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Cascaded PFLANN Model for Intelligent Health informatics in Detection of Respiratory Diseases from Speech Using Bio-inspired Computation

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Journal of Artificial Intelligence and Technology 2024年第2期4卷 124-131页

作者： Jagannath Dayal Pradhan L.V.Narasimha Prasad Tusar Kanti Dash Manisha Guduri Ganapati Panda Electronics and Communications Engineering C V Raman Global UniversityBhubaneswarIndia Department of Computer Science and Engineering Institute of Aeronautical EngineeringHyderabadIndia School of Computing and Informatics University of LouisianaLafayetteLAUSA

Due to the recent developments in communications technology,cognitive computations have been used in smart healthcare techniques that can combine massive medical data,artificial intelligence,federated learning,bio-inspired computation,and the Internet of Medical *** has helped in knowledge sharing and scaling ability between patients,doctors,and clinics for effective treatment of ***-based respiratory disease detection and monitoring are crucial in this direction and have shown several promising *** the subject’s speech can be remotely recorded and submitted for further examination,it offers a quick,economical,dependable,and noninvasive prospective alternative detection ***,the two main requirements of this are higher accuracy and lower computational complexity and,in many cases,these two requirements do not correlate with each *** problem has been taken up in this paper to develop a low computational complexity-based neural network with higher accuracy.A cascaded perceptual functional link artificial neural network(PFLANN)is used to capture the nonlinearity in the data for better classification performance with low computational *** proposed model is being tested for multiple respiratory diseases,and the analysis of various performance matrices demonstrates the superior performance of the proposed model both in terms of accuracy and complexity.

关键词： AI-ML ASR FLANN health informatics neural network PFLANN

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A Variational Autoencoder-Feed Forward Networks (VAE-FNN) Model for Classifying DNA Barcodes 1

A Variational Autoencoder-Feed Forward Networks (VAE-FNN) Mo...

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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Yadav, Abhivyakti Divya, S. Oviya, I.R. Raja, Kalpana Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India School of Medicine Yale University Section for Biomedical Informatics and Data Science New HavenCT United States

ISBN: (纸本)9798350342796

DNA Barcodes, which are particular fragments derived from brief sections of DNA (such as mitochondrial, nuclear, and plastid sequences), can be used to identify organisms from the major life kingdoms. In addition to supporting conventional taxonomic techniques, DNA barcoding is a potent tool that advances our knowledge of species diversity and their ecological functions. On a variety of organisms, the use of this approach for species categorization has been successful. In this paper, we examine how DNA barcoding has been used to classify species based on DNA barcodes as well as other related research that has been done over the years on the subject. After experimenting with a number of deep learning models, we have propose a Variational Auto Encoder + Feed Forward Neural Network workflow for classifiying species using DNA barcodes. The models have been assessed on the basis of performance factors including accuracy, recall, and precision. COI, rbcL, matK, and ITS are the specific gene sections that have been identified as barcodes. For both simulated and real datasets, the model can attain an average accuracy of greater than 95 percent. This DNA barcoding approach has the ability to simplify DNA barcode-based species identification and serve as a tool for species categorization. © 2023 IEEE.

关键词： DNA

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A Mutual Information-Based Assessment of Reverse Engineering on Rewards of Reinforcement Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1089-1100页

作者： Chen, Tong Liu, Jiqiang Baker, Thar Wu, Yalun Xiang, Yingxiao Li, Yike Niu, Wenjia Tong, Endong Zomaya, Albert Y. Beijing Jiaotong University Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100044 China College of Computing and Informatics University of Sharjah Department of Computer Science Sharjah27272 United Arab Emirates The University of Sydney School of Computer Science Sydney2006 Australia

