidirectional Wheel, Chao Yuan, Yao Zhang, Yadong Zhao, Dawei Xu, Jing Yuan, Yongjie Zhai
Modeling And Simulation Of Pipeline Cable Inspection Robot Based On Omnidirectional Wheel, Chao Yuan, Yao Zhang, Yadong Zhao, Dawei Xu, Jing Yuan, Yongjie Zhai
Journal of System Simulation
Abstract: Aiming at the problem that the inner space of underground pipeline cable is narrow and closed, which cannot be inspected by humans, and the existing pipeline robot cannot adapt to the special environment of pipeline cable, a miniaturized, compact pipeline cable inspection robot is designed. This robot is capable of operating within the underground pipeline where cables have already been laid to inspect the inner wall of the pipeline and the working condition of the cables. According to the requirements of the working conditions, the whole three-dimensional model of the robot has been established. The mapping relationship between the …
A New Model Predictive Current Controller Forac-Dc Matrix Converter In Unbalanced Grids, Wenlang Deng, Minghai Wu, Haipeng Xie, Yingjie Hu
A New Model Predictive Current Controller Forac-Dc Matrix Converter In Unbalanced Grids, Wenlang Deng, Minghai Wu, Haipeng Xie, Yingjie Hu
Journal of System Simulation
Abstract: To reduce the fluctuation of active power on the grid side of AC-DC matrix converters under unbalanced input conditions and to address the issue of variable switching frequency in discrete model predictive control, this paper proposes a novel model predictive control method. This method selects effective vectors based on the phase angle of the grid current, thus avoiding the computational burden of evaluating the value function in traditional model predictive control. Additionally, a second-order extended complex Kalman filter is introduced, which achieves the computation accuracy of the secondorder term of the Taylor series expansion and enables the application of …
Visual Robot Obstacle Avoidance Planning And Simulation Using Mapped Point Clouds, Hanlin Huo, Xiangjun Zou, Yan Chen, Xinzhao Zhou, Mingyou Chen, Chengen Li, Yaoqiang Pan, Yunchao Tang
Visual Robot Obstacle Avoidance Planning And Simulation Using Mapped Point Clouds, Hanlin Huo, Xiangjun Zou, Yan Chen, Xinzhao Zhou, Mingyou Chen, Chengen Li, Yaoqiang Pan, Yunchao Tang
Journal of System Simulation
Abstract: In response to the large and complex data volume and high redundancy of visual point cloud obstacle recognition in complex unstructured orchard environments, which severely impacts the real-time performance and efficiency of harvesting operations, a point cloud compression algorithm is proposed based on point cloud segmentation to enhance the efficiency of point cloud obstacle recognition and environmental adaptability. An Informed RRT* based approach is used combined with an inverse projection algorithm, mapping-based informed RRT*(M-Informed RRT*) to solve the harvesting path problem. By constructing a highly real-time and robust integrated robot system for sampling, perception, and obstacle avoidance, efficient obstacle …
An Improved Path Planning Algorithm For Mobile Robots, Haijie Sun, Hongjun San, Le Xiao, Dexin Yao, Jiupeng Chen, Xiaoyuan Yang
An Improved Path Planning Algorithm For Mobile Robots, Haijie Sun, Hongjun San, Le Xiao, Dexin Yao, Jiupeng Chen, Xiaoyuan Yang
Journal of System Simulation
Abstract: To solve the problems of invalid sampling and non-optimal paths of the RRT, the quasi-stream avoidance algorithm is proposed. The RRT algorithm is introduced to specify the sampling interval to limit the sampling points and enhance the goal-oriented nature of sampling. The quasi-stream avoidance algorithm incorporating the A* algorithm (QSA*) is used to quickly bypass the obstacle when it is encountered. A path optimization algorithm is used to smooth the searched path. The simulation results show that compared with the RRT algorithm, the computation time of the RRT-QSA* algorithm is reduced by 96.83%~99.88%, the number of search nodes is …
Research On System-Of-Systems Confrontation Simulation Method Based On Operation Loops, Shan Zhong, Yesheng Zhu, Menglu Zhou
Research On System-Of-Systems Confrontation Simulation Method Based On Operation Loops, Shan Zhong, Yesheng Zhu, Menglu Zhou
Journal of System Simulation
