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Ordered Clustering-Based Semantic Music Recommender System Using Deep Learning Selection

computers, Materials & Continua 2025年第5期83卷 3025-3057页

作者： Weitao Ha Sheng Gang Yahya D.Navaei Abubakar S.Gezawa Yaser A.Nanehkaran School of Computer Science and Technology Weinan Normal UniversityWeinan714099China School of Information&Engineering Yancheng Teachers UniversityYancheng224002China Department of Computer and Technology Engineering Qazvin BranchIslamic Azad UniversityQazvin34199-15195Iran School of Information Engineering Sanming UniversitySanming365004China Department ofManagement Information Systems Faculty of Economics and Administrative SciencesCankayaUniversityAnkara06790Türkiye

Music recommendation systems are essential due to the vast amount of music available on streaming platforms,which can overwhelm users trying to find new tracks that match their *** systems analyze users’emotional responses,listening habits,and personal preferences to provide personalized suggestions.A significant challenge they face is the“cold start”problem,where new users have no past interactions to guide *** improve user experience,these systems aimto effectively recommendmusic even to such users by considering their listening behavior and music *** paper introduces a novel music recommendation system that combines order clustering and a convolutional neural network,utilizing user comments and rankings as ***,the system organizes users into clusters based on semantic similarity,followed by the utilization of their rating similarities as input for the convolutional neural *** network then predicts ratings for unreviewed music by ***,the system analyses user music listening behaviour and music *** popularity can help to address cold start users as ***,the proposed method recommends unreviewed music based on predicted high rankings and popularity,taking into account each user’s music listening *** proposed method combines predicted high rankings and popularity by first selecting popular unreviewedmusic that themodel predicts to have the highest ratings for each *** these,the most popular tracks are prioritized,defined by metrics such as frequency of listening across *** number of recommended tracks is aligned with each user’s typical listening *** experimental findings demonstrate that the new method outperformed other classification techniques and prior recommendation systems,yielding a mean absolute error(MAE)rate and rootmean square error(RMSE)rate of approximately 0.0017,a hit rate of 82.45%,an average normalized discounted cumulative gain

关键词： Music recommender system order clustering deep learning

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TELEX: Two-Level Learned Index for Rich Queries on Enclave-based Blockchain systems

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IEEE Transactions on Knowledge and Data engineering 2025年第07期37卷 4299-4313页

作者： Wu, Haotian Tang, Yuzhe Shen, Zhaoyan Tao, Jun Lin, Chenhao Peng, Zhe The Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong Syracuse University Department of Electrical Engineering and Computer Science United States Shandong University School of Computer Science and Technology China Southeast University School of Cyber Science and Engineering China Xi'an Jiaotong University Faculty of Electronic and Information Engineering China The Hong Kong Polytechnic University Shenzhen Research Institute Hong Kong

Blockchain has become a popular paradigm for secure and immutable data storage. Despite its numerous applications across various fields, concerns regarding the user privacy and result integrity during data queries persist. Additionally, the need for rich query functionalities to harness the full potential of blockchain data remains an area ripe for exploration. In order to address these challenges, our paper first utilizes a framework based on the Trusted Execution Environment (TEE) and oblivious RAM technique to achieve both privacy and data integrity. To enhance the query efficiency over the entire blockchain, we then devise a two-level learned indexing methodology named TELEX within the TEE for both integer and string keys. We also propose different query processing algorithms for versatile query types, including exact queries, aggregate queries, Boolean queries, and range queries. By implementing the prototype and conducting extensive evaluation, we demonstrate the feasibility and remarkable improvement in efficiency compared to existing solutions. © 1989-2012 IEEE.

关键词： Query languages

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From Data to Diagnosis: Investigating Approaches in Mental Illness Detection 2

From Data to Diagnosis: Investigating Approaches in Mental I...

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2nd International Conference of Intelligent Methods, systems and Applications, IMSA 2024

作者： Montasser, Reem Kadry Mazen, Sherif A. Helal, Iman M. A. October University for Modern Sciences and Arts Faculty of Computer Science Software Engineering Department Giza Egypt Cairo University Faculty of Computers and Artificial Intelligence Information Systems Department Giza Egypt

ISBN: (纸本)9798350362633

Mental illness is a considerable global public health problem, impacting both individual well-being and society's health. The growing popularity of social media and the increase of other data sources led to more research interest in detecting mental illness with a particular focus on utilizing and fusing emotional information. Artificial intelligence techniques like natural language processing and others, have shown encouraging improvements in recognizing complex relationships in a wide range of textual data, including clinical notes, social media posts, and interviews. In this paper, we assess the different branches of artificial intelligence based on previous research in mental illness detection, discussing methods, trends, challenges, and future research directions. © 2024 IEEE.

关键词： Adversarial machine learning

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Leveraging Deep Learning Models for Accurate and Reproducible Cardiac Measurements in Echocardiography

Leveraging Deep Learning Models for Accurate and Reproducibl...

