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Aspect Level Songs Rating Based Upon Reviews in English

Computers, Materials & Continua 2023年第2期74卷 2589-2605页

作者： Muhammad Aasim Qureshi Muhammad Asif Saira Anwar Umar Shaukat Atta-ur-Rahman Muhammad Adnan Khan Amir Mosavi Department of Computer Science Bahria University Lahore54000Pakistan Lahore Institute of Science and Technology Lahore 54792Pakistan Department of Multidisciplinary Engineering Texas A&M UniversityCollege Station77843USA Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam 31441Saudi Arabia Department of Software Gachon UniversitySeongnam13120Korea John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

With the advancements in internet facilities,people are more inclined towards the use of online *** service providers shelve their items for *** users post their feedbacks,reviews,ratings,*** the use of the *** enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these *** Analysis(SA)is a technique that performs such decision *** research targets the ranking and rating through sentiment analysis of these reviews,on different *** a case study,Songs are opted to design and test the decision *** aspects of songs namely music,lyrics,song,voice and video are *** the reason,reviews of 20 songs are scraped from YouTube,pre-processed and formed a *** machine learning algorithms—Naïve Bayes(NB),Gradient Boost Tree,Logistic Regression LR,K-Nearest Neighbors(KNN)and Artificial Neural Network(ANN)are *** performed the best with 74.99%*** are validated using K-Fold.

关键词： Machine learning natural language processing songs reviews:sentiment analysis songs rating aspect level sentiment analysis reviews analysis text classification music

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A Logical Evaluation of Predicting Immune System Strength by using Enhanced Deep Learning Strategy

A Logical Evaluation of Predicting Immune System Strength by...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Malathi, K. Ramkumar, M. Ruby, E. D. Kanmani Santhi, G.B. Korde, Vivek Aljawawdeh, Hamzeh J. Saveetha University Department of Cse Chennai India Veltech Rangarajan Dr Sagunthala R & D Institute of Science & Technology Department of Ece Chennai India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Faculty of information technology Department of software engineering Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

The immune system plays a crucial role in defending the body against diseases, making it vital to develop models that can accurately predict immune system strength. This study presents an Enhanced Deep Learning Strategy (EDLS) designed to predict immune system strength based on immune-related biomarkers such as White Blood Cell (WBC) count and Immunoglobulins. A dataset consisting of 23,654 records with 12 features was collected from clinical sources. The data underwent comprehensive preprocessing, including noise reduction using a Median Filter, feature selection with Random Forest, and data fusion using Kalman Filters. The EDLS model integrates AutoEncoder for dimensionality reduction and Cascaded Long Short-Term Memory (LSTM) for capturing temporal dependencies. The proposed model outperforms nine traditional and advanced machine learning models, including Logistic Regression, Support Vector Machine (SVM), and Convolutional Neural Network (CNN), with an accuracy of 94.72%. Evaluation metrics such as Precision (92.01%), Recall (93.77%), and F1-Score (92.88%) highlight the model's effectiveness in predicting immune system strength. Feature importance analysis reveals that WBC count and Immunoglobulins are the most influential predictors. This study demonstrates the potential of EDLS for enhancing immune strength prediction, which can have significant implications for personalized healthcare and disease prevention. © 2024 IEEE.

关键词： Median filters

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Ranking Center-based NSGA-II

Ranking Center-based NSGA-II

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Khosrowshahli, Rasa Rahnamayan, Shahryar Ibrahim, Amin Makrehchi, Masoud Lab Canada Ontario Tech University Department of Electrical Computer and Software Engineering OshawaON Canada Brock University Engineering Department St. CatharinesON Canada Ontario Tech University Faculty of Business and Information Technology OshawaON Canada

ISBN: (纸本)9798350337020

Multi-objective optimization is a branch of computation to solve mathematical optimization problems when conflicting multiple objective functions must be simultaneously optimized. Many population-based algorithms, such as NSGA-II are used to find an optimal Pareto set of solutions in a short time. However, previous research works showed the performance of classic NSGA-II is degraded when solving problems with many objectives (M ≥ 5). In this work, we aim to take advantage of center-based sampling scheme to increase the exploration and exploitation capability of NSGA-II algorithm. This sampling strategy has demonstrated promising results on several single-objective evolutionary algorithms such as GA, DE, and PSO. Recently, a novel clustering center-based strategy has been proposed, which motivated us to utilize center-based sampling scheme in NSGA-II to solve multi-objective optimization problems. The outcomes confirm that the proposed clustering center-based NSGA-II is able to effectively solve CEC-2017 multi-objective benchmark problems with 2, 3, 5, 10, and 15 objectives. © 2023 IEEE.

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Towards Trustworthy Aircraft Safety: Explainable AI for Accurate Incident and Accident Predictions 20th

Towards Trustworthy Aircraft Safety: Explainable AI for Accu...