Rewards are critical hyperparameters in reinforcement learning (RL), since in most cases different reward values will lead to greatly different performance. Due to their commercial value, RL rewards become the target of reverse engineering by the inverse reinforcement learning (IRL) algorithm family. Existing efforts typically utilize two metrics to measure the IRL performance: the expected value difference (EVD) and the mean reward loss (MRL). Unfortunately, in some cases, EVD and MRL can give completely opposite results, due to MRL focusing on whole state-space rewards, while EVD only considering partly sampled rewards. Such situation naturally rises to one fundamental question: whether current metrics and assessment are sufficient and accurate for more general use. Thus, in this article, based on the metric called normalized mutual information of reward clusters (C-NMI), we propose a novel IRL assessment;we aim to fill this research gap by considering a middle-granularity state space between the entire state space and the specific sampling space. We utilize the agglomerative nesting algorithm (AGNES) to control dynamical C-NMI computing via a fourth-order tensor model with injected manipulated trajectories. With such a model, we can uniformly capture different-dimension values of MRL, EVD, and C-NMI, and perform more comprehensive and accurate assessment and analyses. Extensive experiments on several mainstream IRLs are experimented in object world, hence revealing that the assessing accuracy of our method increases 110.13% and 116.59%, respectively, when compared with the EVD and MRL. Meanwhile, C-NMI is more robust than EVD and MRL under different demonstrations. © 2020 IEEE.

关键词： Reverse engineering

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Crystal structure guided machine learning for the discovery and design of intrinsically hard materials

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Journal of Materiomics 2022年第3期8卷 678-684页

作者： Russlan Jaafreh Tamer Abuhmed Jung-Gu Kim Kotiba Hamad School of Advanced Materials Science&Engineering Sungkyunkwan UniversitySuwon16419South Korea College of Computing and Informatics Sungkyunkwan UniversitySuwon16419South Korea

In this work,a machine learning(ML)model was created to predict intrinsic hardness of various compounds using their crystal *** this purpose,an initial dataset,containing the hardness values of 270 compounds and counterpart applied loads,was employed in the learning *** on various features generated using crystal information,an ML model,with a high accuracy(R^(2)=0.942),was built using extreme gradient boosting(XGB)*** validations conducted by hardness measurements of various compounds,including MSi_(2)(M=Nb,Ce,V,and Ta),Al_(2)O_(3),and FeB_(4),showed that the XGB model was able to reproduce load-dependent hardness behaviors of these *** addition,this model was also used to predict the behavior based on prototype crystal structures that are randomly substituted with elements.

关键词： Machine learning XGB algorithm Intrinsic hardness Crystal chemistry OQMD ICSD MP

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INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging 23

INCREASE: Inductive Graph Representation Learning for Spatio...

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2023 World Wide Web Conference, WWW 2023

作者： Zheng, Chuanpan Fan, Xiaoliang Wang, Cheng Qi, Jianzhong Chen, Chaochao Chen, Longbiao Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Informatics Xiamen University Xiamen China School of Computing and Information Systems University of Melbourne Melbourne Australia College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9781450394161

Spatio-temporal kriging is an important problem in web and social applications, such as Web or Internet of Things, where things (e.g., sensors) connected into a web often come with spatial and temporal properties. It aims to infer knowledge for (the things at) unobserved locations using the data from (the things at) observed locations during a given time period of interest. This problem essentially requires inductive learning. Once trained, the model should be able to perform kriging for different locations including newly given ones, without retraining. However, it is challenging to perform accurate kriging results because of the heterogeneous spatial relations and diverse temporal patterns. In this paper, we propose a novel inductive graph representation learning model for spatio-temporal kriging. We first encode heterogeneous spatial relations between the unobserved and observed locations by their spatial proximity, functional similarity, and transition probability. Based on each relation, we accurately aggregate the information of most correlated observed locations to produce inductive representations for the unobserved locations, by jointly modeling their similarities and differences. Then, we design relation-aware gated recurrent unit (GRU) networks to adaptively capture the temporal correlations in the generated sequence representations for each relation. Finally, we propose a multi-relation attention mechanism to dynamically fuse the complex spatio-temporal information at different time steps from multiple relations to compute the kriging output. Experimental results on three real-world datasets show that our proposed model outperforms state-of-the-art methods consistently, and the advantage is more significant when there are fewer observed locations. Our code is available at https://***/zhengchuanpan/INCREASE. © 2023 ACM.

