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Enhancing Cross-Market Recommendation System with Graph Isomorphism Networks: A Novel Approach to Personalized User Experience 24

Enhancing Cross-Market Recommendation System with Graph Isom...

引用

9th international conference on machine learning Technologies (ICMLT)

作者： Ozturk, Sumeyye Ercan, Ahmed Burak Tugay, Resul Oguducu, Sule Gunduz Istanbul Tech Univ Istanbul Turkiye Gazi Univ Ankara Turkiye

ISBN: (纸本)9798400716379

In today's world of globalized commerce, cross-market recommendation systems (CMRs) are crucial for providing personalized user experiences across diverse market segments. However, traditional recommendation algorithms have difficulties dealing with market specificity and data sparsity, especially in new or emerging markets. In this paper, we propose the CrossGR model, which utilizes Graph Isomorphism Networks (GINs) to improve CMR systems. It outperforms existing benchmarks in NDCG@10 and HR@10 metrics, demonstrating its adaptability and accuracy in handling diverse market segments. the CrossGR model is adaptable and accurate, making it well-suited for handling the complexities of cross-market recommendation tasks. Its robustness is demonstrated by consistent performance across different evaluation timeframes, indicating its potential to cater to evolving market trends and user preferences. Our findings suggest that GINs represent a promising direction for CMRs, paving the way for more sophisticated, personalized, and context-aware recommendation systems in the dynamic landscape of global e-commerce.

关键词： Cross-market recommendations graph isomorphism networks (GINs) user-item interaction modeling market specificity in e-commerce pattern recognition data mining

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Early recognition of Sudden Cardiac Death Combined with Deep Transfer learning and Superficial ECG Features 4

Early Recognition of Sudden Cardiac Death Combined with Deep...

引用

4th IEEE international conference on pattern recognition and machine learning, PRML 2023

作者： Zhang, Yue Cha, Xingzeng Cui, Bingqian Lai, Dakun University of Electronic Science and Technology of China School of Electronic Science and Engineering Chengdu China

ISBN: (纸本)9798350324303

Sudden cardiac death (SCD) is a global public safety issue that poses a serious threat to human life and health. Moreover, the clinical records of SCD data are extremely limited. this paper proposed an early prediction algorithm for SCD based on deep transfer learning. the algorithm used a two-stage training approach, which included pre-training and fine-tuning of convolutional neural networks (CNNs) to automatically capture the deep heart rate features (HRFs) in high-risk electrocardiogram (ECG) signals before SCDs. the aim is to explore automatic early prediction of SCD under limited ECG data training. this paper extracted three HRFs as input sets from 30 second single lead ECG signals of 18 patients with malignant arrhythmia and 18 healthy subjects, including RR interval, dRR interval, and turbulence onset (TO). then, the ten-fold crossover method was used to evaluate the classification performance of this model. Finally, the results showed that the average accuracy (Acc), sensitivity (Se), and specificity (Sp) of predicting SCD within 30 minutes before onset were 91.79%, 87.00%, and 96.63%, respectively. the transfer learning algorithm proposed in this paper effectively addresses the issue of insufficient data in the early prediction models of SCD. It also achieves early and accurate prediction of high-risk signs of SCD by using ECG. this is expected to form an intelligent and effective monitoring and early warning technology for high-risk patients of SCD. © 2023 IEEE.

关键词： Electrocardiograms

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patterns recognition of Sentimental patterns from the X-dataset using Deep learning Algorithm 4

Patterns Recognition of Sentimental Patterns from the X-Data...

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4th international conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Konduri, Praveen S R Department of Data Science School of Computing Mohan Babu University Tirupati India

ISBN: (纸本)9798331515720

this research investigates the application of deep learning techniques to sentiment analysis, a field focused on mining online platforms for subjective assessments. By combining deep neural networks (DNNs) with particle swarm optimization (PSO), we propose a hybrid method to improve feature selection and enhance the accuracy of sentiment classification. the study employs a dataset collected from Twitter using the Ruby Twitter API and evaluates the performance of the proposed method using k-fold cross-validation. the results are compared with the previously used firefly search (FS) method to demonstrate the effectiveness of the hybrid approach. this research contributes to the advancement of sentiment analysis techniques and their application in various domains, such as social media monitoring and market research. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Tunisian Sign Language recognition and Translation Using Deep learning 4th

Tunisian Sign Language Recognition and Translation Using Dee...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： El Askri, Marah Basly, Hend Bchir, Riadh Zayene, Mohamed Amine Sayadi, Fatma Ezzahra Univ Sousse Natl Engn Sch Sousse Lab Networked Objects Control & Commun Syst NOCCS Eniso Sousse Tunisia Univ Monastir Fac Sci Monastir Monastir Tunisia

