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international conference on Image Processing and Capsule Networks, ICIPCN 2020

International Conference on Image Processing and Capsule Net...

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international conference on Image Processing and Capsule Networks, ICIPCN 2020

ISBN: (纸本)9783030518585

The proceedings contain 72 papers. The special focus in this conference is on Image Processing and Capsule Networks. The topics include: Developing a framework to identify potential firms for job assignment;developing a framework for trend prediction of stocks prices;machine learning based diagnosis of alzheimer’s disease;iot based vehicle (car) theft detection;emg signal classification with effective features for diagnosis;eye fatigue algorithm for driver drowsiness detection system;an analytical approach for recognizing the occupational gender bias in the data systems;development of an algorithm for vertebrae identification using speeded up robost features (surf) technique in scoliosis x-ray images;highly protective framework for medical identity theft by combining data hiding with cryptography;improving the prediction accuracy of asd using class imbalance mitigation technique;stock price prediction based on technical indicators with soft computing models;credit card fraud detection technique based on hybrid classification;improved fast block matching motion estimation using multiple frames;efficient framework for identification of soybean disease using machine learning algorithms;brain image visualization;a framework for degraded kannada character recognition;classification of soybean diseases using pre-trained deep convolutional neural networks;feedforward back propagation neural network (ffbpnn) based approach for the identification of handwritten math equations;comparison of machine learning algorithms for smart license number plate detection system;ritchie’s smart watch data analytics and visualization;sentiment analysis for product rating using classification;face recognition system using a hybrid scale invariant feature transform based on local binary pattern;sign language identification using image processing techniques;foreword;preface.

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Development of a Predictive System for Customer Buying pattern Using Apriori Algorithm

Development of a Predictive System for Customer Buying Patte...

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IEEE international conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Peter Oluwasheyi Oluokun Gbemisola Olutosin Babalola Adebusola Modupeola Tope-Oke Stephen Eyitayo Obamiyi Olalekan Ismail Araoye Computer Science Department Afe Babalola University Ado-Ekiti Ado-Ekiti Nigeria Department of Computer Science The Federal Polytechnic Ado-Ekiti Ado-Ekiti Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

A lot of retailers face the challenge of how to understand their customers’ buying habits, and this makes stocking products, arrange items on shelves, and making customers to come back again a difficult task. Without understanding what the customer typically wants to buy, retail stores can miss out on opportunities to increase sales and improve customer satisfaction. To address this, this study developed a system using the Apriori algorithm to analyze past shopping data from a supermarket. The developed system identifies patterns in the items of customers that are frequently bought together. This allows the business to predict future purchases. By using these method, stores can now manage their stock better to create better marketing strategies, and to improve their customers overall shopping experience. The results that were obtained from this study also show that the system can help businesses stay competitive by understanding customer needs and acting on them more effectively.

关键词： Humanities Consumer behavior Itemsets Customer satisfaction Inventory management Prediction algorithms pattern recognition Faces Business

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A New Head Pose Estimation Method Using Vision Transformer Model 2021

A New Head Pose Estimation Method Using Vision Transformer M...

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7th international conference on computing and Artificial Intelligence, ICCAI 2021

作者： Ling, Xufeng Wang, Dong Yang, Jie Shanghai Normal University Tianhua College Ai School No. 1661 North Sheng Xin Road China Institute of Image Processing and Pattern Recognition Shanghai Normal University Tianhua College China

ISBN: (纸本)9781450389501

In this paper, a self-attention-based Vision Transformer (VIT) method is introduced into estimate human head pose parameters. Firstly, the head pose image is divided into 32X32 patches, each image patch is regarded as a word, and the whole image is treated as a paragraph composed of n words by the VIT. Image recognition can be regarded as the semantic recognition of this paragraph. Next, we redesign the regression VIT to estimate the parameters. Then we select Head Pose Database as the training and validation dataset. The VIT is trained on the enhanced and normalized dataset. Finally, the trained VIT is used to regress the head pose parameters on testing samples. Experimental results show that VIT has high accuracy and good generalization ability for head pose estimation. © 2021 ACM.