关键词： Kriging

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AGCL: Aspect Graph Construction and Learning for Aspect-level Sentiment Classification 31

AGCL: Aspect Graph Construction and Learning for Aspect-leve...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Jian, Zhongquan Wu, Daihang Wang, Shaopan Wang, Yancheng Yao, Junfeng Wang, Meihong Wu, Qingqiang Institute of Artificial Intelligence Xiamen University China School of Informatics Xiamen University China School of Film Xiamen University China College of Management Mahidol University Thailand School of Computing and Data Science Xiamen University Malaysia Malaysia Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University China Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Xiamen University China

ISBN: (纸本)9798891761964

Prior studies on Aspect-level Sentiment Classification (ALSC) emphasize modeling interrelationships among aspects and contexts but overlook the crucial role of aspects themselves as essential domain knowledge. To this end, we propose AGCL, a novel Aspect Graph Construction and Learning method, aimed at furnishing the model with finely tuned aspect information to bolster its task-understanding ability. AGCL's pivotal innovations reside in Aspect Graph Construction (AGC) and Aspect Graph Learning (AGL), where AGC harnesses intrinsic aspect connections to construct the domain aspect graph, and then AGL iteratively updates the introduced aspect graph to enhance its domain expertise, making it more suitable for the ALSC task. Hence, this domain aspect graph can serve as a bridge connecting unseen aspects with seen aspects, thereby enhancing the model's generalization capability. Experiment results on three widely used datasets demonstrate the significance of aspect information for ALSC and highlight AGL's superiority in aspect learning, surpassing state-of-the-art baselines greatly. Code is available at https://***/jian-projects/agcl. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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On the Performance Evaluation of Mobility Model-Based GPSR Routing Protocol in Flying Ad hoc Networks 6

On the Performance Evaluation of Mobility Model-Based GPSR R...

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6th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2024

作者： Bezziane, Mohamed Ben Sahraoui, Yesin Brik, Bouziane Mekkas, Lina Bougeurra, Sara Khaldi, Anouar University Kasdi Merbah Computer Science And Information Technologies Department Ouargla Algeria National Higher School of Artificial Intelligence Computer Science Algiers Algeria University of Sharjah College of Computing And Informatics Computer Science Department Sharjah United Arab Emirates

ISBN: (纸本)9798350350265

In recent times, we have witnessed the rapid and remarkable evolution of communication technologies, particularly in the domain of Flying Ad Hoc Networks (FANETs). This evolution has necessitated a thorough evaluation and significant enhancement of routing protocols to improve the overall performance of the system. To address the challenge of efficiently routing data in FANETs, where conventional methods may fall short during dynamic scenarios, the need for an effective framework becomes paramount in addressing these challenges. This paper aims to optimize routing efficiency in the context of FANETs, evaluating the performance of Greedy Perimeter Stateless Routing (GPSR) routing protocol under various mobility models, including Random Waypoint Mobility (RWPM), Mass Mobility, and Gauss Markov mobility models, to uncover their strengths and limitations. The simulation results reveal notable variations in performance indicators, including End-to-End delay, Packet Delivery Ratio, and Loss rate across various mobility models. However, Mass Mobility and Gauss-Markov Mobility exhibited consistent increases in Energy Consumption as the UAV count rose. On the other hand, Random Way Point Mobility demonstrated higher energy consumption compared to the former models. These findings offer valuable insights for selecting the most suitable mobility model for specific applications, and improving routing protocols, thereby advancing the reliability and effectiveness of Flying Ad Hoc environments. © 2024 IEEE.

