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Articles 331 - 360 of 1287
Full-Text Articles in Computer Engineering
Approximate Computing And In-Memory Computing: The Best Of The Two Worlds!, Mohammed Essa Fawzy Essa
Approximate Computing And In-Memory Computing: The Best Of The Two Worlds!, Mohammed Essa Fawzy Essa
Theses and Dissertations
Machine learning (ML) has become ubiquitous, integrating into numerous real-life applications. However, meeting the computational demands of ML systems is challenging, as existing computing platforms are constrained by memory bandwidth, and technology scaling no longer yields substantial improvements in system performance. This work introduces novel hardware architectures to accelerate ML workloads, addressing both compute and memory challenges. In the compute domain, we explore various approximate computing techniques to assess their efficacy in accelerating ML computations. Subsequently, we propose the Approximate Tensor Processing Unit (APTPU), a hardware accelerator that utilizes approximate processing elements to replace direct quantization of inputs and weights …
Uav-Based Tracking And Following Of Railroad Lines, Keith Michael Lewandowski
Uav-Based Tracking And Following Of Railroad Lines, Keith Michael Lewandowski
Theses and Dissertations
Given the pivotal role of the railroad industry in modern transportation and the potential risks associated with track malfunctions, the inspection and maintenance of railroad tracks emerges as a critical concern. While existing solutions excel in performing accurate measurements and detection, they often rely on large, expensive, and time-consuming platforms for inspections. This project, however, seeks to solve the same problem with the use of an unmanned aerial vehicle (UAV), significantly reducing time and cost while maintaining detection capabilities. In particular, this solution is ideal for large-scale, high-level inspections following major events such as floods [6], hurricanes [7] or earthquakes …
Ga-Gesrgan: Document Images Super Resolution Using Gabor Filters, Esrgan Models And Genetic Algorithms, Zakia Kezzoula, Djamel Gaceb, Ayoub Titoun
Ga-Gesrgan: Document Images Super Resolution Using Gabor Filters, Esrgan Models And Genetic Algorithms, Zakia Kezzoula, Djamel Gaceb, Ayoub Titoun
Emirates Journal for Engineering Research
In the last decade, we have witnessed significant progress in image super-resolution, thanks in particular to the emergence and improvement of deep learning models, which can adapt to the complexity of tasks and improve image quality. This article presents a novel approach to image super-resolution GA-GESRGAN to enhance the quality of document images through a multi-step methodology. Initially, document images are processed using Gabor filters to extract features across various spatial frequencies. These extracted features are then utilized to train Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) models. Once improved images are obtained through ESRGAN models, a Genetic Algorithm combines these …
Parallax-Tolerant Image Stitching With Geometric Structure Protection For Unmanned Ship Visual Perception, Zhilin Yang, Yong Yin, Rukai Zhang, Qianfeng Jing, Sen Jiang, Wenfeng Zhu
Parallax-Tolerant Image Stitching With Geometric Structure Protection For Unmanned Ship Visual Perception, Zhilin Yang, Yong Yin, Rukai Zhang, Qianfeng Jing, Sen Jiang, Wenfeng Zhu
Journal of System Simulation
Abstract: In order to address the artifacts caused by misalignment in maritime image stitch with low texture and large parallax, a geometric structure parallax-tolerant image stitch algorithm based on point-line feature registration and optimal seam fusion is proposed. Line segment features are introduced into the traditional homography transformation based on point features, and potential coplanar local line segments are merged into global line segments to provide accurate alignment conditions for seam line fusion. In the image fusion stage, the energy function of seam cutting method is designed by using the color difference and gradient difference of tanh measure and introducing …
An Improved Cat Swarm Optimization For Heterogeneous Multiple Mobile Robots, Liang Kang, Yi Du, Lihua Yin
An Improved Cat Swarm Optimization For Heterogeneous Multiple Mobile Robots, Liang Kang, Yi Du, Lihua Yin
Journal of System Simulation
Abstract: At present, it is difficult to achieve the real homogeneity of the members of the multiple mobile robots. The existing swarm intelligence algorithm is also difficult to accommodate the heterogeneity of the team. Focusing on the heterogeneous cooperation of multiple mobile robots, the concept of mother and child robots is proposed. In order to realize the application of swarm intelligence algorithm in heterogeneous multiple mobile robots, the basic cat swarm algorithm is improved. The subdomain and neighborhood of cat swarm are defined, and eight improvements of cat swarm algorithm are proposed, including priority of search direction, extended trajectory tracking …
