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Adaptive Route optimization for Mobile Robot Navigation using Evolutionary Algorithm 3

Adaptive Route Optimization for Mobile Robot Navigation usin...

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3rd IEEE International conference on Artificial Intelligence in Engineering and Technology, IICAIET 2021

作者： Lim, Kit Guan Lee, Yoong Hean Tan, Min Keng Yoong, Hou Pin Wang, Tienlei Teo, Kenneth Tze Kin Modelling Simulation Computing Laboratory Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Malaysia Wuyi University Department of Intelligent Manufacturing Jiangmen China

ISBN: (纸本)9781665428996

As technologies are advancing, demand for an intelligent mobile robot also increases. In autonomous robot design, the main problem faced by researchers is the path planning of mobile robot. Various kind of path planning algorithm was introduced in the past, but no algorithm has absolute superior towards the others algorithm. Classical methods like artificial potential field, grid search, and visual method have been easily overtaken by artificial intelligence due to its adaptability and ability to learn from the past mistakes or experience. For example, Ant Colony optimization (ACO) is an optimization algorithm based on swarm intelligence which is widely used to solve path planning problem. However, the performance of ACO is highly dependent on the selection of its parameters. In this paper, the proposed adaptive ACO introduced two different ants, namely abnormal ant and random ant into the normal ACO to increase its global search ability and reduce the high convergence rate of ACO. Conventional ACO and adaptive ACO are compared in this paper and the results showed that adaptive ACO has better performance than conventional ACO in path planning. © 2021 IEEE.

关键词： Motion planning

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Proceedings - 2022 3rd International conference on Computer Science and Management Technology, ICCSMT 2022

Proceedings - 2022 3rd International Conference on Computer ...

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3rd International conference on Computer Science and Management Technology, ICCSMT 2022

ISBN: (纸本)9798350320404

The proceedings contain 111 papers. The topics discussed include: design of food safety management information system for western restaurant;research on enterprises’ response to emergencies based on machine learning;design and application of crop variety recommendation and production decision system in agricultural production;research on network structure analysis of English learning in social media;research on the prediction of e-commerce platform user churn based on random forest model;gazetteer enhanced named entity recognition for musical user-generated content;an application study of stochastic service systems based on operations search;risk early warning and response of PPP projects in characteristic towns based on fuzzy group decision- making and matter-element model;design and realization of an online examination system based on maven framework;design and implementation of dynamic interactive e-book system;optimization method for cloud service composition of machine tools under multiple constraints;application of environmental monitoring and control system in agricultural production;and design and implementation of warehouse management system based on Vuetify 3.0 framework.

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A Stable Stochastic optimization Algorithm for Triangulation of Bayesian Networks

A Stable Stochastic Optimization Algorithm for Triangulation...

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3rd International conference on Knowledge Discovery and Data Mining

作者： Dong, Xuchu Ouyang, Dantong Ye, Yuxin Feng, Shasha Yu, Haihong Jilin Univ Dept Comp Sci & Technol Changchun 130012 Jilin Peoples R China

ISBN: (纸本)9780769539232

In this paper, we present a novel deterministic heuristic and a new genetic algorithm to solve the problem of optimal triangulation of Bayesian networks. The heuristic, named MinFillWeight, aims to select variables minimizing the multiplication of the weights on nodes of fill-in edges. The genetic algorithm, named GA-MFW, uses a new rank-reserving crossover operator and a 2-fold mutation mechanism utilizing the MinFillWeight heuristic. Experiments on representative benchmark show that the deterministic heuristic and the stochastic algorithm have good performance and stability to various problems.

关键词： Bayesian networks clique tree heuristics genetic algorithm ant colony optimization

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The Implementation of an Artificially intelligent Personal Assistant for a Personal Computer 3

The Implementation of an Artificially Intelligent Personal A...

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3rd International conference on Advances in Mechanics Engineering (ICAME)

作者： Hsieh, Chun-Hsiang Buehrer, Daniel J. Natl Chung Cheng Univ Minxiong Taiwan

ISBN: (纸本)9783038352297

This research describes the design of an Artificially intelligent Personal Assistant (AIPA) system that uses concepts from the artificial neural networks, a triple-based knowledge representation that is used for both an ontology and its binary relationships, fast access via hash functions, and Class Algebra as the theoretical basis. The purpose of the research and implementation is to make it more convenient for people to use Chinese to communicate with computers. Through artificial intelligent natural language technology, not only are the users able to colloquially to communicate with AIPA, but the system can also identify the users' commands to bridge the Google search engine. Therefore, users can efficiently search for information or execute programs by using AIPA.

