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2nd international conference on Artificial Intelligence and signal processing, AISP 2022

作者： Dalai, A.K. Jena, A.K. Ramana, B.V. Maneesha, B. Panda, Nibedan School of Computer Science and Engineering VITAP University India Department of Information Technology Aditya Institute of Technology and Management AP Tekkali532201 India Department of CSE Presidency University Yelahanka Bengaluru560064 India

ISBN: (纸本)9781665442909

Recently there is an emergent curiosity among researchers to apply machine learning algorithms over diversified real world complications to get simpler *** notion behind this briefing is to represent the basic machine learning algorithms and its applicability in current research. Broadly machine learning algorithms falls to the category of either supervised or unsupervised learning technique. In this paper we have discussed supervised machine learning techniques with its simplicity to apply over various problem areas and simultaneously the challenges for such algorithms. Furthermore SVM and Random Forest (RF) are utilised learn, categorise, and compare cancer, liver, diabetes, iris, and heart data in this study. For all considered data sets, the results of SVM and RF are compared. The results are properly analysed in order to develop better prediction learning techniques. © 2022 IEEE.

关键词： Support vector machines

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Integrative learning in Engineering Education by Action Research Method

Integrative Learning in Engineering Education by Action Rese...

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2nd international Multi-conference on Society, Cybernetics and Informatics

作者： Pendharkar, Gita Judge, Sukhvir Gredeskoul, Olga RMIT Univ SIEBS Melbourne Vic 3170 Australia

ISBN: (纸本)9781934272473

Demanding industry standards and rapidly changing technology creates a lot of challenge for graduate engineers in terms of engineering knowledge. A graduate engineer needs to be confident in testing real life systems. However, in general, it is observed that the students lack practical skills and find it hard to integrate two or more courses in the program. By using action research in teaching and learning, the potential of applying integrative learning to engineering curricula is researched. The aim in this paper is to enhance integrative learning by introducing a common project for two different courses, Digital signal processing which is a theoretical course and Microprocessor Applications which is a practical oriented course.

关键词： Engineering education

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Deep Active learning for Text Classification 2018

Deep Active Learning for Text Classification

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2nd international conference on Vision, Image and signal processing (ICVISP)

作者： An, Bang Wu, Wenjun Han, Huimin Beihang Univ 37 XueYuan Rd Beijing Peoples R China

ISBN: (纸本)9781450365291

In recent years, Active learning (AL) has been applied in the domain of text classification successfully. However, traditional methods need researchers to pay attention to feature extraction of datasets and different features will influence the final accuracy seriously. In this paper, we propose a new method that uses Recurrent Neutral Network (RNN) as the acquisition function in Active learning called Deep Active learning (DAL). For DAL, there is no need to consider how to extract features because RNN can use its internal state to process sequences of inputs. We have proved that DAL can achieve the accuracy that cannot be reached by traditional Active learning methods when dealing with text classification. What's more, DAL can decrease the need of the great number of labeled instances for Deep learning (DL). At the same time, we design a strategy to distribute label work to different workers. We have proved by using a proper batch size of instance, we can save much time but not decrease the model's accuracy. Based on this, we provide batch of instances for different workers and the size of batch is determined by worker's ability and scale of dataset, meanwhile, it can be updated with the performance of the workers.

关键词： Active learning Deep learning machine learning Artificial Intelligence Text Classification

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A Semantic Internet of Things Framework using machine learning Approach based on Cloud Computing 2018

A Semantic Internet of Things Framework using Machine Learni...