Research On Path Optimization Algorithm In Dynamic Routing Environment, Xin Xie, Xiaobing Hu, Hang Zhou
Research On Path Optimization Algorithm In Dynamic Routing Environment, Xin Xie, Xiaobing Hu, Hang Zhou
Journal of System Simulation
Abstract: In a real dynamic routing environment, static path optimization (SPO) and traditional dynamic path optimization (DPO) tend to encounter issues such as detours, reversals and high computational complexity due to frequent real-time optimization calculation. To address these problems, a novel restart co-evolutionary path optimization (RCEPO) method based on the ripple-spreading algorithm (RSA) is proposed. This method integrates the path optimization process with the dynamic changes of the routing network environment to enhance the effectiveness of path optimization. Moreover, the path reoptimization calculation is performed only when the dynamic changes in the routing environment exceed the predicted range, thereby reducing …
High-Resolution Image Reconstruction Of Ect Region Of Interest Based On Finite Element Simulation, Lifeng Zhang, Da Chen
High-Resolution Image Reconstruction Of Ect Region Of Interest Based On Finite Element Simulation, Lifeng Zhang, Da Chen
Journal of System Simulation
Abstract: High-resolution image reconstruction of interest region is one of the research hotspots of electrical capacitance tomography (ECT) technology. The ECT model with uniform electrode distribution only has a high sensitivity coefficient at the boundary position of the reconstructed field and is not suitable for imaging regions of interest. In order to improve the sensitivity distribution in the region of interest and improve image resolution, a high-resolution image reconstruction method of ECT region of interest based on finite element simulation is proposed, and the electrode distribution is optimized according to the conformal transformation theory. Simulation experiments are conducted, and the …
Fusion Of Improved A* And Dynamic Window Approach For Mobile Robot Path Planning, Rongshen Lai, Lei Dou, Zhiyong Wu, Shuai Sun
Fusion Of Improved A* And Dynamic Window Approach For Mobile Robot Path Planning, Rongshen Lai, Lei Dou, Zhiyong Wu, Shuai Sun
Journal of System Simulation
Abstract: The traditional A* algorithm is computationally simple and has short planning paths, but it still suffers from redundancy of inflection points, low search efficiency and zigzagging planning paths. Aiming at the above problems, a fusion algorithm combining the improved A* algorithm and the improved dynamic window approach is proposed for the path planning of mobile robots. For the problem of redundant inflection points, the key nodes are extracted to effectively remove the useless inflection points; for the problem of low search efficiency, a dynamic weighting factor is introduced into the heuristic function of the evaluation function, which changes the …
A Highly Robust Target Tracking Algorithm Merging Cnn And Transformer, Peijin Liu, Xuefeng Fu, Haofeng Sun, Lin He, Shujie Liu
A Highly Robust Target Tracking Algorithm Merging Cnn And Transformer, Peijin Liu, Xuefeng Fu, Haofeng Sun, Lin He, Shujie Liu
Journal of System Simulation
Abstract: To address the performance degradation of target tracking algorithms caused by target object deformation, scale variation, fast motion, and occlusion, a highly robust target tracking algorithm that Merging a CNN and Transformer is proposed based on siamese network architecture. In the feature extraction stage, standard convolutions are employed to extract shallow local feature information, while a convolution-like Transformer module is designed in the deep network to model global information. The pixel values in the Transformer are computed using a sliding window significantly reducing computational complexity. In the feature aggregation stage, a multi-head cross-attention module is utilized to construct a …
Hybrid Evolutionary Multi-Objective Optimization Algorithm For Vehicle Routing Problem With Simultaneous Delivery And Pickup, Wenqiang Zhang, Xiaomeng Wang, Xiaoxiao Zhang, Guohui Zhang
Hybrid Evolutionary Multi-Objective Optimization Algorithm For Vehicle Routing Problem With Simultaneous Delivery And Pickup, Wenqiang Zhang, Xiaomeng Wang, Xiaoxiao Zhang, Guohui Zhang
Journal of System Simulation
Abstract: In order to provide reasonable and effective decision support for logistics enterprises in vehicle distribution route planning, a hybrid evolutionary multi-objective optimization algorithm combining a multi-region mixed-sampling strategy for global search and a local search based on individual route sequence differences is proposed for the problem. A reasonable mathematical model is constructed and the global search strategy is used to make the population individuals to converge quickly to the Pareto front from multiple directions, and the local search strategy is employed to guide the poorly performing individuals in the population to evolve towards the direction of better performing individuals, …
