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A Robust Sparse Nonnegative Matrix Factorization with Low Rank representation for clustering 23

A Robust Sparse Nonnegative Matrix Factorization with Low Ra...

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Proceedings of the 2023 4th international conference on machine learning and Computer Application

作者： Xue Li Xinxin Liu Yiwei Li Shuo Zhang School of Computer Science and Technology Anhui University of Technology China Data Communication Product Line Huawei Technologies Co.Ltd China College of Science Mathematics and Technology Wenzhou-Kean University China

ISBN: (纸本)9798400709449

Nonnegative Factorization Matrix has achieved excellent results in the field of data mining and machine learning. However, NMF and its extensions, use the least square error function, are sensitive to noises and simultaneously ignore some useful discriminant information in the error matrix. To tackle this issue, we propose a novel method, named Robust Sparse Nonnegative Matrix Factorization with Low Rank (RSLR-NMF), in this paper. RSLR-NMF can effectively remove any redundant information from the input data and extract the hidden information in the noisy error part based on Bilinear Error Matrix Decomposition (BEMD) module. RSLR-NMF takes into account reconstruction error matrix and low rank error matrix simultaneously with th>th> norm constraint and low rank constraint, respectively. Besides, elegant updating rules are presented to solve the proposed method. the experimental results on several data sets show that the proposed RSLR-NMF provides more faithful basis factors and effective clustering results.

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Advertisement Placement Strategies Based on Artificial Intelligence Hotspot Tracking Algorithms

Advertisement Placement Strategies Based on Artificial Intel...

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Electronic Communications, Internet of things and Big data (ICEIB), international conference on

作者： Yao Dongyu College of Humanities Liaoning Institute of Science and Technology Benxi China

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

the application of AI technology in advertisement placement strategy has become increasingly important. thus, advertisement placement strategies were studied using AI hotspot tracking technology through in-depth analysis of consumer behavioral data and market trends. the results can be used for personalized customization and precise targeting of advertisement content. the effect of hotspot tracking algorithms was evaluated using the term frequency-inverse document frequency (TF-IDF) in predicting consumer preferences and optimizing advertisement placement strategies. TF-IDF is a text mining technique used to effectively extract keywords from a large amount of text data to hot topics of consumer interest. the outcome is used to generate advertising content. By applying the TF -IDF algorithm to consumer-generated content analysis, consumer interest and emotional tendencies toward specific topics are identified and quantified to predict market trends. Combining the results of AI hotspot tracking and TF -IDF analysis, a set of innovative ad delivery strategies was proposed to improve the relevance and attractiveness of advertisements and adjust the content and delivery time. Based on real-time consumer feedback and behavioral patterns, the strategies can be used to achieve a higher return on investment.

关键词： Text mining machine learning algorithms Target tracking Prediction algorithms Market research Real-time systems Behavioral sciences

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Detection and Correspondence Matching of Corneal Reflections for Eye Tracking Using Deep learning 25

Detection and Correspondence Matching of Corneal Reflections...

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25th international conference on pattern recognition (ICPR)

作者： Chugh, Soumil Brousseau, Braiden Rose, Jonathan Eizenman, Moshe Univ Toronto Dept Elect & Comp Engn Toronto ON Canada Univ Toronto Dept Elect & Comp Engn Ophthalmol & Vis Sci Toronto ON Canada