关键词： intelligent Assistant QA system Semantic Network Ontology Machine learning

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Predicting the Trajectory of AI Utilizing the Markov Model of Machine learning 3

Predicting the Trajectory of AI Utilizing the Markov Model o...

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3rd IEEE International conference on Computer Communication and Artificial Intelligence, CCAI 2023

作者： Isaac, Matilda Akinola, Olukunle Mobolaji Hu, Bintao Xi'an Jiaotong-Liverpool University School of Internet of Things Suzhou China

ISBN: (纸本)9798350335262

The next generation of Artificial Superintelligence (ASI) poses a variety of important societal problems, including the possible crises and upheavals of the AI machine, which could cause fundamental changes. As the discussion around Artificial Superintelligence underscores the importance of continual dialogue between man and its ability to control technology, it also raises the problem of designing intelligent interactive and collaborative tools and systems to allow this dialogue. Historically, the term 'AI' was used from 1950 to 1975, then fell out of favor during the' AI winter' from 1975 to 1995, and was narrowed to ANI (Artificial Narrow Intelligence). As a result, terms like 'Machine learning,' 'Natural language Processing,' and 'Data Science' were frequently mislabelled as AI. Today, AI has allowed clinicians to rely heavily on ML which is highly integrated with coding, billing, medical records, scheduling, contracting, medication ordering, and administrative functions. AI is now a thriving industry with massive capital investments and once again is on the verge of a great revolution. There are compelling reasons to investigate artificial super intelligence. This type of AI is capable of surpassing human intellect by expressing cognitive skills and developing its own mental capabilities. ASI is a highly sophisticated, and intelligent type of AI that goes beyond normal intellectual capacity. This paper will discuss the societal impact and the current academic impact of ASI. Finally, this study would attempt to utilize the Markov Decision Model of Machine learning to predict the trajectory of ASI in the very near future. © 2023 IEEE.

关键词： Robotics

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Layer-wise Top-k Gradient Sparsification for Distributed Deep learning 3

Layer-wise Top-k Gradient Sparsification for Distributed Dee...

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3rd International conference on Neural Networks, Information and Communication Engineering, NNICE 2023

作者： Li, Guangyao Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350335972

Distributed training is widely used in training large-scale deep learning models, and data parallelism is one of the dominant approaches. Data-parallel training has additional communication overhead, which might be the bottleneck of training system. Top-k sparsification is a successful technique to reduce the communication volume to break the bottleneck. However, top-k sparsification cannot be executed until backpropagation is completed, which disables the overlap of backpropagation computations and gradient communications, leading to limiting the system scaling efficiency. In this paper, we propose a new distributed optimization approach named LKGS-SGD, which combines synchronous SGD (S-SGD) with a novel layer-wise top-k sparsification algorithm (LKGS). The LKGS-SGD enables the overlap of computations and communications, and adapts to gradient exchange at layer-wise. Evaluations are conducted by real-world applications. Experimental results show that LKGS-SGD achieves similar convergence to dense S-SGD, while outperforming the original S-SGD and S-SGD with top-k sparsification. © 2023 IEEE.

关键词： Backpropagation

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Implementation Strategy for Code Analyser Component of Rule-Based Self-learning Model for Automatic Evaluation of C plus plus Programs 3rd

Implementation Strategy for Code Analyser Component of Rule-...

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3rd International conference on Information and Communication Technology for intelligent Systems (ICTIS)

作者： Christian, Maxwell GLS Univ Ahmadabad Gujarat India

ISBN: (纸本)9789811317477;9789811317460

This research paper provides a detailed explanation about the implementation strategy of the code analyser component of the rule-based self-learning model for automatic evaluation of C++ programs Christian (Information and Communication Technology for Sustainable Development. conference 2017, vol 9, pp 469, Springer, 2017, [1]). The main idea of this paper is to enlighten the procedure through which the first and the foremost component of the referred and targeted model can be implemented. This research paper also focuses on the rule formation style and also how different grades can be incorporated to achieve the grade analysing part of the model. Also, the research paper explains generic nature of the implementation strategy and various ways in which it can be extended as well as moderated for further scope of model implementation.

关键词： Automatic evaluation Automatic grading Rule-based evaluation Rule-based grading Self-learning models

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Efficient Utilization of Metaheuristic Techniques for Profit Maximization in Combinatorial Production Planning: A Comparative Study 3

Efficient Utilization of Metaheuristic Techniques for Profit...