Gpu Parallel Acceleration Framework For Heuristic Optimization Algorithm, Dongjie Wang, Sixin Wen, Wanzhi Meng, Di Wu
Gpu Parallel Acceleration Framework For Heuristic Optimization Algorithm, Dongjie Wang, Sixin Wen, Wanzhi Meng, Di Wu
Journal of System Simulation
Abstract: Heuristic optimization algorithm are a type of algorithm that uses large-scale populations for iterative calculations and are widely used to solve all kinds of complex optimization problems. However, such algorithm have the disadvantages of large calculation and long time consumption. To solve this problem, heuristic optimization algorithms are parallelized using GPU and compute unified device architecture (CUDA) to substantially improve computational efficiency. A GPU parallel framework for heuristic optimization algorithm is proposed, which designs an information interaction framework and algorithm parallel optimization strategy with a parallel logical structure, and solves the problem of the dissimilarity of the logical structure …
Estimation Of The Berthing Parameter Of Unmanned Surface Vessels Based On 3d Lidar, Haichao Wang, Yong Yin, Qianfeng Jing, Lin Cong
Estimation Of The Berthing Parameter Of Unmanned Surface Vessels Based On 3d Lidar, Haichao Wang, Yong Yin, Qianfeng Jing, Lin Cong
Journal of System Simulation
Abstract: Accurate estimation of berthing parameters is a prerequisite for unmanned surface vessel autonomous berthing. A method for berthing parameter estimation is proposed based on shipborne 3D LiDAR. The method consists of two main modules: ship pose estimation and berthing state estimation. In the berthing position estimation module, raw point cloud data undergoes preprocessing algorithms aims at downsampling and removing outliers. Point cloud registration algorithms are employed to determine the vessel's position during the berthing process. The berthing state estimation module extracts berth boundary information by using the MSAC algorithm, and on the basis of this information, calculates the berthing …
Research On Virtual Simulation Testing Technology For Intelligent Navigation Collision Avoidance Decision-Making And Planning, Jialun Liu, Fan Yang, Lingli Xie, Shijie Li, Tengfei Wang
Research On Virtual Simulation Testing Technology For Intelligent Navigation Collision Avoidance Decision-Making And Planning, Jialun Liu, Fan Yang, Lingli Xie, Shijie Li, Tengfei Wang
Journal of System Simulation
Abstract: This paper studies virtual simulation testing technology for intelligent navigation collision avoidance decision-making and planning. The application requirements of intelligent navigation in cargo ships are introduced, and the current research status of collision avoidance decision-making strategies, path planning algorithms and decision-making planning testing technology are analyzed. For the intelligent navigation collision avoidance decision-making and planning capabilities of cargo ships, an intelligent navigation collision avoidance decision-making and planning algorithm is proposed based on the encounter situation division in the collision avoidance rules, combined with the quaternary ship field and Bezier curve interpolation theory. A simulation testing method for decision-making planning …
Digital Twin Method Of Stress Field Of Deep Submersible Spherical Shell Based On Simulation Database, Yu Cao, Jie Li, Fang Wang, Zhixiang Liu, Xueliang Wang
Digital Twin Method Of Stress Field Of Deep Submersible Spherical Shell Based On Simulation Database, Yu Cao, Jie Li, Fang Wang, Zhixiang Liu, Xueliang Wang
Journal of System Simulation
Abstract: This paper presents a method for predicting the stress field of deep diving spherical shells based on simulation databases and digital twin technology. By establishing simulation databases of stress field distribution of pressure-resistant spherical shells under different scales and loads, virtual sensing monitoring of stress states in other parts of the vessel is realized through finite sensor layout of pressureresistant shells on the submersible. Based on the DT(digital twin) technology, a three-level virtual structure layer is constructed. The Level-1 DT layer realizes the spatial mapping and cloud image display from the finite element simulation model to the digital model. …
Parametric Identification Of Ship Maneuvering Motion Response Model Based On Square Root Cubature Kalman Filtering, Qinghao Li, Junsheng Ren, Yan Hua
Parametric Identification Of Ship Maneuvering Motion Response Model Based On Square Root Cubature Kalman Filtering, Qinghao Li, Junsheng Ren, Yan Hua
Journal of System Simulation
