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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Vannurswamy, K. Shekar, B.H. Pilar, Bharathi Karunakar Kotegar, A. Jiang, Frank Mangalore University Dept of Computer Science Mangalore India Mangalore University Dept of Computer Science Konaje Mangalore India University College Mangalore Dept of Computer Science Mangalore India Mahe Manipal Dept of Data Science Manipal India Deakin University School of Information Tech Melbourne Australia

ISBN: (纸本)9798350376876

This paper introduces a new multimodal biometric system integrating facial and iris recognition using discrete wavelet transform (DWT) and long short-term memory (LSTM) neural networks enhanced with an attention mechanism. DWT is effective in extracting global and local features, and these extracted traits are addressed as instances of serial data to enable LSTMs to pick temporal connections in and provide context-specific continuations with continual regularity;other than these factors, improvement of the features has a major impact because attention emphasizes exactly on where more contextual contribution would occur with these elements to allow further contextual insights during the output phase. Experimental results show commendable gains in performance as compared with conventional systems, and achieves identification of up to 9 9. 9 4 %. It can be utilized in security environment where accurate identification with biometrics and proper authentication is highly needed. Framework: Features acquired from the DWT are integrated inside an LSTM network. Thus, it provides guidelines for both the structured feeding schedule and the organizing of time data, thus better facilitating biometric recognition. Though some progress has been made in optimizing biometric identification for individuals with varied features, there is still much to be done. © 2024 IEEE.

关键词： Authentication

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Unveiling Consumer Segmentation: Harnessing K-means Clustering Using Elbow and Silhouette for Precise Targeting 3rd

Unveiling Consumer Segmentation: Harnessing K-means Clusteri...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Saraswat, Shweta Agrohi, Vaibhav Kumar, Mahesh Lamba, Monica Kaur, Raminder Department of Artificial Intelligence and Data Science Department Arya College of Engineering Rajasthan Jaipur India Department of Computer Science and Engineering Department Arya College of Engineering Rajasthan Jaipur India Department of Computer Science and Engineering Department Graphics Era Deemed to Be University Dehradun India Department of Computer Science and Engineering Department JECRC University Jaipur India

ISBN: (纸本)9789819708918

Consumer segmentation is essential for accurate targeting and successful marketing efforts in today’s competitive business environment. Modern marketing groups individuals by interests and attributes. Segmentation drives targeting, personalization, and ROI. This article segments customers using K-means clustering. The Elbow approach and Silhouette score determine the appropriate number of clusters and increase segmentation accuracy. They also examine the possibility of precision targeting and customized marketing techniques across sectors. Businesses may optimize marketing, improve customer happiness, and increase profits by using K-means clustering. To compete in today’s market, this research helps marketers enhance targeting. Elbow and Silhouette K-means clustering may enhance client segmentation, engagement, loyalty, and economic success. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： K-means clustering

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Explaining the Effects of Preprocessing on Constraint Satisfaction Search 30th

Explaining the Effects of Preprocessing on Constraint Sat...

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30th Irish Conference on Artificial Intelligence and Cognitive science, AICS 2022

作者： Wallace, Richard J. Insight Centre for Data Analytics and Department of Computer Science University College Cork Cork Ireland

ISBN: (纸本)9783031264375

Preprocessing constraint satisfaction problems is a much studied method for improving the performance of subsequent solution search. The traditional explanation for its beneficial effects is "problem reduction", where possible values that cannot take part in a solution are discarded, leaving fewer possibilities to explore during search. Here, we show that this is not the only or even the main factor when dynamic variable ordering heuristics are used. Multiple lines of evidence indicate that under these conditions domain reductions effected by preprocessing serve to inform the heuristic as to which variables should be chosen for instantiation before others. It is suggested that an information transmission model is needed to account for such effects, and it is argued that an extension of this approach can incorporate simple domain reduction effects as well. © 2023, The Author(s).

关键词： Constraint satisfaction problems

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Deep Ensemble Learning Approach for Face Anti-Spoofing Detection based on Pre-trained Models

Deep Ensemble Learning Approach for Face Anti-Spoofing Detec...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Vannurswamy, K. Shekar, B.H. Pilar, Bharathi Karunakar Kotegar, A. Jiang, Frank Mangalore University Dept of Computer Science Mangalore India Mangalore University Konaje Dept of Computer Science Mangalore India University College Mangalore Dept of Computer Science Mangalore India MAHE Manipal Dept of Data Science Manipal India Deakin University School of Information Tech Melbourne Australia

ISBN: (纸本)9798350376876

In this paper, we propose a Weighted Deep Ensemble Learning (WDEL) to increase the overall accuracy of face anti-spoofing model by exploiting multiple learning architectures. Current anti-spoofing models based on one deep learning architecture are not able to extract all important information for discriminating real and fake faces. We therefore design a framework that fuses the information of five pre-trained deep learning models, Inception, Xception, VGG16, ResNet50 and MobileNet to create a high-quality feature representation against spoofing attacks. Moreover, we apply a weighted voting mechanism with each weight being reliant on the corresponding classifier's performance from real and spoof classification considering different metrics: precision for the non-spoof classification and recall for the spoof classification. The final prediction is obtained by aggregating these multiple classifiers using their optimal weights in an ensemble fashion. Additionally, we provide an experiment comparing the performance of these aforementioned pre-trained deep learning models. The comparison reveals that MobileNet, Xception, and ResNet50 exhibit the highest recall for spoofing and precision for real images, along with improved F1 score values in detection. This establishes them as optimal choices for facial spoofing detection. © 2024 IEEE.