关键词： Routing protocols

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Gaussian Backbone-Based Spherical Evolutionary Algorithm with Cross-search for Engineering Problems

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Journal of Bionic Engineering 2024年第2期21卷 1055-1091页

作者： Yupeng Li Dong Zhao Ali Asghar Heidari Shuihua Wang Huiling Chen Yudong Zhang College of Computer Science and Technology Changchun Normal UniversityChangchun130032JilinChina School of Surveying and Geospatial Engineering College of EngineeringUniversity of TehranTehranIran School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Biological Sciences Xi’an Jiaotong-Liverpool UniversitySuzhou215123JiangsuChina Key Laboratory of Intelligent Informatics for Safety and Emergency of Zhejiang Province Wenzhou UniversityWenzhou325035China School of Computer Science and Engineering Southeast UniversityNanjing210096China

In recent years,with the increasing demand for social production,engineering design problems have gradually become more and more *** novel and well-performing meta-heuristic algorithms have been studied and developed to cope with this *** them,the Spherical Evolutionary Algorithm(SE)is one of the classical representative methods that proposed in recent years with admirable optimization ***,it tends to stagnate prematurely to local optima in solving some specific ***,this paper proposes an SE variant integrating the Cross-search Mutation(CSM)and Gaussian Backbone Strategy(GBS),called *** this study,the CSM can enhance its social learning ability,which strengthens the utilization rate of SE on effective information;the GBS cooperates with the original rules of SE to further improve the convergence effect of *** objectively demonstrate the core advantages of CGSE,this paper designs a series of global optimization experiments based on IEEE CEC2017,and CGSE is used to solve six engineering design problems with *** final experimental results fully showcase that,compared with the existing well-known methods,CGSE has a very significant competitive advantage in global tasks and has certain practical value in real ***,the proposed CGSE is a promising and first-rate algorithm with good potential strength in the field of engineering design.

关键词： Meta-heuristic algorithms Engineering optimization Spherical evolution algorithm Global optimization

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Early fire danger monitoring system in smart cities using optimization-based deep learning techniques with artificial intelligence

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Journal of Reliable Intelligent Environments 2024年第2期10卷 197-210页

作者： Reddy, P. Dileep Kumar Margala, Martin Shankar, S. Siva Chakrabarti, Prasun Telangana Secunderabad India School of Computing and Informatics University of Louisiana at Lafayette Lafayette United States Department of CSE KG Reddy College of Engineering and Technology Telangana Hyderabad501504 India Department of Computer Science and Engineering Sir Padampat Singhania University Rajasthan Udaipur313601 India

One primary safety concern for smart cities is fire. Traditional techniques are not appropriate because of their high false alarm rates, delayed characteristics, and susceptibility in situations with heritage buildings. Smart cities must develop sophisticated methods to mitigate the severe effects of fires and achieve early fire detection in real time. An artificial intelligence-based recurrent neural network with a whale optimization framework (AI-RNN-WO) was introduced to estimate the risk of fire hazards early on. IoT sensor devices are first deployed in smart cities to continuously monitor environmental parameters such as temperature, smoke, flame, relative humidity, fuel moisture, and duff moisture code. These sensed data are then saved in the cloud storage system Firebase. Then, the sensed dataset is updated to the designed model, which pre-processes the data and extracts relevant features from the dataset. The RNN parameters are tuned using whale optimization, which improves the prediction results and attains better accuracy. The performance of the proposed AI-RNN-WO model is validated using a MATLAB tool, and the performance is compared with existing models. The produced model has demonstrated its effectiveness by attaining the highest accuracy (99.5%) and lowest error rate (0.1%). © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Recurrent neural networks

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Comparing Impact of Time Lag and Item Lag in Relative Judgment of Recency 44

Comparing Impact of Time Lag and Item Lag in Relative Judgme...

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44th Annual Meeting of the Cognitive science Society: Cognitive Diversity, CogSci 2022

作者： Maini, Sahaj Singh Labuzienski, Louis Gulati, Saurabh Tiganj, Zoran Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States

Many memory models suggest self-terminating backward scanning along a memory representation. In these models, time to retrieve a particular item from memory could depend on how far in the past the item was presented or on the number of items presented since that item. To investigate which of these two types of memory representation is more likely, we designed a relative Judgment of Recency (JOR) task with variable presentation rates. The variable presentation rate deconfounded the age of memory and the number of intervening items. Our results favor the hypothesis that memory representation is temporally organized. This result is important for advancing memory models and for building stronger ties between cognitive and neural models of memory. © 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY)

关键词： Judgment of Recency Time versus items Variable presentation time

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