Abstract: The system identification algorithm, based on the square root cubature Kalman filter (SRCKF) is proposed to address issues such as low accuracy, poor robustness, and weak generalization ability encountered by the extended Kalman filter (EKF) algorithm in parameters identification of ship maneuvering motion models. This algorithm, within the framework of CKF, replaces the original covariance matrix with its root mean square and utilizes triangular decomposition for prediction and update to enhance identification stability. The EKF is used as a comparison algorithm to identify the parameters of the second-order nonlinear response model of a ship with rudder angles that comply …
Section Point Cloud Denoising Method Based On Enhanced Dbscan And Distance Consensus Evaluation, Chengpeng Ge, Dong Zhao, Rui Wang, Qinghua Ma
Section Point Cloud Denoising Method Based On Enhanced Dbscan And Distance Consensus Evaluation, Chengpeng Ge, Dong Zhao, Rui Wang, Qinghua Ma
Journal of System Simulation
Abstract: A denoising method based on the improved DBSCAN(density-based spatial clustering of applications with noise) algorithm is proposed to address the problem of removing noise points in point cloud data. The statistical filtering method is applied to pre-screen isolated outliers and remove largescale noise from the point cloud. The DBSCAN algorithm is optimized to reduce computational time complexity and achieve adaptive parameter adjustment, thereby dividing the point cloud into normal clusters, suspected clusters and abnormal clusters, and immediately removing abnormal clusters. Distance consensus assessment is applied, and suspect clusters are further evaluated. By calculating the distance between the suspected point …
A Method Based On Deep Learning For Assisting Sins/Dvl Integrated Navigation, Xinghong Kuang, Aowei Huang
A Method Based On Deep Learning For Assisting Sins/Dvl Integrated Navigation, Xinghong Kuang, Aowei Huang
Journal of System Simulation
Abstract: The navigation and positioning accuracy of an Autonomous Underwater Vehicle (AUV) affects the efficiency of the AUV to a certain extent, and since GNSS cannot be used underwater, the integrated navigation system of Strapdown Inertial Navigation System/ Doppler Velocity Log (SINS/DVL) has been widely favored. However, DVL will fail in some cases, and if DVL is isolated directly, the system will become a pure inertial navigation system, which seriously affects the accuracy of navigation and positioning. In order to cope with the situation that DVL is missing in some beams, a DLinear-Informer assisted integrated navigation algorithm is proposed. Through …
Dynamic Data Driven Simulation: An Overview, Xu Xie, Xiaogang Qiu, Yizheng Bao, Kai Xu
Dynamic Data Driven Simulation: An Overview, Xu Xie, Xiaogang Qiu, Yizheng Bao, Kai Xu
Journal of System Simulation
Abstract: Dynamic data driven simulation is a simulation paradigm which integrates simulation and data together. This paradigm continuously feeds real-time data into the simulation, enabling the simulation be dynamically adjusted by the data, which thus improves the simulation-based estimation and prediction capability. Due to this integration, the dynamic data driven simulation can estimate system states and predict future state evolution more accurately. This paper reviews the origins and basic concept of dynamic data driven simulation, and introduces several simulation paradigms originated from the idea of "integrating models with data", and identifies the linkages and differences among them. The particle filterbased …
Uav Dynamic Path Planning Algorithm Combined With Dynamic Window Approach, Bin Liu, Ying Lan, Wentao Huang, Qinqin Fan
Uav Dynamic Path Planning Algorithm Combined With Dynamic Window Approach, Bin Liu, Ying Lan, Wentao Huang, Qinqin Fan
Journal of System Simulation
Abstract: To solve the problem of the poor search for optimal performance and obstacle avoidance ability of path planning algorithms in complex dynamic environments, a UAV dynamic path planning algorithm combined with dynamic window approach (UAV-DPPA-DWA) is proposed. In the UAVDPPA- DWA algorithm, a novel elliptic tangent graph algorithm based on the evaluation of offset degree and obstacle distance is proposed to obtain the optimal guidance path for the UAV in static environments. If the UAV detects moving obstacles, a localized obstacle avoidance trajectory will be generated using the dynamic window method with adaptive parameters. Otherwise, the UAV will continue …
Study On Robust Chance Constrained Optimization Of Multi-Energy Supply System Based On Wind And Solar Power Combined Output Simulation, Zhe Bao, Wei Li, Xiaofang Zhang, Zongyuan An, Ye Xu
Study On Robust Chance Constrained Optimization Of Multi-Energy Supply System Based On Wind And Solar Power Combined Output Simulation, Zhe Bao, Wei Li, Xiaofang Zhang, Zongyuan An, Ye Xu
Journal of System Simulation