关键词： Federated learning

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Monte Carlo Simulation-Based Robust Workflow Scheduling for Spot Instances in Cloud Environments

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Tsinghua science and Technology 2024年第1期29卷 112-126页

作者： Quanwang Wu Jianzhao Fang Jie Zeng Junhao Wen Fengji Luo College of Computer Science Chongqing UniversityChongqing 400044China National Experimental Teaching Demonstration Center Chongqing UniversityChongqing 400044China College of Big Data and Software Engineering Chongqing UniversityChongqing 400044China School of Civil Engineering The University of SydneySydney 2006Australia

When deploying workflows in cloud environments,the use of Spot Instances(SIs)is intriguing as they are much cheaper than on-demand ***,Sls are volatile and may be revoked at any time,which results in a more challenging scheduling problem involving execution interruption and hence hinders the successful handling of conventional cloud workflow scheduling *** some scheduling methods for Sls have been proposed,most of them are no more applicable to the latest Sls,as they have evolved by eliminating bidding and simplifying the pricing *** study focuses on how to minimize the execution cost with a deadline constraint when deploying a workflow on volatile Sls in cloud *** on Monte Carlo simulation and list scheduling,a stochastic scheduling method called MCLS is devised to optimize a utility function introduced for this *** the Monte Carlo simulation framework,MCLS employs sampled task execution time to build solutions via deadline distribution and list scheduling,and then returns the most robust solution from all the candidates with a specific evaluation mechanism and selection *** results show that the performance of MCLS is more competitive comparedwithtraditionalalgorithms.

关键词： constrained optimization Monte Carlo simulation robustness Spot Instances(Sls) workflow scheduling

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CRP2-VCS: Contrast-Oriented Region-based Progressive Probabilistic Visual Cryptography Schemes

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5501-5517页

作者： Wu, Xiaotian Song, Bofan Fang, Jia Yan, WeiQi Peng, Qing-Yu Jinan University Guangdong Key Laboratory of Data Security and Privacy Preserving College of Cyber Security College of Information Science and Technology Guangzhou China Auckland University of Technology Department of Computer and Information Systems Auckland New Zealand Jinan University Sino-French Joint Laboratory for Astrometry Dynamics and Space Science Guangzhou China

Most visual cryptography schemes (VCSs) are condition-oriented which implies their designs focus on satisfying the contrast and security conditions in VCS. In this paper, we explore a new architecture of VCS: contrast-oriented region-based progressive probabilistic VCS (CRP2-VCS). The term contrast-oriented indicates the optimality of multi-contrast is taken into consideration when producing shadows. First of all, new requirements for CRP2-VCS, described by probabilities, are introduced. As a non-interference requirement is proposed, the secret interference problem in existing region-based progressive VCS can be avoided. Then, a construction of CRP2-VCS based on a multi-contrast-maximizing model is provided. The multi-contrast-maximizing problem is essentially a probabilistic VCS model that fuses region-based sharing, multi-contrast optimization, and general access structure (GAS) together. Finally, a Max-Min based technique is adopted to solve the multi-objective optimization problem. Moreover, to further boost the visual quality, the proposed method is extended to allow employing XOR operation for image recovery. Experimental results and comparisons are demonstrated to show the effectiveness and advantages, such as optimal visual quality, non-expansible shadow and GAS sharing policy, are provided by the proposed technique. © 2025 IEEE.

关键词： Visualization Cryptography Probabilistic logic Interference Encoding Circuits and systems Polynomials Optimization Minimax techniques Computational modeling

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Secure UPI: Machine Learning-Driven Fraud Detection System for UPI Transactions 2

Secure UPI: Machine Learning-Driven Fraud Detection System f...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rani, Rupa Alam, Adnan Javed, Abdul Ajay Kumar Garg Engineering College Ghaziabad Department of Computer Science & Engineering U.P. Ghaziabad India Banasthali Vidyapith Department of Computer Science & Engineering Rajasthan India Abes Institute of Technology Department of Computer Science & Engineering-Data Science U.P. Ghaziabad India