ISBN: (纸本)9783031821554;9783031821561

Human activity recognition, a dynamic field within computer vision and machine learning, aims to accurately identify and interpret human actions or activities using video data. this paper delves deeper into the domain by concentrating on Sign Language (SL) as a unique and intricate form of human activity. Sign Language (SL) is a vital communication tool, used by individuals who are deaf or hard of hearing, facilitating their interaction and engagement with others in a visually expressive manner. A lot of research and projects in SL are being done, and it has been observed that most of them are done in American Sign Language (ASL) or Indian Sign Language (ISL), but no such progress has been carried out for the other SLs. therefore, this paper aims to present a work done in Arabic sign language (ArSL), especially Tunisian SL. the main idea is to recognize Tunisian SL and then translate it into spoken language using artificial intelligence (AI) methods. the purpose of this work is to ease the communication between individuals who are deaf and those who are not. the proposed method uses holistic landmarks with sequence key points for Tunisian sign language and trains it with a Long Short-Term Memory (LSTM) model. the results show that the proposed model can be used for real-time SL estimation, providing a better interpretation method for the deaf community. As a result, our model achieved a validation accuracy of 98% with a cross-validation technique.

关键词： Deep learning LSTM Sign Language recognition

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Comparison of Multi-Modal Federated learning Framework and SPSS in the Evaluation of Lymph Node Metastasis Probability in Gynecological Malignancies 4

Comparison of Multi-Modal Federated Learning Framework and S...

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4th IEEE international conference on pattern recognition and machine learning, PRML 2023

作者： Hu, Zhijun. Ma, Ling. Yue, Ding Wu, Guangyu Shi, Xiaohua Sirejiding, Shalayiding Liu, Kaijiang. Shanghai Jiao Tong University Renji Hospital School of Medicine Department of Gynecologic Oncology Shanghai China Library Shanghai Jiao Tong University Shanghai China Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai China Shanghai Jiao Tong University School of Medicine Department of Radiology Renji Hospital Shanghai China

ISBN: (纸本)9798350324303

this study aimed to evaluate the diagnostic efficacy of CT, MRI, and PET/CT in detecting lymph node metastasis (LNM) in patients with gynecologic malignancies. A total of 567 patients diagnosed with cervical carcinoma (CC) and endometrial carcinoma (EC) were included in this study. the results of this study indicated that PET/CT demonstrated higher levels of sensitivity but lower levels of specificity compared to CT and MRI. Additionally, a deep learning-based multimodal federated learning model was implemented, which integrated non-image and MRI data, to improve diagnostic accuracy in the detection of LNM. the model exhibited heightened sensitivity and specificity in comparison with individual imaging modalities. Overall, the findings of this study underscore the potential of employing multimodal federated learning models to accurately detect LNM in patients with gynecological malignancies, which in turn emphasizes the necessity for further research and validation. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Comparative Analysis of machine learning Techniques for Concrete Compressive Strength Prediction 4

Comparative Analysis of Machine Learning Techniques for Conc...

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4th international conference on data Analytics for Business and Industry, ICDABI 2023

作者： Musleh, Fuad Taha, Ranyah Musleh, Abdel Rahman University of Bahrain Civil Engineering Department Isa Town Bahrain Al Iman School Computer Department Isa Town Bahrain University of Bahrain Electrical Engineering Department Isa Town Bahrain

ISBN: (纸本)9798350369786

the compressive strength of concrete is of utmost importance in ensuring the structural stability and integrity of construction projects. With increasing importance placed on this aspect, there is a demand for intelligent and efficient techniques to accurately predict and evaluate compressive strength. machine learning methods (ML) present a promising approach to developing accurate models for assessing the compressive strength of concrete. By leveraging the power of data analysis and pattern recognition, these techniques enable the construction industry to obtain reliable and robust concrete for a wide range of applications. ML models can identify the key factors influencing compressive strength, allowing for more informed decision-making, and ensuring the construction of structurally sound and durable projects. this article explores the assessment and comparison of four machine learning (ML) methods, namely Gradient Boosting, Categorical Gradient Boosting CatBoost, Random Forest (RF), and extreme gradient boosting (XGBoost) regressors, for predicting concrete compressive strength. Among these techniques, the CatBoost Regressor demonstrated superior performance in terms of accuracy, R-squared coefficient of determination, and Root Mean Square Error RMSE, surpassing the other three methods. © 2023 IEEE.

