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共 31059 条记录，以下是4921-4930 订阅

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User Interface Design’s Impact on Customer Satisfaction and Loyalty in SaaS E-Commerce

User Interface Design’s Impact on Customer Satisfaction and...

引用

Creative Communication and Innovative technology (ICCIT), IEEE

作者： Eka Purnama Harahap Pandu Hermawan Dhiyah Ayu Rini Kusumawardhani Nina Rahayu Maulana Arif Komara Harry Agustian Master of Technology Information University of Raharja Tangerang Indonesia Dept. of Information System Bina Bangsa University Serang Indonesia Dept. of Information System Pandawan Sejahtera Indonesia Tangerang Indonesia Dept. of Computer Systems University of Raharja Tangerang Indonesia Dept. of Digital Bussiness CAI Sejahtera Indonesia Tangerang Indonesia Dept. of Computer Systems Sesindo Tangerang Indonesia

ISBN: (数字)9798350367492

ISBN: (纸本)9798350367508

This study aims to analyze the impact of user interface (UI) design on customer satisfaction and loyalty in SaaS e-commerce applications, using a quantitative approach to measure the perceptions of SaaS e-commerce application users and determine how visual aspects and interface functionality contribute to the user experience, with data analysis conducted using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method, indicating that an intuitive and responsive interface design significantly influences increased customer satisfaction, which in turn strengthens their loyalty to the application; the study seeks to provide insights that developing an effective user interface is crucial for building long-term relationships with customers and reaffirms the importance of integrating good interface design in e-commerce application development strategies, and further suggests that incorporating user-centered design principles and continuously collecting user feedback can enhance the user experience, thereby allowing developers to create interfaces that not only attract users but also retain them, essential for e-commerce businesses aiming to improve user engagement and loyalty in a highly competitive market.

关键词： Customer satisfaction Buildings Psychology Virtual reality User interfaces User experience Mathematical models Electronic commerce Usability Business

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Recognition of Human Anomalies Using Convolution Neural Network

Recognition of Human Anomalies Using Convolution Neural Netw...

引用

Advances in Data engineering and Intelligent Computing Systems (ADICS), International Conference on

作者： Teja Parella Balakrishna Ponnam Santhiya P Praveen. A Sujihelen. L dept. of Computer Science and engineering Sathyabama Institute of Science and Technology Chennai Tamilnadu India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

By instantly identifying suspicious occurrences, people, and things, smart CCTV systems greatly improve monitoring capacities. In this research, we investigate how to enhance CCTV video stream anomaly detection using state-of-the-art machine-vision and machine-learning techniques, namely UNET and ALEXNET. In contrast to more conventional methods that depend on supervised learning, this research makes use of uncontrolled and semi-supervised learning techniques to tackle the variety and complexity of anomalies. Using algorithms like UNET and ALEXNET, the system can learn to spot outliers on its own, with little to no help from humans. Because it can tell the difference between normal and abnormal situations, this method improves monitoring efficiency and decreases false alarms. In addition, the system maintains original quality for unusual occurrences while keeping typical video at a lower level, optimizing storage economy. By combining UNET and ALEXNET algorithms, smart CCTV systems may detect suspicious events before they happen, which increases security and allows for faster reactions to any dangers.

关键词： Training System performance Supervised learning Streaming media Semisupervised learning Recording Security

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Addressing the Imbalanced Class Distribution in Fatty Liver Detection in CT Images Using Transfer Learning

Addressing the Imbalanced Class Distribution in Fatty Liver ...

引用

International Conference on information and Communication Systems (ICICS)

作者： Hamzeh Hailat Ahmad M. Mustafa Hassan Najadat Mamoon Alomari Sofyan Freihat Dept. of Computer Information Systems Jordan University of Science and Technology Irbid Jordan Dept. of Diagnostic Radiology and Nuclear Medicine Jordan University of Science and Technology Irbid Jordan

ISBN: (数字)9798331540760

ISBN: (纸本)9798331540777

Addressing the common challenges posed by limitations in medical images, our study involved the collection of CT images from the abdominal area of patients. We meticulously selected the appropriate shot for diagnosing fatty liver disease and employed the appropriate data augmentation techniques suitable for medical images to overcome the scarcity of positive samples. Three pre-trained models, namely InceptionResNetV2, EfficientNetB4, and EfficientNetV2S, were used in the study to focus on the use of transfer learning; the performance of these models was evaluated both before and after applying data augmentation techniques. After obtaining the results, the EfficientNetV2S model produced promising results for the fatty liver disease classification. F1-score, Test Accuracy, and Test Loss on the balanced dataset were 0.86, 0.93, and 0.19, respectively.