ISBN: (纸本)9798350371055

Secure UPI specializes in developing an advanced fraud detection gadget the usage of the effective XGBoost device getting to know set of rules to create an advanced fraud identity device. XGBoost is a properly-proper alternative for enhancing the precision of fraud detection fashions because of its reputation for dealing with tricky datasets with efficiency and its music record of success throughout multiple industries. In order to extract pertinent records, like transaction quantity, frequency, and place, our approach preprocesses UPI transaction facts. This article makes use of a labelled dataset to train the XGBoost version in order that we may also take gain of its sturdy prediction talents and capacity to handle imbalanced datasets. To help create a system that is less difficult to apprehend and use, feature importance evaluation is used to discover essential symptoms of feasible fraud. After training, the model is covered right into a real-time UPI transaction tracking device, in which it maintains an eye fixed out for any suspicious traits in incoming transactions. In order to lessen the results of fraudulent activity, the system is constructed with 98.2 % accuracy to send out instant notifications and take preventive steps. This challenge allows in improving UPI transaction security and advancing economic era are accomplished through demonstrating the performance of machine learning in fraud detection. © 2024 IEEE.

关键词： Dimensionality Reduction Fraud Detection Imbalanced datasets Machine Learning PCA SMOTE UPI XGBoost

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Advancements in Liver Tumor Detection:A Comprehensive Review of Various Deep Learning Models

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computer Modeling in Engineering & sciences 2025年第1期142卷 91-122页

作者： Shanmugasundaram Hariharan D.Anandan Murugaperumal Krishnamoorthy Vinay Kukreja Nitin Goyal Shih-Yu Chen Deptartment of Artificial Intelligence and Data Science Vardhaman College of EngineeringHyderabad501218India Deptartment of CSE V.S.B College of EngineeringKarur639111India Deptartment of Electrical and Electronics Engineering Vardhaman College of EngineeringHyderabad501218India Centre for Research Impact&Outcome Chitkara UniversityRajpura140401PunjabIndia Department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendergarh123031HaryanaIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment *** Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and *** review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and *** metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical *** review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging *** suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular *** paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

关键词： Liver tumor detection liver tumor segmentation image processing liver tumor diagnosis feature extraction tumor classification deep learning machine learning

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Predicting Pre-Owned Bike Values with Machine Learning Techniques in Bangladesh 2

Predicting Pre-Owned Bike Values with Machine Learning Techn...

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2nd IEEE International Conference on data science and Network Security, ICDSNS 2024

作者： Ferdous, Kazi Hassanul Arman, Md.Shohel Ahmed, Kazi Rifat Tasnim, Nusrat Mahmud, Imran Revathi, P. Daffodil International University Diu Data Science Lab Department of Software Engineering Dhaka Bangladesh Holy Cross College Department of Computer Science Tamilnadu Tiruchirapalli India

ISBN: (纸本)9798350373110

As the importance of sustainable practices in the automobile sector grows, it's critical to anticipate motorcycle prices and offerings. With so many variables to consider when buying a secondhand motorcycle-condition, mileage, brand reputation, model characteristics, etc.-accurate pricing prediction is essential for both customers looking for good values and sellers hoping to maximize profits. This study investigates the effectiveness of four machine learning techniques Decision Tree models, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), and Stochastic Gradient Descent (SGD) Regressions-in predicting motorcycle prices using a dataset obtained from motorcycle merchants. It's noteworthy that these methods are used with a hybrid architecture to improve prediction accuracy. Different approaches are compared and contrasted to find the best fit for the given dataset, and the difficulties and roadblocks that come with them are also discussed. After extensive testing, the decision tree model's effectiveness is shown with an amazing 97% accuracy rate, providing accurate price estimates that are essential for encouraging sustainable habits in the ever-changing Internet of Things environment. With an impressive accuracy rate, this methodology provides precise pricing estimations, which are critical for supporting sustainable practices in the automotive industry. © 2024 IEEE.

关键词： Motorcycles

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Enhancing Computational Model for Human Motion Recognition Using Deep Learning

Enhancing Computational Model for Human Motion Recognition U...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Yadav, Ram Kumar Daniel, A. Kumar, Birendra Sakhare, Apeksha Moriwal, Rahul Pawar, Raju Amity university Gwalior Department of Computer Science and Engineering Gwalior India G H Raisoni college of engineering Department of Data Science Internet of Things and Cyber Security Nagpur India G H Raisoni college of engineering Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350379525

Human motion recognition (HMR) is a fundamental task in computer vision with applications in healthcare, surveillance, human-computer interaction, and intelligent environments. This paper proposes a better-performing model for HMR using VGG16 and starts the preprocessing on the diverse dataset of RGB images. Our meticulous process involves normalization, resizing, and then extracting images. These preparatory steps lay the foundation for robust model training. Exploration then takes two VGG16, EfficientNetB7 and ensemble VGG16-EfficientNet B7 deep learning architectures. © 2024 IEEE.

关键词： Motion estimation

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