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3rd IEEE International conference on Control, Instrumentation, Energy and Communication, CIEC 2024

作者： Mukherjee, Arijit Mukherjee, Surojit Future Institute of Technology Deptartment of Electrical Engineering Kolkata India Great Lakes Institute of Management Post-Graduate Program in Management Chennai India

ISBN: (纸本)9798350313703

Efficient production planning requires well-structured models compatible with metaheuristic and other optimization techniques. This study compares three established metaheuristic methods - sanitized-teaching-learning-based optimization (s-TLBO), Particle Swarm optimization (PSO), and Differential Evolution (DE) with a termination criterion of equal number of functional evaluations - for profit-maximizing production planning. The focus is on a single-level problem involving complex constraints, non-linear costs, budget and raw material limitations. The paper presents two solution strategies: a penalty-based and a correction-based approach. Analyzing 300 unique instances of the problem reveals that s-TLBO implemented with correction-based approach consistently outperforms the other methods in devising optimal production plans for two Saudi Arabian petrochemical industries with 24 products, 54 processes, and three raw materials for plant 1 and two raw materials for plant 2. © 2024 IEEE.

关键词： Combinatorial optimization

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Fuzzy rider optimization algorithm for vein-based biometric recognition 3

Fuzzy rider optimization algorithm for vein-based biometric ...

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3rd International conference on intelligent Sustainable Systems, ICISS 2020

作者： Verma, Dipti Arora, Ankit Vishwavidyalaya Engineering College CSVTU Department of CSE Bhilai C.G India

ISBN: (纸本)9781728170893

Biometric recognition system plays a vital role for identifying the person automatically using the characteristic vector from its behavioral or physiological characteristics. In recent days, recognition system based on vein image is a facilitating and promising biometric technology. Vein recognition uses various modalities, like hand, palm and finger images for the identification of person. This article introduces the model for vein-based biometric recognition using the rider-based score level fusion method. The developed method includes four steps, which are pre-processing, vein extraction, feature extraction and biometric recognition. The first step is to input the training images, like finger, hand, and palm into pre-processing step. In the pre-processing level, noises are removed from the input image and the Region of Interest (ROI) extraction is also executed in pre-processing module based on neighbourhood search method. After that, veins are extracted for each modality using the holoentropy, enlightening of image, and the circular averaging filter. After vein extraction, the features are acquired, and then, Entropy-based Euclidean Distance (EED) is developed for score level fusion. At last, the biometric identification is carried out based on the proposed Fuzzy Rider optimization Algorithm (Fuzzy ROA). The proposed Fuzzy ROA is newly designed by combining the Fuzzy logic and the Rider optimization Algorithm (ROA). The performance metrics, such as False Rejection Ratio (FRR), False Acceptance Ratio (FAR) and accuracy is analyzed, and then compared with other methods. The evaluation of the developed method attains the maximum accuracy of 90.19%, minimum FAR and FRR of 10.09% and 0.87% respectively. © 2020 IEEE.

关键词： Fuzzy logic

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A Hybrid Method for Vietnamese Text Normalization 19

A Hybrid Method for Vietnamese Text Normalization

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3rd International conference on Natural Language Processing and Information Retrieval (NLPIR)

作者： Nguyen Thi Thu Trang Dang Xuan Bach Nguyen Xuan Tung Hanoi Univ Sci & Technol VBee Grp Hanoi Vietnam Hanoi Univ Sci & Technol Hanoi Vietnam

ISBN: (纸本)9781450362795

This paper presents a hybrid method for normalizing written text often found on newspapers to its spoken form. To normalize raw text with a number of non-standard words (NSWs), a two-step model is proposed. The first step involves classifying NSWs into different categories using Random Forest. The latter one is to expand them, depending on their NSW types, into pronounceable syllables using a hybrid method. Most of numeric types can be expanded by well-defined rules while most of alphabetic ones must be expanded by a deep learning (i.e. sequence-to-sequence) model and a post adjustment. The experiment on a Vietnamese corpus with proposed NSW categories shows that the most ambiguous cases of the classification model are for abbreviation and read-as-sequence types, hence combined into one category for the latter expansion with more complex model and better context. The classification model gives an enhanced result of 99.20% with the category combination and the feature optimization. In the expansion, the sequence-to-sequence model shows a good result of 96.53% for abbreviations and 96.25% for loanwords with a post-adjustment for some completely wrong cases. This model can predict effectively the expansions of abbreviations in context.

关键词： Text normalization Deep learning Speech Synthesis Automatic Speech Recognition

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