关键词： Accuracy Liver diseases Communication systems Computed tomography Transfer learning Data augmentation Data models

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EEG Signal Power Prediction Using DEAP Dataset 7

EEG Signal Power Prediction Using DEAP Dataset

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7th International Conference on Intelligent Informatics and Biomedical Sciences, ICIIBMS 2022

作者： Munoz-Gonzalez, Angel Horie, Ryota Shibaura Institute of Technology Functional Control Systems Course Tokyo Japan Shibaura Institute of Technology Dept. of Information and Communications Engineering Tokyo Japan

ISBN: (数字)9781665482295

ISBN: (纸本)9781665482295

When we listen to music, our emotions can change due to changes in our brain response. Because of this, a large number of research projects for classifying the emotional response using brain signals when listening to music through machine learning techniques emerged. On the contrary, to our knowledge, there is no previous research attempting to estimate the dynamic changes in the electroencephalogram (EEG) response under music stimuli through machine learning techniques. Therefore, in this manuscript, we proposed an approach to predict and anticipate changes in the EEG signal under music stimuli. Using the DEAP dataset, we split the EEG response to music stimuli into one-second length frames. After that, we compared the changes in the power of the brain signal of consecutive frames through two one-tailed Wilcoxon rank-sum tests. This test allowed us to label the changes in the second frame as "lower", "similar"or "higher"signal compared to the first frame. Then, we attempted to predict these changes using a Support-Vector Machine (SVM) classifier with stratified 5-fold validation with different input combinations (only music, only brain signal, or a combination of both). Due to the use of multi-label classification with imbalanced data, we measured the results through F1-Scores. Over chance level predictions of the changes of signal power were obtained when using the previous second brain signal for the different channels and bands, especially in the frontal F3 and F4 channels. © 2022 IEEE.

关键词： Electroencephalography

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AI-Powered Human-computer Interaction Assisting Early Identification of Emotional and Facial Symptoms of Autism Spectrum Disorder in Children: “A Deep Learning-Based Enhanced Facial Feature Recognition System”

AI-Powered Human-Computer Interaction Assisting Early Identi...

引用

Machine Intelligence and Smart Innovation (ICMISI), International Conference on

作者： Jasmine ElMahalawy Yehia A. ElSwaify Diaa Elliboudy Omar M. Abbas Nour Moustafa Nayera Wael dept. of Computer Engineering Arab Academy for Science Technology and Maritime Transport Alexandria Egypt

ISBN: (数字)9798350365740

ISBN: (纸本)9798350365757

Autism spectrum disorder (ASD) diagnosis often benefits from early identification. This paper proposes an enhanced deep learning model for analyzing facial features in children to aid in early ASD detection. The model integrates the YOLO architecture with convolutional neural networks (CNNs) employing transfer learning. Trained on a Kaggle dataset of 2,836 facial images (1,418 autistic and 1,418 non-autistic children), the model achieves high accuracy with MobileNet (95%), Xception (94%), and InceptionV3 (89%). This approach has the potential to improve existing detection methods for ASD and contribute to better human-computer interaction for individuals with special needs. Notably, the model demonstrates high specificity, effectively distinguishing autistic from non-autistic cases across various facial expressions in benchmark datasets.

关键词： Human computer interaction Autism Accuracy Sensitivity Face recognition Transfer learning Psychology

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Comparative Study of Face Tracking Algorithms for Remote Photoplethysmography

Comparative Study of Face Tracking Algorithms for Remote Pho...

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Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： J.A.S.Y. Jayasinghe Stamos Katsigiannis Lakmini Malasinghe Dept. of Electrical and Electronic Engineering Sri Lanka Institute of Information Technology Sri Lanka Dept. of Computer Science Durham University Durham United Kingdom

Remote Photoplethysmography (rPPG) is a non-invasive approach for monitoring Heart Rate (HR) that can be used in various applications in healthcare and biometrics. rPPG measurements acquired using facial videos have become very popular and one of the main steps of this technique is facial tracking and Region of Interest (ROI) extraction. This research paper investigates four widely used face tracking algorithms, namely MediaPipe Face Mesh (MPFM), Haar Cascade, Multi-task Cascaded Convolutional Network (MTCNN), and Dlib, concerning their ROI extraction capabilities for rPPG HR measurements. Using evaluation metrics such as accuracy, processing time, and ease of extracting ROIs, this work also recommends the most suitable face tracking algorithm from those mentioned above for rPPG measurements, and presents a compilation of a prioritization list of ROIs based on their sensitivities for rPPG measurements. Experimental results showed that the MPFM algorithm and cheek ROIs provided the best HR measurements.