Abstract: In the field of modeling and analyzing capabilities for operation system-of-systems (SoS), traditional structured capability assessment models lack the analysis of the interaction between both rivals and armies in different roles. The system model based on operation loop theory can be combined with the relationship between sensor, decision-making, influence, and target nodes for system capability calculation, but the existing model is usually only suitable for static analysis and cannot be used for dynamic simulation of SoS confrontation. In order to solve the problems above, a SoS confrontation simulation method based on operation loops is proposed. It abstracts both rivals’ …
Deep Learning Approach In Melanoma Stage Classification, Frank Lemba Lemba, Clopas Kwenda
Deep Learning Approach In Melanoma Stage Classification, Frank Lemba Lemba, Clopas Kwenda
African Conference on Information Systems and Technology
Accurate and efficient classification of melanoma stages is crucial for effective treatment planning and improved patient outcomes. Traditional diagnostic methods are often time-consuming and subjective, highlighting the need for advanced computational approaches. This study proposes a self-supervised learning framework combined with a convolutional neural network (CNN) to classify melanoma stages more effectively. Initially, features are extracted from unlabeled skin images using pre-trained VGG16 and ResNet50 models. These features are combined and reduced in dimensionality using Principal Component Analysis (PCA). Subsequently, K-means and DBSCAN clustering is applied to pseudo-label the data, providing a foundation for pre-training a CNN model. This pre-trained …
Trends From 20 Years Of Artificial Intelligence In Financial Services In Africa, Nthabiseng Moela, Lerato Matlala, Jackie Ma, Dipuo Maphutha, Hossana Twinomurinzi
Trends From 20 Years Of Artificial Intelligence In Financial Services In Africa, Nthabiseng Moela, Lerato Matlala, Jackie Ma, Dipuo Maphutha, Hossana Twinomurinzi
African Conference on Information Systems and Technology
The need for financial inclusion in Africa, particularly for marginalised groups like women and small businesses, highlights the importance of leveraging Artificial Intelligence (AI). This study provides a bibliometric analysis of AI's integration into African financial services from 2003 to 2023. The key results show a significant increase in AI use, particularly in fraud detection, credit risk prediction, and stock market volatility forecasting, with 49% of the research coming from South Africa, Nigeria, and Tunisia. However, areas like financial development management, inflation control, and gender disparities in loan access remain underexplored. The emphasis has been on the technical implementation of …
Review Of Data Bias In Healthcare Applications, Atharva Prakash Parate, Aditya Ajay Iyer, Kanav Gupta, Harsh Porwal, P. C. Kishoreraja, R. Sivakumar, Rahul Soangra
Review Of Data Bias In Healthcare Applications, Atharva Prakash Parate, Aditya Ajay Iyer, Kanav Gupta, Harsh Porwal, P. C. Kishoreraja, R. Sivakumar, Rahul Soangra
Physical Therapy Faculty Articles and Research
In the area of medical artificial intelligence (AI), data bias is a major difficulty that affects several phases of data collection, processing, and model building. The many forms of data bias that are common in AI in healthcare are thoroughly examined in this review study, encompassing biases related to socioeconomic status, race, and ethnicity as well as biases in machine learning models and datasets. We examine how data bias affects the provision of healthcare, emphasizing how it might worsen health inequalities and jeopardize the accuracy of AI-driven clinical tools. We address methods for reducing data bias in AI and focus …
Neurosymbolic Cognitive Methods For Enhancing Foundation Model-Based Reasoning, Kaushik Roy, Siyu Wu, Alessandro Oltramari
Neurosymbolic Cognitive Methods For Enhancing Foundation Model-Based Reasoning, Kaushik Roy, Siyu Wu, Alessandro Oltramari
Faculty Publications
Foundation models have emerged as powerful tools, exhibiting extraordinary performance across various tasks, such as language processing, visual recognition, code generation, and human-centered engagement. However, recent studies have highlighted their limitations when grounded, abstract, and generalized reasoning capabilities are required. Complex tasks often involve multiple hierarchical reasoning steps, which are typical features of human thinking processes. In fact, in this chapter we claim that cognitively-inspired computational models, such as the so-called Common Model of Cognition, are key to enable complex reasoning within foundation model-based artificial intelligence (AI) systems. We investigate neurosymbolic approaches for mapping AI system components to those of …