ISBN: (纸本)9781728188089

Eye tracking systems that estimate the point-of-gaze are essential in extended reality (XR) systems as they enable new interaction paradigms and technological improvements. It is important for these systems to maintain accuracy when the headset moves relative to the head (known as device slippage) due to head movements or user adjustment. One of the most accurate eye tracking techniques, which is also insensitive to shifts of the system relative to the head, uses two or more infrared (IR) light emitting diodes to illuminate the eye and an IR camera to capture images of the eye. An essential step in estimating the point-of-gaze in these systems is the precise determination of the location of two or more corneal reflections (virtual images of the IR-LEDs that illuminate the eye) in images of the eye. Eye trackers tend to have multiple light sources to ensure at least one pair of reflections for each gaze position. the use of multiple light sources introduces a difficult problem: the need to match the corneal reflections with the corresponding light source over the range of expected eye movements. Corneal reflection detection and matching often fail in XR systems due to the proximity of camera and steep illumination angles of light sources with respect to the eye. the failures are caused by corneal reflections having varying shape and intensity levels or disappearance due to rotation of the eye, or the presence of spurious reflections. We have developed a fully convolutional neural network, based on the UNET architecture, that solves the detection and matching problem in the presence of spurious and missing reflections. Eye images of 25 people were collected in a virtual reality headset using a binocular eye tracking module consisting of five infrared light sources per eye. A set of 4,000 eye images were manually labelled for each of the corneal reflections, and data augmentation was used to generate a dataset of 40,000 images. the network is able to correctly identify

关键词： Eye tracking Corneal Reflections Semantic Segmentation Virtual Reality pattern recognition

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Two-stage Multi-task learning for Automatic Sleep Staging Method 4

Two-stage Multi-task Learning for Automatic Sleep Staging Me...

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4th international conference on Artificial Intelligence and pattern recognition, AIPR 2021

作者： Xi, Haole Wang, Yagang Cui, Shuiping Niu, Rui Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450384087

Sleep staging is an important preliminary examination for the diagnosis of sleep disorders. Proper identification of sleep stages is important for the diagnosis and treatment of sleep disorders. It is a common method to classify sleep stages through Polysomnography (PSG) signals, but artificial classification of sleep stages is complicated and prone to errors. To solve the above problems, a two-stage multi-task learning for automatic sleep staging method (Feature Engineering and LightGBM, FLNet) is proposed. In the first stage, sleep representation and event mapping are combined with Feature Engineering. the intrinsic correlation of EEG data can be better mined by selecting the specific frequency bands that affect sleep staging and normalizing the processed EEG data and analyzing the power spectral density. In the second stage, the machine learning model LightGBM is used to fully learn the preprocessed EEG data. On the basis of ensuring the improvement of various indicators and the robustness of the network, the memory consumption is reduced and the sleep stage speed is improved. Using PhysioNet's sleep-EDFX signal database, the accuracy of multi-classification and micro F1-score were 87.32% and 86.73%, respectively. the results show that the proposed method is superior to the existing machine learning methods, and has a significant improvement in the performance of automatic sleep staging, which is more suitable for clinical application. © 2021 ACM.

关键词： Spectral density

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A machine learning Approach in data Perturbation for Privacy-Preserving data mining 4th

A Machine Learning Approach in Data Perturbation for Privacy...

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4th international conference on Smart Computing and Informatics, SCI 2020

作者： Dansana, Jayanti Singh, Adarsh KIIT Deemed To Be University Bhubaneswar Odisha India

ISBN: (纸本)9789811615016

data mining is a process where we can extract relevant information or patterns from the collection of data. In this era of big data, every organization aims to handle huge amounts of data and perform data mining techniques in order to extract pieces of information or patterns for various work and decision making. To protect privileged data and leakage of private information, the clients use different privacy-preserving techniques such as perturbation that protects client's data from revealing private information. the job of perturbing data on the client side is a herculean task, and it gets more difficult with the increase in the size of data. In this paper, we proposed a machine learning regression model that has been trained in such a way that it predicts the perturb data from original data and it even contains a comparative study of different regression models and their accuracy in perturbing the data. © 2021, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Privacy-preserving techniques

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Multimodal Physiological Analysis of Cognitive Affective Effect of Immersive learning in Virtual Reality 23

Multimodal Physiological Analysis of Cognitive Affective Eff...