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2024 International Conference on E-Mobility, Power Control and Smart systems, ICEMPS 2024

作者： Stojmenski, Aleksandar Mihajlov, Martin Ss. Cyril and Methodius University Faculty of Computer Science and Engineering Skopje Macedonia Jozef Stefan Institute Laboratory For Open Systems and Networks Ljubljana Slovenia

ISBN: (纸本)9798350394399

The primary metrics recorded during ultrasound sessions focused on cardiac assessment are focused on the size of the heart's features, its two chambers and pre-chambers. Presently, these measurements are predominantly conducted manually, relying on the subjective judgment of medical professionals. A sonographer takes a transthoracic echocardiogram by first applying a viscous ultrasound gel on the patient at five distinct physical locations and placing the transducer on these positions at various angles. Each angle captures slightly different perspectives of the heart called views. From each view we can make out cutouts of cardiac structures and see motion of valves and walls. This study introduces an innovative approach to cardiac assessment which delegates the evaluation task to a trained algorithm. This has the potential to expedite the measurement process and help clinicians in the interpretation of the results by proposing common cardiac issues based on pre-trained data. In the paper we provide a process for intelligent extraction of diameters along the left ventricular outflow tract (LVOT) and aortic apparatus using only the ultrasound image. We show that it is possible to successfully train a model which can extract the measurements of the diameter of the LVOT in mid systole, the diameter of the aorta annulus (AA) in mid-systole, the diameter at the aortic sinus of Valsalva (ASV) at end diastole, and the diameter of the Sino tubular junction (SJ) at end diastole. With these measures extracted, the model uses machine learning to automate the diagnostics process which can bring diagnostics closer to patients' homes. © 2024 IEEE.

关键词： Echocardiography

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Privacy preserving large language models: ChatGPT case study based vision and framework

IET Blockchain

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IET Blockchain 2024年第S1期4卷 706-724页

作者： Ullah, Imdad Hassan, Najm Gill, Sukhpal Singh Suleiman, Basem Ahanger, Tariq Ahamed Shah, Zawar Qadir, Junaid Kanhere, Salil S. School of Computer Science Faculty of Engineering The University of Sydney SydneyNSW Australia Higher Colleges of Technology Abu Dhabi United Arab Emirates School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Management Information Systems Department College of Business Administration Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia Department of Information Technology Sydney International School of Technology and Commerce SydneyNSW Australia Department of Computer Science and Engineering College of Engineering Qatar University Doha Qatar Sydney Australia

The generative Artificial Intelligence (AI) tools based on Large Language Models (LLMs) use billions of parameters to extensively analyse large datasets and extract critical information such as context, specific details, identifying information, use this information in the training process, and generate responses for the requested queries. The extracted data also contain sensitive information, seriously threatening user privacy and reluctance to use such tools. This article proposes the conceptual model called PrivChatGPT, a privacy-preserving model for LLMs consisting of two main components, that is, preserving user privacy during the data curation/pre-processing and preserving private context and the private training process for large-scale data. To demonstrate the applicability of PrivChatGPT, it is shown how a private mechanism could be integrated into the existing model for training LLMs to protect user privacy;specifically, differential privacy and private training using Reinforcement Learning (RL) were employed. The privacy level probabilities are associated with the document contents, including the private contextual information, and with metadata, which is used to evaluate the disclosure probability loss for an individual's private information. The privacy loss is measured and the measure of uncertainty or randomness is evaluated using entropy once differential privacy is applied. It recursively evaluates the level of privacy guarantees and the uncertainty of public databases and resources during each update when new information is added for training purposes. To critically evaluate the use of differential privacy for private LLMs, other mechanisms were hypothetically compared such as Blockchain, private information retrieval, randomisation, obfuscation, anonymisation, and the use of Tor for various performance measures such as the model performance and accuracy, computational complexity, privacy vs. utility, training latency, vulnerability to attacks, and

关键词： Blockchain

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Sentiment Analysis On Arabic Companies Reviews 6

Sentiment Analysis On Arabic Companies Reviews

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Fouda, Aya E. Ahmed, Karim Salah Mohamed, Karim Ashraf Noshy, Mayer Mamdouh ElKattan, Youssef Mhran, Amany Ahmed Abdelbaky, Ibrahim Fouad, Khaled M. School of Computer Science Nile University Giza Egypt Artificial Intelligence Department Faculty of Computers and Artificial Intelligence Benha University Banha13518 Egypt Information Systems Faculty of Computers and Artificial Intelligence Benha University Egypt Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt

ISBN: (纸本)9798350373875

This study introduces an innovative approach to sentiment analysis, specifically tailored for the Arabic language, a domain that poses unique challenges due to its complex morphology and diverse dialects. Utilizing a substantial dataset of over 108,000 reviews related to Arabic companies, our primary objective was to develop a robust and reliable sentiment scoring system that caters to the intricacies of the Arabic language, aimed at assisting businesses in understanding customer sentiments more *** methodology encompassed an extensive preprocessing phase, crucial for preparing the dataset for accurate analysis. This phase included converting emojis and emoticons into textual descriptions a contemporary and expressive facet of digital communication, removing Arabic diacritics to standardize text input, and normalizing the text for consistency. The preprocessing was carefully designed to address the specific challenges posed by the Arabic script and dialectal *** the core sentiment analysis, we employed two robust machine learning models: Logistic Regression and Naive Bayes. Each model was meticulously chosen and adapted for its proven effectiveness in text classification, especially in handling the nuances of sentiment analysis within the Arabic text. Our approach not only leverages the strengths of these models but also adapts them to accommodate the linguistic characteristics of *** results of our study provide significant contributions to the field of sentiment analysis in Arabic. By addressing the unique challenges of the language and adapting conventional machine learning techniques accordingly, our research offers valuable insights and tools for businesses and researchers focused on the Arab market. These insights are instrumental for companies seeking to understand and respond to customer sentiments in a linguistically diverse and culturally rich region. © 2024 IEEE.