关键词： Image recognition

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A deep learning study on understanding banglish and abbreviated words used in social media 5

A deep learning study on understanding banglish and abbrevia...

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5th international conference on Intelligent computing and Control Systems, ICICCS 2021

作者： Das, Suharto Islam, Md Sanzidul Mahmud, Imran Daffodil International University Dept of Software Engineering Dhaka Bangladesh Daffodil International University Dept of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665412728

Day by Day, the trend of using social media(SM) has increased among the people. With the increasing rate, the use of Banglish(Merge of Bangla and English) and shortcut words has also being increased. In this research, Banglish and Shortcut words have been fully converted to English. For this type of conversation, we have used the CNNs method and the method consist of multiple layer and these layers are connected to each other. This method is considered to be the best method because it does not need any feature extraction. The convolutional neuron network is used in many areas such as image and pattern recognition, speech recognition, natural language processing and video analysis. In this research we have used CNN method because we have decided to use computer vision as some of the words are close to each other and at first we need to convert all the shortcut words into images for pre-processing the data. With the help of CNN method searched for the Banglish and shortcut words that people use for their daily conversation. At first we find the different representative of a single word and then we converted those shortcut and Banglish words to main word using CNNs method. © 2021 IEEE.

关键词： Speech recognition

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A Modified Sugeno Integral Based Similarity Measure for Intuitionistic Fuzzy soft Set and its Application

A Modified Sugeno Integral Based Similarity Measure for Intu...

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IEEE international conference on Fuzzy Systems (FUZZ-IEEE)

作者： Niher Ranjan Das Q.M. Danish Lohani Department of Mathematics South Asian University New Delhi India

In the paper, intuitionistic fuzzy soft set (IFSS) is considered for the mathematical modeling of real-world issues. The IFSS involves an extra parameter to extrapolate the traditional notion of intuitionistic fuzzy set, and hence it is highly utilized for real-life applications. The similarity measures for IFSSs are defined to deal with the problems of pattern recognition, machine learning, and decision-making. This paper introduces a Sugeno integral based similarity measure for IFSSs in which a novel concept of $(\alpha,\beta)-cut$ is applied for the first time. Sugeno integral is a measure to calculate the total expected value of similarity between two IFSSs. Since IFSS consists of two independent components, $(\alpha,\beta)-cut$ is used to ensure the operationalization of the measure. Here, some of the mathematical properties related to the proposed measure are also studied. Moreover, we compare the performance of the proposed measure with some well-known similarity measures over a benchmark example. We have further validated the efficiency of the proposed measure on the basis of accuracy, sensitivity, F-measure, and error rate over UCI Machine Learning Repository data set.

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Comprehensive Visual Features and Pseudo Labeling for Robust Natural Language-based Vehicle Retrieval

Comprehensive Visual Features and Pseudo Labeling for Robust...

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IEEE Computer Society conference on Computer Vision and pattern recognition Workshops (CVPRW)

作者： Bach Hoang Ngo Dat Thanh Nguyen Nhat-Tuong Do-Tran Phuc Pham Huy Thien Minh-Hung An Tuan-Ngoc Nguyen Loi Nguyen Hoang Vinh Dinh Nguyen Quang-Vinh Dinh AI Vietnam Research Vietnam FPT Telecom Vietnam Faculty of Computer Science and Engineering Ho Chi Minh City University of Technology School of Computing and Information Technology Eastern International University Vietnam

Vehicle retrieval has become crucial for public transportation and intelligent transportation systems due to the exponential development of large-scale transportation videos. Vehicle re-identification and vehicle tracking are the main components of most vision-based vehicle recovery systems. Unfortunately, the limited amount of information provided by traffic video feeds limits the vision-based vehicle retrieval algorithm’s efficacy. Therefore, this article proposes a contrastive cross-modal vehicle retrieval approach to maximize the complementarity of natural language and visual representations. An efficient method to fuse multiple input image features is also proposed to extract comprehensive information from various vehicles along with pseudo labeling and efficient post-processing techniques to enhance retrieval accuracy. The proposed method achieved the 3rd ranking of Mean Reciprocal Rank (MRR) score of 0.4795 on the test set for the Challenge Track 2: Tracked-Vehicle Retrieval by Natural Language Descriptions 2023. Source code for the proposed approaches is openly accessible at https://***/anminhhung/AICity-2023-Track2.