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2nd international conference on Digital signal processing (ICDSP) / international conference on Computer Graphics and Virtuality (ICCGV)

作者： Ding, Pei-Wun Hsu, I-Ching Natl Formosa Univ 64 Wenhua Rd Huwei Township 632 Yunlin Taiwan

ISBN: (纸本)9781450364027

A major limitation of existing Semantic Web applications is the lack of automatic generation linked data for personal needs. Internet of Things (IoT) can provide automatic sensing data to improve this limitation. The study addresses this issue by defining a Semantic Internet of Things Framework (SIOTF), which is implemented on Hadoop-based cloud computing ecosystem to provide efficiency in dealing with a mass of sensing data. The SIOTF is composed of four modules: Internet of Things module, Naive Bayesian Classification module, Open Data Service module, and Semantic Web module. The proposed SIOTF is used to develop a Culture Sharing Cloud Platform (CSCP) that provides customized culture information for personnel needs. To demonstrate the feasibility of CSCP, the experimental results illustrate the efficiency and effectiveness of the proposed approach.

关键词： Cloud Computing IoT Semantic Web machine learning

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Classification of Hand Movements via EMG using machine learning Methods for Prosthesis 2

Classification of Hand Movements via EMG using Machine Learn...

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2nd international conference on Artificial Intelligence and signal processing, AISP 2022

作者： Karuna, M. Guntur, Sitaramanjaneya Reddy Department of Electronics and Communication Engineering Vignan’s Institute of Information Technology India Department of Biomedical Engineering Vignan’s Foundation for Science Technology and Research India

ISBN: (纸本)9781665442909

The recognition of hand movements using surface electromyography (sEMG) and a machine learning technique is becoming increasingly significant to control a prosthetic hand in a rehabilitation facility for people who have had their hands amputated in order to regain lost capability. However, in real life, controlling a prosthetic hand utilizing non-invasive methods is still a challenge. Existing research results are limited and not meeting the needs of amputee. The objective of this work is to fulfill the gap by proposing empirical mode decomposition (EMD) based machine learning (ML) classifier to recognize hand movements of the Ninapro dataset, this benchmark standard is used to evaluate four classifiers by comparing the performance accuracy results. The outcome of this work is better movement recognition achieved using one of the four distinct classifiers. © 2022 IEEE.

关键词： Empirical mode decomposition

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Extreme learning machine Based Defect Detection for Solder Joints

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JOURNAL OF INTERNET TECHNOLOGY 2020年第5期21卷 1535-1543页

作者： Ma, Liyong Xie, Wei Zhang, Yong Feng, Xijia Harbin Inst Technol Sch Informat Sci & Engn Harbin Heilongjiang Peoples R China Harbin Inst Technol Dept Civil Engn Harbin Heilongjiang Peoples R China

The quality of solder joints is essential for electronic products, and the detection of defects in solder joints is critical to the quality control of electronic products. A vision inspection is developed to detect defects of solder joints in automatic line. Extreme learning machine is applied to identify defective solder joints from qualified ones. Five low level features and three advanced features are employed as input features. The low-level features include roundness, roughness, entropy, contrast and histogram of oriented gradient. The advanced features include grey-level co-occurrence matrix, local binary pattern, and segmentation-based fractal texture analysis. To solve unbalanced samples problem, Gaussian mixture model based dense estimation scheme is proposed to adjust the classification super plane for extreme learning machine. The experimental results demonstrate that the proposed defect detection method is more efficient than neural network, support vector machines, common extreme learning machine and convolutional neural network-based methods, and it has real-time performance to meet the equirement of the actual production line.

关键词： Defect detection Solder joints Extreme learning machine Gaussian mixture model

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proceedings of 2024 2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems: Exploration and Blend of Emerging Technologies for Future Innovation, ICMACC 2024

Proceedings of 2024 2nd International Conference on Recent T...

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2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

ISBN: (纸本)9798350366570

The proceedings contain 142 papers. The topics discussed include: fault analysis of ship DC power system;event triggered and self-triggered formation control of multi agent systems;mitigating credit card fraud: a machine learning perspective;automatic license plate detection using information processing system vision and deep learning;face detection and recognition based intelligence surveillance robot for attendance system;Clusterboost: an Airbnb recommendation engine using metaclustering;key management in space integrated terrestrial network: a cybersecurity perspective;user profile based movie recommender system using machine learning;and utilization of non-orthogonal multiple access (NOMA) for spectral efficiency in mixed fading environments.