关键词： Sentiment analysis

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Light field depth estimation:A comprehensive survey from principles to future

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High-Confidence Computing 2024年第1期4卷 92-107页

作者： Tun Wang Hao Sheng Rongshan Chen Da Yang Zhenglong Cui Sizhe Wang Ruixuan Cong Mingyuan Zhao State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Zhongfa Aviation Institute Beihang UniversityHangzhou 311115China Faculty of Applied Sciences Macao Polytechnic UniversityMacao 999078China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Light Field(LF)depth estimation is an important research direction in the area of computer vision and computational photography,which aims to infer the depth information of different objects in threedimensional scenes by capturing LF *** this new era of significance,this article introduces a survey of the key concepts,methods,novel applications,and future trends in this *** summarize the LF depth estimation methods,which are usually based on the interaction of radiance from rays in all directions of the LF data,such as epipolar-plane,multi-view geometry,focal stack,and deep *** analyze the many challenges facing each of these approaches,including complex algorithms,large amounts of computation,and speed *** addition,this survey summarizes most of the currently available methods,conducts some comparative experiments,discusses the results,and investigates the novel directions in LF depth estimation.

关键词： Light field Depth estimation Deep learning Sub-aperture image Epipolar-plane image

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Evaluating the Impact of Pulse Oximetry Bias in Machine Learning Under Counterfactual Thinking 3rd

Evaluating the Impact of Pulse Oximetry Bias in Machine L...

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3rd International Workshop on Applications of Medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

作者： Martins, Inês Matos, João Gonçalves, Tiago Celi, Leo A. Wong, An-Kwok Ian Cardoso, Jaime S. Faculty of Engineering University of Porto Porto Portugal Institute for Systems and Computer Engineering Technology and Science Porto Portugal Duke University Durham United States Massachusetts Institute of Technology Cambridge United States

ISBN: (纸本)9783031820069

Algorithmic bias in healthcare mirrors existing data biases. However, the factors driving unfairness are not always known. Medical devices capture significant amounts of data but are prone to errors;for instance, pulse oximeters overestimate the arterial oxygen saturation of darker-skinned individuals, leading to worse outcomes. The impact of this bias in machine learning (ML) models remains unclear. This study addresses the technical challenges of quantifying the impact of medical device bias in downstream ML. Our experiments compare a "perfect world", without pulse oximetry bias, using SaO2 (blood-gas), to the "actual world", with biased measurements, using SpO2 (pulse oximetry). Under this counterfactual design, two models are trained with identical data, features, and settings, except for the method of measuring oxygen saturation: models using SaO2 are a "control" and models using SpO2 a "treatment". The blood-gas oximetry linked dataset was a suitable test-bed, containing 163,396 nearly-simultaneous SpO2 - SaO2 paired measurements, aligned with a wide array of clinical features and outcomes. We studied three classification tasks: in-hospital mortality, respiratory SOFA score in the next 24 h, and SOFA score increase by two points. Models using SaO2 instead of SpO2 generally showed better performance. Patients with overestimation of O2 by pulse oximetry of ≥ 3% had significant decreases in mortality prediction recall, from 0.63 to 0.59, P © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Oximeters

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Real-Time Deepfake Detection for AI-Generated Arabic Speech 6

Real-Time Deepfake Detection for AI-Generated Arabic Speech

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Awaad, Rewaa Hedya, Abdelrahman Fadel, Mariam Zaky, Ahmed B. Minia University Department of Computer Science and Information Systems Minya61111 Egypt Egypt-Japan University of Science and Technology Department of Computer Science and Information Technology Alexandria21934 Egypt Benha University Faculty of Engineering at Shoubra Benha13511 Egypt

ISBN: (纸本)9798350378511

This study investigates the effectiveness of Retrieval-based Voice Conversion (RVC) in detecting AI-generated Arabic speech across diverse linguistic contexts. The primary research questions address whether the RVC model can accurately differentiate between real and synthetic speech samples in various languages and its generalization capability across different linguistic contexts. Experimental evaluations show that the model achieves accuracies of 92% using Ensemble Learning (XGBoost and LightGBM), 93% with Meta Learning, and 90% with Neural Networks. Assessments encompass scenarios with real Arabic and English speech not included in training datasets, as well as different speakers across languages. The study contributes insights into the robustness and practical application of RVC for speech authentication. © 2024 IEEE.

关键词： Translation (languages)

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