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14th international conference on Applications of Fuzzy Systems, soft computing, and Artificial Intelligence Tools, ICAFS 2020

14th International Conference on Applications of Fuzzy Syste...

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14th international conference on Applications of Fuzzy Systems, soft computing, and Artificial Intelligence Tools, ICAFS 2020

ISBN: (纸本)9783030640576

The proceedings contain 103 papers. The special focus in this conference is on Applications of Fuzzy Systems, soft computing, and Artificial Intelligence Tools. The topics include: Vegetation Index Formation Using Fuzzy Analyses of Object Multispectral Reflection;the Challenge of Adaptation in Future Networking Environment: Engineering Methodology;machine Learning in Automated Chest Radiographs Classification;machine Learning for Better Understanding of Autistics;Evaluation of the Management and Prevention of Covid-19 Pandemic in Most Infected Countries via Fuzzy PROMETHEE Approach;fuzzy Dynamic Programming-Based Multi-stage Transportation Problem;comparative Assessment of the Regional Freight Transportation by Method of Fuzzy Linear Regression;construction of Device for Fuzzy Chaos Signal Generation;monadic Automata with Relational Morphisms;dental Disease Detection Using Fuzzy Logic;evaluation of the Effectiveness of Integration Processes in Production Enterprises Based on the Fuzzy Logic Model;fuzzy Approach for Evaluation of Student’s Performance;the Man Who Changed the Scientific World: To the Centenary of the Birth of Lotfi Zadeh;z-set Based Approach to Control System Design;combined Heat and Power Economic Emission Dispatch Applying Exchange Market Algorithm with Fuzzy Satisfying Techniques;intuitionistic Fuzzy Assessment of Aggregated Quality of Life Index;review of Place recognition Approaches: Traditional and Deep Learning Methods;fuzzy Reasoning in Music;integrative Jurisprudence from Fuzzy Logic Perspective;ecological and Economic Security Processes Modeling with Cloud-Based soft computing;transformation of Mathematical Methods for Assessing the State of Economic Systems in Digitalization Based on the Theory of Fuzzy Sets (Review);Prediction of Daily Solar Irradiation Using CNN and LSTM Networks.

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Human Brain Stroke Prediction using Machine Learning Methods with Synthetic Minority Oversampling Approach

Human Brain Stroke Prediction using Machine Learning Methods...

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Women in Data Science at Prince Sultan University (WiDS PSU), international conference of

作者： Muhammad Mujahid Noor Ayesha Ahmad Taher Azar Tanzila Saba Zeeshan Haider Artificial Intelligence and Data Analytics (AIDA) Lab CCIS Prince Sultan University Riyadh School of Clinical Medicine Zhengzhou University Zhengzhou China College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Automated Systems and Soft Computing Lab (ASSCL) Prince Sultan University Riyadh Saudi Arabia Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Automated Systems and Soft Computing Lab (ASSCL) Prince Sultan University Riyadh Saudi Arabia

ISBN: (数字)9798350395839

ISBN: (纸本)9798350395846

The brain is considered to be the principal anatomical component of the human body. A stroke is a medical condition defined by cerebral injury that occurs as a consequence of blood vessel rupture within the brain. Symptoms might become apparent when there is an interruption in the brain circulation of blood and other vital chemicals. Stroke is widely acknowledged as a significant medical disorder because to its elevated mortality rates, enduring cognitive impairments, and concomitant comorbidities. The timely recognition of the numerous prodromal manifestations linked to a stroke has the capacity to attenuate its magnitude. The primary objective of this study is to utilize machine learning methodologies in order to predict the likelihood of an early-stage occurrence of a cerebral stroke. In order to assess the efficacy of the algorithm, a dataset for stroke prediction was acquired via the Kaggle platform. Various preprocessing techniques are applied to normalize and clean the dataset for stroke prediction. This research utilized many classification models including Random Forest, Ada Boost, LightGBM,, Decision Tree (DT), Logistic Regression (LR),and Support vector machine (SVM) to perform classification tasks. The Random Forest classifier has a higher level of classification accuracy in comparison to alternative machine learning classifiers, obtaining an impressive accuracy rate of 99.45%. The results of the investigation revealed that machine learning approaches demonstrated a higher level of performance in comparison to alternative methods.