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Proceedings of the 2023 4th international conference on Computer Science and Management Technology

作者： Yu Li Yang Shen Faculty of Psychology Beijing Normal University China Collaborative Innovation Center of Assessment Toward Basic Education Quality Beijing Normal University China

ISBN: (纸本)9798400709517

this paper used videos to assess the impact of emotion on learners‘ attention, and employed a Continuous Performance Test (CPT) task to evaluate the effectiveness of physiological signal pattern recognition, all under immersive virtual reality environment. A total of eighteen subjects were recruited for this study, and they were shown four videos with different emotional tendencies. After watching each video, the subjects were required to complete a mood scale and a CPT task. Simultaneously, EEG and PPG data were collected from the participants. this study found that the valence and arousal of emotions have an impact on attention, and that there are distinct physiological patterns that can be used to measure attention. In the future, it is hopefully captured through learners' multimodal data for computation to quantitatively assess learners' attention levels, providing effective means for status evaluation in learning analytics.

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Integrating Cloud-based data mining Algorithms for Smart City Infrastructure Management and Decision Support Systems

Integrating Cloud-based Data Mining Algorithms for Smart Cit...

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Soft Computing for Security Applications (ICSCSA), international conference on

作者： M. Guru Vimal Kumar J. Jude Moses Anto Devakanth D. Selvapandian Venkatesan R J. Revathi R. Aruna Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Applications Madanapalle Institute of Technology & Science Chittoor India Department of Computer Science and Engineering Academy of Higher Education Coimbatore India Department of Information Technology Bannari Amman Institute of Technology Erode India Department of Mathematics Kongu Engineering College Erode India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

this study explores cloud-based data mining algorithm integration in elevating smart city infrastructure management and decision support systems. Specifically, the authors focus on optimizing traffic management through the analysis of real-time traffic sensor data. Timestamped history records of vehicle count, traffic speed, density, weather condition, accidents, and traffic signals were utilized. machine learning models such as Support Vector machine, K-Nearest Neighbors, Decision Trees, and Random Forest were trained and tested. the analysis indicated SVM to be the most effective instrument, resulting in an accuracy of 98.98%. the model was also instrumental in reducing vehicle count by 16.7%, traffic density by 25%, and traffic speed was increased by 50%. KNN’s overall accuracy experience was 94.5%, and the model was shown to be highly optimized for various forever traffic conditions. However, the overall impact on traffic speed and density was not as significant compared to the previously-discussed model. DT and RF demonstrated respective accuracies of 92.3% and 91.2%, offering varying levels of decision interpretation and ensemble learning opportunities. As a result, the study indicates that machine learning can play a critical role in improving urban traffic management via traffic congestion prediction and minimization, resulting in improved traffic flow, overall trip time reduction, and general urban mobility management. T.

关键词： Support vector machines Decision support systems Cloud computing Accuracy Smart cities Nearest neighbor methods Real-time systems data mining Optimization Random forests

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Elevated learning based Secured Phishing Website Identification Methodology using Artificial Intelligence Assistance

Elevated Learning based Secured Phishing Website Identificat...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： G. Ramkumar Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Phishing attacks pose a huge threat for online security and therefore need advanced detecting techniques in order to mitigate the damages it causes. this paper introduces a sophisticated model for the detection of phishing websites, named the Blended ResNet-EfficientNet Model (BREM), which unifies the advantages of the ResNet and EfficientNet architectures. To address this challenge, BREM uses the rich hierarchical pattern recognition ability of ResNet-50 and the practical feature extraction capability of EfficientNet-B3 to achieve classification performance in phishing detection. On overall assessment BREM outperforms both traditional machine learning models and standalone deep learning model with an accuracy of 96%, precision at 94%, recall of 95% and FI score of 94.5%. these same authors validate the high specificity (97 %), negative predictive value (95 %) and Matthews correlation coefficient of 0.92 further underlining the robustness and reliability of BREM. this approach does not only improve the accuracy of detection, but also provides much better security against phishing campaigns. In the near future research directions such as real-time deployment, more experiments on different feature sets, adversarial robustness, transfer learning from heterogeneous datasets, model learning models and pattern recognition. they are able to draw from an immense amount of data and address minor deviances, which allows AI's better discrimination of real versus false websites, therefore increasing user cyber protections and preventable breaches [3] [4]. At the heart of securing phishing websites identification is making use of powerful AI capabilities to scan and recognize phishing convincingly. this AI utilizes different methods - natural language processing (NLP), image recognition and behavioral analysis - to keep an eye on things happening to web content and its design. Behavior analysis tracks user-interactions and web behavior to detect for abnormal operations, which co