Abstract: In order to effectively avoid potential imbalance between supply and demand caused by the uncertainty of wind and solar power outputs, and promote the sustained development of the multi-energy supply system, a robust chance-constrained optimization model is developed for identifying optimal operation strategies under complexities and uncertainties through incorporating Copula theory, chance-constrained programming, and robust programming within a general framework. The results show that this model can not only accurately characterize the distribution probability of combined outputs of wind and solar power and formulate the operational strategies under low default risk conditions, but also reduce the proportion of highrisk …
Digital Twin-Driven Structural Thermal Deformation Compensation System For Radio Telescopes, Zhen Lei, Yuhua Liu, Kai Ding, Haoxiang Chen, Dongwei Li
Digital Twin-Driven Structural Thermal Deformation Compensation System For Radio Telescopes, Zhen Lei, Yuhua Liu, Kai Ding, Haoxiang Chen, Dongwei Li
Journal of System Simulation
Abstract: The structural thermal deformation of large-scale radio telescopes under solar thermal load cannot be measured in real-time and compensated dynamically. To solve this problem, a digital twin-driven structural thermal deformation compensation method and system is studied. Based on the fusion of measured data and simulation data, a temperature field modeling method is proposed. A simulation and prediction model of structural thermal deformation is established, and a dynamic compensation model of structural thermal deformation is built. A digital twin-driven dynamic structural thermal deformation compensation system for radio telescopes is developed. A micro-experimental model is studied to verify the effectiveness of …
Research On Digital Twins Technology In Cyberspace Security, Qiankun Ren, Xinli Xiong, Jingju Liu, Qian Yao
Research On Digital Twins Technology In Cyberspace Security, Qiankun Ren, Xinli Xiong, Jingju Liu, Qian Yao
Journal of System Simulation
Abstract: Combined with digital twins and cyber-space modeling and simulation, the network digital twins (NDT) technology with in-deep research can enable the development of diverse techniques of cyber security. The basic concept and research history of NDT are summarized, and a taxonomy is proposed to survey applications of NDT. A cyber security-oriented network digital twin model (CyS-NDT) is concluded through the literature. The relationship between the internal security problem of NDT and the method of enabling network security technology is discussed to prospect further challenges and opportunities.
Faids: Artificial Intelligence Developmental Systems Framework For Predicting And Preventing Cyberattacks In Supply Chain Networks, Lordt Becklines
Faids: Artificial Intelligence Developmental Systems Framework For Predicting And Preventing Cyberattacks In Supply Chain Networks, Lordt Becklines
Research & Publications
Cyber threats and attacks disrupt and damages supply chain networks (SCNs), which are complex and interlinked. Current methods to predict and prevent cyberattacks are inadequate and ineffective. This research proposes an AI developmental systems framework (FAIDS) to protect SCNs from cyberattacks. The framework has four components: (1) an AI threat intelligence system; (2) an AI risk assessment system; (3) an AI decision support system; and (4) an AI learning and adaptation system. The framework is tested on a simulated retail SCN. The results show that the framework can predict and prevent cyberattacks and improve the network's resilience and security. The …
Synthetic Data Generation For Accurate, Fair, And Private Recommender Systems, Derek B. Lilienthal
Synthetic Data Generation For Accurate, Fair, And Private Recommender Systems, Derek B. Lilienthal
Master's Theses
A notorious challenge for recommender systems on online platforms is to accurately and fairly recommend items that align with users’ preferences while retaining user privacy. These systems often rely on historical data that is partially labeled and includes incomplete user information, leading to the over-representation of majority groups and disproportionately favoring popular items. Substituting user data with synthetic data can address these concerns, but accurately replicating real-world datasets has been a challenging problem. Recent advancements in generative AI have demonstrated the impressive capabilities of diffusion models in generating realistic data across several domains. In this work, we introduce two variations …
Analyzing The Usability, Performance, And Cost-Efficiency Of Deploying Ml Models On Various Cloud Computing Platforms, Hongyu Wang
Analyzing The Usability, Performance, And Cost-Efficiency Of Deploying Ml Models On Various Cloud Computing Platforms, Hongyu Wang
Masters Theses (Archived)
With the enhanced computing capabilities and accessibility to cloud resources, major cloud computing providers such as Google Cloud Platform (GCP), Amazon Web Services (AWS), and Microsoft Azure offer Machine Learning (ML) and AI services. Their primary purpose is to provide efficiency, scalability, and adaptability in modern software development and IT operations while reducing overall costs and operational complexity. However, prospective customers of the services often question which ML-AI service will best suit their organizational and business needs. This study compares and analyzes the usability, performance, and cost-efficiency of deploying Machine Learning (ML) models across three cloud platforms: GCP, AWS, and …