关键词： Support vector machines Deep learning Accuracy Machine learning algorithms Computed tomography Stroke (medical condition) Prediction algorithms

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A soft computing Based Approach for Pixel Labelling on 2D Images Using Fine Tuned R-CNN 10th

A Soft Computing Based Approach for Pixel Labelling on 2D ...

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10th international conference on Innovations in Computer Science and Engineering, ICICSE 2022

作者： Arappal, Nedumaran Singh, Ajeet Saidulu, D. School of Electrical and Computer Engineering Kombolcha Institute of Technology Wollo University Dessie Ethiopia School of Computer and Information Sciences University of Hyderabad Hyderabad Telangana500034 India Department of Information Technology Guru Nanak Institutions Technical Campus Hyderabad Telangana India

ISBN: (纸本)9789811974540

Nowadays Artificial Intelligence (AI) is an integral component of the core business model of several organisations and a crucial strategic component of many industries’ strategies such as—healthcare, finance, retail education etc. on a global scale. Machine Learning (ML) is a technology to achieve AI. Machine learning methods utilize a variety of statistical, A vast, unstructured, and complicated dataset may be analysed using probabilistic and optimization approaches to learn from the past and find relevant trends. Automated oriented text categorization, network intervention detection, spam email filtering, credit card deceit detection, consumer purchase behaviour detection, and manufacturing process optimization are just a few of the many uses for these algorithms, image categorization, object recognition and disease modelling. Allocating a different class oriented label to every pixel of a particular image is certainly one of the significant steps in constructing complex automated systems like driver-less vehicles/drones, human-friendly oriented robotic machines, robot-assisted medical procedure, and savvy military systems. Pixel labeling’s computational purpose is to give each individual pixel in an imagery a class label. It is critical long-range (pixel) label dependencies in a model must be captured pictures to provide strong visual coherence and high class accuracy. This may be accomplished in a feed-forward architecture by considering a suitably big input to the context patch surrounding each individual pixel to be labelled. We present a strategy in this chapter that consists of an ideally finetuned recurrent convolutional neural network that mainly allows us to incorporate a broad input context without restricting the model’s capacity. Our solution does not rely on any such segmentation techniques or task-specific characteristics, in contrast to the majority of the conventional methods. The system is trained on raw pixels from beginning to end, and it models

关键词： Pixels

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Intelligent Workforce Management: A Smart Biometric Approach to Optimal Staff Scheduling

Intelligent Workforce Management: A Smart Biometric Approach...

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Recent Trends in Computer Science and Technology (ICRTCST), international conference on

作者： Mohnaa Ghosh Devyanjali Srivastava M. Kowsigan Department of Computing Technologies College of Engineering & Technology SRM Institute of Science and Technology Kattankulathur Chengalpattu Tamil Nadu India

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

Manager discretion arises from ineffective absence and attendance tracking, which are dependent on human scheduling, and leads to ineffective staff scheduling. Systems for tracking time and attendance frequently need manual data entry. For precise attendance tracking, fingerprint biometric integration is essential. The suggested method creates an optimization model for staff scheduling using biometric data. The suggested method offers a user-friendly interface to prevent manager errors, increased operational effectiveness, and equitable scheduling. The administrative load has decreased as a result. The suggested strategy has a number of benefits over conventional techniques, such as improved resource efficiency and attendance monitoring accuracy. Subsequent improvements to this methodology may encompass broadening the system’s flexibility to accommodate diverse organizational structures, investigating supplementary biometric data sources, and augmenting the system’s scalability and amalgamation with supplementary HR and management instruments. All things considered, there are some encouraging advantages to this new method of staff scheduling for businesses of all kinds.

关键词： Productivity Scheduling algorithms soft sensors Scalability Companies Fingerprint recognition Scheduling

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