关键词： Accuracy Phishing Scalability Transfer learning Robustness Natural language processing Real-time systems pattern recognition Security Protection

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Predicting Indian Sign Language Gestures Using Deep learning and machine learning Techniques

Predicting Indian Sign Language Gestures Using Deep Learning...

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Sustainable Expert Systems (ICSES), international conference on

作者： Bhushan Fulkar Rushikesh Burle Pramod Mahale Utkrsha Pacaharaney Department of Artificial intelligence And Data science Datta Meghe institute of Higher Education and Research Wardha India Department of Artificial Intelligence And Machine Learning Datta Meghe institute of Higher Education and Research(DU) Wardha Maharashtra Department of Artificial Intelligence And Machine Learning Datta Meghe institute of Higher Education and Research Wardha India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Indian Sign Language is the language used by people with disabilities in India. Unfortunately, the general public's ignorance of sign language makes it difficult for the especially abled and the general public to communicate. We are releasing a strategy to reduce this difference. We have adopted two distinct strategies to deal with this problem. first by employing the depth+RGB data acquired with a Microsoft Kinect to make gesture predictions in real-time. To segment the hand region from the RGB-D camera data, we used affine transformation and 3D reconstruction to map the RGB and depth information. Convolutional neural networks were trained with inputs that were segmented hand images or videos. With 50 static Indian Sign Language hand gestures used for training, a 97.18% classification accuracy was achieved on test data. Furthermore, this model achieved a 97.71 % classification accuracy when we transferred learning American Sign Language. A convolutional kernel and an LSP were used to train twenty dynamic gestures. this model's classification accuracy was 98.88%. Nevertheless, as soon as we implemented this system, we found that the methodology was flawed. Essentially, it is absurd to need to carry around both a bulky Microsoft Kinect and a device capable of performing the computation required for human-to-person interaction. We attempted to address this issue by using semantic segmentation of the hands. U-Net and ResNet 101 were combined.

关键词： Training Sign language Accuracy three-dimensional displays Webcams Speech recognition Auditory system Speech enhancement Real-time systems Videos

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A data-Driven machine learning Approach for Human Gender Classification Using Explainable AI

A Data-Driven Machine Learning Approach for Human Gender Cla...

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international conference on Computer, Communication, Control and Information Technology (C3IT)

作者： Shakib Mahmud Dipto Adria Binte Habib Nadia Tasnim Mim Salman Ibne Eunus Nabiha Tasnim Orchi Department of Computer Science and Engineering University of Liberal Arts Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering BRAC University Dhaka Bangladesh

ISBN: (数字)9798350389128

ISBN: (纸本)9798350389135

Since gender recognition contains a wealth of information about the differences between male and female characteristics, it is crucial and essential for many applications in commercial domains, such as human-computer interaction applications and computer-aided physiological or psychological analysis. there are lots of scientific studies that contain different approaches to classify gender. Apart from those, we have included Explainable Artificial Intelligence to interpret the usability of the model. In this paper, we study a gender classification dataset to predict gender from tabular data of specific human body features such as hair length, forehead length, height, nose width, and length. We have used several machine learning (ML) algorithms such as - Gaussian Naive Bayes (GNB), Support Vector machine (SVM), Decision Tree (DT), and Stochastic Gradient Descent (SGD) to predict gender. After that, we performed a comparison study and implemented Lime on the best performing ML model to interpret the prediction of the model. the Gaussian Naive Bayes architecture with the dataset shows a reasonable accuracy of 98%, proving the usability of the model.

关键词： Biometrics Support vector machines Human computer interaction Accuracy Recurrent neural networks Explainable AI Computer architecture Predictive models Security Usability

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