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Sea Erosion Prediction and Monitoring Using IOT and ML

Sea Erosion Prediction and Monitoring Using IOT and ML

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Recent Trends in Microelectronics, Automation, Computing and Communications Systems (ICMACC), International Conference on

作者： Usha Bala V Harshavardhan Sai. Y Slipa Shramila V Asritha M Bharath M A. Chandu Dept. of Computer Science & Engineering Anil Neerukonda Institute of Technology & Sciences Visakhapatnam India

ISBN: (数字)9798350366570

ISBN: (纸本)9798350366587

One of the major environmental issues that the world is currently facing is sea erosion, which requires an innovative solution for early detection and mitigation. Our paper focuses on the integrated system that combines both IoT and Machine Learning. The four main important environmental factors that are responsible for sea erosion are temperature, wave height, wave speed, and soil moisture. IoT sensors deployed along the prototype design are used to monitor these environmental parameters. Another key component is the machine learning algorithm, which is used analyse the interactions, patterns, and relations among the environmental data. By analysing the patterns among the previous data, the model can predict the likelihood of sea erosion events. This allows for the early detection of erosion events and timely interventions that can minimize the damage or loss caused by sea erosion. The main aim of this project is to help the coastal management authority take smart decisions and proactive steps using both real-time and past data analysis.

关键词： Temperature sensors Temperature measurement Prevention and mitigation Soil moisture Sea measurements Machine learning Predictive models Monitoring Intelligent sensors Erosion

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Optimized Parameter Design of Binocular Structured Light System for Precision Reflective Surface CPU in Industrial Assembly Processes

Optimized Parameter Design of Binocular Structured Light Sys...

引用

IEEE Region 10 International Conference TENCON

作者： Cheuk Tung Shadow Yiu Kam Tim Woo Dept. of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

ISBN: (数字)9798350350821

ISBN: (纸本)9798350350838

This paper constructs a simulated CPU assembly line robot system. The system incorporates binocular structured light vision technology for CPU recognition and localization. The reflective surface of the CPU poses a significant challenge for 3D reconstruction, mainly when dealing with tilted angle situations. This inherent characteristic of the CPU adds complexity to the vision system to capture and reconstruct the object's 3D information accurately. This research aims to optimize the vision system's key parameters, balancing cost-effectiveness and the required precision. By achieving the necessary level of accuracy in picking the CPUs to the desired location, our work aims to enhance robotic assembly operations, offering an optimized parameter configuration approach and supporting the needs of modern industrial processes.

关键词： Robotic assembly Location awareness Surface reconstruction Three-dimensional displays Accuracy Machine vision Refining Assembly Surface treatment Robots

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Resume Parser and Job Description Matcher

Resume Parser and Job Description Matcher

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International Conference on Computation System and information technology for Sustainable Solutions (CSITSS)

作者： Spandana Anilkumar Kamkar Srushti Sanjay Vaishnavi P S Chaitra M Dept. of Computer Science and Engineering BNM Institute of Technology Affiliated to VTU Bangalore India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Resume Parser and Job Description Matcher is an innovative project aimed at enhancing the efficiency and accuracy of job candidate screening and matching processes. Leveraging natural language processing (NLP) techniques, particularly using SpaCy and machine learning models, the system parses resumes and job descriptions to extract relevant skills, experience, and qualifications. By analyzing textual data and applying advanced algorithms, such as Support Vector Machines (SVM), the system learns to match candidates with job postings based on keyword relevance and contextual understanding. Our paper demonstrates improved matching results compared to traditional methods, facilitating streamlined recruitment processes, and enhancing workforce management strategies.

关键词： Support vector machines Machine learning algorithms Resumes Machine learning Natural language processing Vectors Data mining information technology Recruitment Qualifications

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The Low-Complexity Timing Synchronization Circuits for IEEE 802.15.4a/z IR-UWB Systems

The Low-Complexity Timing Synchronization Circuits for IEEE ...

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Novel Intelligent and Leading Emerging Sciences Conference (NILES)

作者： Kuan-Wei Wang Chung-An Shen Chin-Ya Huang Electronic and Computer Engineering Dept. National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (数字)9798350378511

ISBN: (纸本)9798350378528

This paper presents the design and implementation of a low-complexity timing synchronization circuit for the IEEE 802.15.4a/z IR-UWB wireless communication standard. The circuit structure of a low-complexity correlator for complex ternary values is designed. Moreover, a two-stage synchronization approach is utilized for a more accurate sample alignment. Therefore, a balance between the performance of synchronization and hardware complexity is achieved. The proposed synchronization architecture is designed and implemented based on the Xilinx ZC706 FPGA platform. The experimental results show that the proposed design saves 74% of buffer and 87.4% of multiplier utilization.

关键词： IEEE 802.15 Standard Wireless communication Correlation Estimation computer architecture Hardware Correlators Synchronization Field programmable gate arrays Standards

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