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Literature Survey-Food Recognition and Calorie Measurement Using Image processing and machine learning Techniques 2nd

Literature Survey-Food Recognition and Calorie Measurement U...

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2nd international conference on Communications and Cyber-Physical Engineering (ICCCE)

作者： Reddy, V. Hemalatha Kumari, Soumya Muralidharan, Vinitha Gigoo, Karan Thakare, Bhushan S. STESs Sinhgad Acad Engn Pune Maharashtra India

ISBN: (纸本)9789811387159;9789811387142

Nowadays, with easy access to internet, food is delivered at our doorsteps just on the click of a button due to which people have started to consume higher amount of fast food. This has accelerated the chances of suffering from a chronic disease known as obesity. Since obesity has become such a widespread disease, various mobile e-health applications have been developed for assistive calorie measurement to help people fight against health-related problems. In this paper, we have surveyed different methods for food recognition and calorie measurement using various methods and compared their performances based on several factors.

关键词： Convolutional neural network (CNN) Deep learning Food recognition machine learning (ML)

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A Received signal Strength Based Indoor Localization Algorithm Using ELM Technique and Ridge Regression 2

A Received Signal Strength Based Indoor Localization Algorit...

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IEEE 2nd international conference on Electronic Information and Communication Technology (ICEICT)

作者： Feng, Zhiyue Cao, Yanhua Yan, Jun Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing Peoples R China Project Construct Bur Water Resources Dept Jiangsu Prov Nanjing Peoples R China

ISBN: (纸本)9781538692981

In this paper, a received signal strength (RSS) based localization algorithm by extreme learning machine (ELM) technique is proposed. In the offline phase, in order to cope with the environmental noise of the training data set, the improved ridge regression based ELM (IRR-ELM) algorithm is proposed to obtain more stable prediction which has better generalization ability, because the ridge parameter is obtained with the variance of the training error. In the online phase, the obtained prediction model is straightly used for position prediction. Since it can reduce the deviation in the off-line training phase, the proposed algorithm has more stable and accurate position estimation result in online phase. At last, the RSS measurements obtained from field test are used for performance evaluation. It shows that the localization performance of the proposed is better than that of the existing ELM based localization methods.

关键词： extreme learning machine localization received signal strength ridge regression

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A Forgotten Strategy for Pooled Contextualized Embedding learning 2020

A Forgotten Strategy for Pooled Contextualized Embedding Lea...

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2nd international conference on Image processing and machine Vision / international conference on Pattern Recognition and machine learning (IPMV)

作者： Cai, Dunbo Huang, Zhiguo Qian, Ling Hang, Tao Feng, Qingsong China Mobile Suzhou Software Suzhou Peoples R China

ISBN: (纸本)9781450388412

String (word) embeddings are keys to advanced neural natural language understanding (NLU) models. Recently, a new embedding called contextual string embedding (CtxEmb) reached a new state of the art on many NLU tasks. Specially, a pooling variant of CtxEmb demonstrated new best results on named entity recognition (NER) tasks. While the pooling variant is good at reaching new best performance in terms of Micro F1 score, it poses questions on what kind of information in the pool is helpful. In particular, the pooling variant maintains a (possibly long) list of previously seen occurrences of a word, and computes embedding for the word in a new context based on those occurrences. In this paper, we propose a strategy to forget some previous occurrences, and thus exploring which history are beneficial in constructing embeddings for a new context of a word. The proposed forgotten strategy is designed by accounting for a distance metric defined in this paper. Preliminary experiments on the WNUT-17 task show the effectiveness of our forgotten strategy, and uncover that embeddings that are diverse in terms of cosine similarity are helpful in forming an aggregated embedding.

关键词： Word Embedding learning Contextual String Embedding Representation learning

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