Controllable Physics-Informed Latent Space For Acoustic Wave Manipulation, Tristan Shah
Controllable Physics-Informed Latent Space For Acoustic Wave Manipulation, Tristan Shah
Master's Theses
Partial Differential Equations (PDEs) are foundational descriptive tools in scientific and engineering disciplines, enabling the exploration of phenomena from material design to climate dynamics and the propagation of pandemics and wildfires. Establishing control of PDEs, especially when the underlying phenomena are only partially observable, holds great potential for fostering technological innovations. This ambition faces considerable obstacles, as a robust sample-based solution remains elusive. Addressing this challenge, this thesis introduces a framework leveraging physics-informed machine learning (ML) to enable control of PDEs. Central to this framework is an agent, equipped with sensors to perceive its environment, that generates a low-dimensional physics-informed …
An Exploration Of Dimensionality Reduction Of Dynamics On Lie Groups Via Structure-Aware Canonical Correlation Analysis, Wooyoung Chung
An Exploration Of Dimensionality Reduction Of Dynamics On Lie Groups Via Structure-Aware Canonical Correlation Analysis, Wooyoung Chung
Master's Theses
Incorporating prior knowledge into a data-driven modeling problem can drastically improve performance, reliability, and generalization outside of the training sample. The stronger the structural properties, the more effective these improvements become. Manifolds are a powerful nonlinear generalization of Euclidean space for modeling finite dimensions. When additionally assuming that the manifold carries (Lie) group structure, this imposes a drastically stricter global constraint. The range of their applications is very wide and includes the important case of robotic tasks. We apply this idea to Canonical Correlation Analysis (CCA). In traditional CCA one constructs a hierarchical sequence of maximal correlations of up to …
Reinforcement Learning Based Planning And Control For Robotic Source Seeking Inspired By Fruit Flies, Gauravkumar Koradiya
Reinforcement Learning Based Planning And Control For Robotic Source Seeking Inspired By Fruit Flies, Gauravkumar Koradiya
Master's Theses
In the exploration of mobile sensing agents such as robots equipped with sensors, these agents have the potential to measure unknown scalar fields, ranging from chemical concentrations to temperature fluctuations. One of the primary interests is the robots’ ability to gravitate towards the peaks or troughs of these fields, a phenomenon termed as “source seeking”. This behavior is invaluable in practical scenarios, like detecting chemical leaks or locating survivors after catastrophes. However, the challenge becomes significantly more pronounced when operating in turbulent flow fields, where the erratic nature of odorant plumes makes predictions and simulations highly unreliable. Interestingly, fruit flies …
Domain Specific Aspect Extraction For Product Design, Divyam Sobti
Domain Specific Aspect Extraction For Product Design, Divyam Sobti
Master's Theses
As technology advances, computers become increasingly proficient at interpreting and translating human language into machine-understandable text. With the help of algorithms in natural language processing (NLP), machines can now translate textual data. These algorithms help identify and extract specific text components known as aspects. The aspects represent specific attributes or topics within textual data. For instance, an Amazon review states,“ This phone has good battery life but poor camera quality,” and attributes like ‘battery life’ and ‘camera quality’ represent aspects in the text. Aspect extraction is a pivotal process involving identifying and isolating key features or topics within text. This …
Healthcare Assistance Challenges-Driven Neurosymbolic Ai, Kaushik Roy
Healthcare Assistance Challenges-Driven Neurosymbolic Ai, Kaushik Roy
Publications
Although Artificial Intelligence technology has proven effective in providing healthcare assistance by analyzing health data, it still falls short in supporting decision-making. This deficiency largely stems from the predominance of opaque neural networks, particularly in mental health care AI applications, which raise concerns about their unpredictable and unverifiable nature. This skepticism hinders the transition from information support to decision support. This presentation will explore neurosymbolic approaches that combine neural networks with symbolic control and verification mechanisms. These approaches aim to unlock AI’s full potential by enhancing information analysis and decision-making support for healthcare assistance.