关键词： Adversarial machine learning

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Empowering Healthcare with Deep learning: An Application for Early Detection of Skin Cancer 4th

Empowering Healthcare with Deep Learning: An Application for...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Oluwatobi, Komolafe Joseph Asghar, Ikram Diyan, Muhammad Nawaz, Rab Saleem, Sidra Ullah, Rahmat Ahmad, Jawad Univ Teesside Sch Comp Engn & Digital Technol Middlesbrough Cleveland England Univ Agr Faisalabad Faisalabad Pakistan Univ Essex Sch Comp Sci & Elect Engn Colchester Essex England Edinburgh Napier Univ Edinburgh Midlothian Scotland

ISBN: (纸本)9783031821523;9783031821530

Skin cancer is a hazardous ailment and a leading contributor to mortality. Early diagnosis of skin cancer can significantly minimize or prevent these fatalities. the diagnostic process can be both lengthy and costly. this study aims to establish a basis for creating an exact and effective convolutional neural network (CNN) model for detecting skin cancer. this model aims to enhance the early and accurate diagnosis of skin cancers, potentially leading to a decrease in the number of associated fatalities. the model utilizes a convolutional neural network with Keras Tensorflow as the backend to categorize seven distinct categories of skin cancer. Subsequently, the results are analyzed to determine the practical applications of the model. the MobileNet optimizer is used for classification. A web application has been developed that offers dermatologists the three most likely diagnostics for a specific blister. It will aid in swiftly recognizing patients with high priorities and accelerating their workflow. the application generates a result within a time frame of 5-10 seconds.

关键词： Skin Cancer CNN machine learning image classification medical data security

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4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

4th International Conference on Pattern Recognition and Arti...

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4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

ISBN: (纸本)9789819787043

the proceedings contain 69 papers. the special focus in this conference is on pattern recognition and Artificial Intelligence. the topics include: Hierarchical Attention Decoder for Solving Math Word Problems;enhancing Output Diversity Improves Conjugate Gradient-Based Adversarial Attacks;open-World Visual Reasoning by a Neuro-Symbolic Program of Zero-Shot Symbols;the Hidden Influence of Latent Feature Magnitude When learning with Imbalanced data;AM-SORT: Adaptable Motion Predictor with Historical Trajectory Embedding for Multi-Object Tracking;character Identifying Video Language Alignment Network for Weakly-Supervised Video-Subtitle Moment Retrieval;spiral Patch Exemplar-Based Inpainting of 3D Colored Meshes;BEVFormer’s Plugin: Integrating Historical Detection data, BEV-Level Information, Time Information, and Depth Information;adjustable Visual Appearance for Generalizable Novel View Synthesis;flexible Video Matting With Temporally Coherent Trimaps Generation;adversarial Augmentation Training Makes Action recognition Models More Robust to Realistic Video Distribution Shifts;egocentric View Hand Action recognition by Leveraging Hand Surface and Hand Grasp Type;visual Concept Networks: A Graph-Based Approach to Detecting Anomalous data in Deep Neural Networks;contrastive Language-Entity Pre-training for Richer Knowledge Graph Embedding;Fine-Tuning Pre-trained Model with Prototype learning for OOD Detection;prototype-Guided Contrastive Knowledge Graph Representation learning for Diagnosis Prediction;stop Overkilling Simple Tasks with Black-Box Models, Use More Transparent Models Instead;feature-Based Explainable Reinforcement learning in Environments with Multiple Sources of Risk;probing Network Decisions: Capturing Uncertainties and Unveiling Vulnerabilities Without Label Information;enhancing Robustness of Over-Parameterized Models via Feature Reweighting Using Logit-Wise Mixup;language-Aware Non-autoregressive Khmer Textline recognition;ATA: Attentive Text Augment

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4th international Mobile, Intelligent, and Ubiquitous Computing conference, MIUCC 2024

4th International Mobile, Intelligent, and Ubiquitous Comput...

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4th international Mobile, Intelligent, and Ubiquitous Computing conference, MIUCC 2024

ISBN: (纸本)9798350367775

the proceedings contain 69 papers. the topics discussed include: a comprehensive survey on dimension reduction for medical big data using optimization algorithms;software product discovery as a crucial part of software product success;a comparative study of different smart university frameworks;intelligent decision support system for loan evaluation using machine learning;real-time boundary detection for continuous Arabic sign language translation;trade-offs in performance and optimization techniques in Unity 3D to achieve realism;transfer learning based indoor object recognition for visually impaired people;truth revealed: enhancing deception detection using long-term recurrent convolutional networks;and developing a medical question-answering chatbot for Egyptian Arabic: a comparative study.

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4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

4th International Conference on Pattern Recognition and Arti...

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4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

ISBN: (纸本)9789819787012

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