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20th International Conference on the Economics of Grids, Clouds, Systems, and Services, GECON 2024

作者： Amin, Maryam Noor, Umara Fatima, Manahil Rashid, Zahid Altmann, Jörn Department of Software Engineering Faculty of Computing and Information Technology International Islamic University Islamabad44000 Pakistan Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul Korea Republic of College of Engineering Seoul National University Seoul Korea Republic of Integrated Major in Smart City Global Convergence Seoul National University Seoul Korea Republic of

ISBN: (纸本)9783031812255

Despite technological advancements, ensuring aircraft safety remains a challenge, however, Machine learning (ML)-based approaches for predicting future incidents play a crucial role in addressing flight safety. As ML models increase in complexity, their decision-making process becomes less transparent, posing significant challenges to trustworthiness. While simpler models demonstrate lower accuracy, more intricate models such as deep neural networks achieve higher accuracy but sacrifice interpretability. In this study, we enhance trustworthiness in aircraft safety prediction by leveraging a dataset of past accidents and incidents to prevent similar accidents from occurring in the future. To achieve this, we apply Random Forest and Extreme Gradient Boosting models to classify different categories of aircraft incidents. Additionally, we apply two powerful explainable artificial intelligence (XAI) techniques: Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP) to provide insights into both local and global predictions made by the models. Notably, our results reveal high accuracy in these predictions while maintaining trustworthiness. This research contributes to the advancement of XAI and offers valuable insights for safety-critical applications and decision support systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Aircraft accidents

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TF-SASM: Training-free Spatial-aware Sparse Memory for Multi-object Tracking 7

TF-SASM: Training-free Spatial-aware Sparse Memory for Multi...

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7th International Conference on Multimedia Analysis and Pattern Recognition, MAPR 2024

作者： Nguyen-Quang, Thuc Tran, Minh-Triet University of Science VNU-HCM Viet Nam National University Software Engineering Lab John von Neumann Institute Ho Chi Minh City Viet Nam University of Science VNU-HCM Viet Nam National University Faculty of Information Technology Software Engineering Lab John von Neumann Institute Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350368437

Multi-object tracking (MOT) in computer vision remains a significant challenge, requiring precise localization and continuous tracking of multiple objects in video sequences. The emergence of data sets that emphasize robust reidentification, such as DanceTrack, has highlighted the need for effective solutions. While memory-based approaches have shown promise, they often suffer from high computational complexity and memory usage due to storing feature at every single frame. In this paper, we propose a novel memory-based approach that selectively stores critical features based on object motion and overlapping awareness, aiming to enhance efficiency while minimizing redundancy. As a result, our method not only store longer temporal information with limited number of stored features in the memory, but also diversify states of a particular object to enhance the association performance. Our approach significantly improves over MOTRv2 in the DanceTrack test set, demonstrating a gain of 2.0% AssA score and 2.1% in IDF1 score. © 2024 IEEE.

关键词： Object detection

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Path2Vec: A Deep Representation Learning Method for Trajectory Feature Extraction and HYSPLIT Uncertainty Quantification

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Applied Mathematics and Nonlinear Sciences 2024年第1期9卷

作者： Ren, Ke Jin, Chengyao Song, Yuxuan Xu, Yang Zhang, Huijie Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian223003 China School of Information Science and Technology Northeast Normal University Changchun130117 China

Accurate quantification of the uncertainty in HYSPLIT model simulations is crucial for analyzing atmospheric pollution propagation paths and assessing environmental risks. This study introduces Path2Vec, a method based on deep representation learning for extracting trajectory features and measuring uncertainty. The method is capable of mining spatiotemporal-independent trajectory motion patterns in the HYSPLIT model. We first extract spatiotemporal-invariant features of the trajectories using a sliding window technique. Subsequently, we utilize a deep representation learning model that integrates a variational autoencoder (VAE) with long short-Term memory (LSTM) networks to encode high-quality deep representations of the trajectories. By measuring the similarity and performing clustering analysis on the generated trajectory deep representations, we can identify and classify different motion patterns, and quantify the uncertainty of HYSPLIT. Experimental results indicate that the Path2Vec method surpasses traditional similarity measurement techniques, such as Euclidean distance and Edit Distance on Real sequence, in extracting spatiotemporal-independent motion patterns and quantifying uncertainty. This study provides a novel and effective approach for trajectory feature extraction and uncertainty quantification, with wide-ranging applications in fields such as meteorological simulation and air pollution propagation path analysis. © 2024 Ke Ren, Chengyao Jin, Yuxuan Song, Yang Xu and Huijie Zhang, published by Sciendo.

关键词： Risk assessment

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Accurate Prediction of Workloads and Resources with Multi-Head Attention and Hybrid LSTM for Cloud Data Centers

Accurate Prediction of Workloads and Resources with Multi-He...

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作者： Bi, Jing Ma, Haisen Yuan, Haitao Zhang, Jia Beijing University of Technology School of Software Engineering Faculty of Information Technology Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Southern Methodist University Department of Computer Science Lyle School of Engineering DallasTX75205 United States

Currently, cloud computing service providers face big challenges in predicting large-scale workload and resource usage time series. Due to the difficulty in capturing nonlinear features, traditional forecasting methods usually fail to achieve high prediction performance for resource usage and workload sequences. Besides, there is much noise in original time series of resources and workloads. If these time series are not de-noised by smoothing algorithms, the prediction results can fail to meet the providers' requirements. To do so, this work proposes a hybrid prediction model named VAMBiG that integrates Variational mode decomposition, an Adaptive Savitzky-Golay (SG) filter, a Multi-head attention mechanism, Bidirectional and Grid versions of Long and Short Term Memory (LSTM) networks. VAMBiG adopts a signal decomposition method named variational mode decomposition to decompose complex and non-linear original time series into low-frequency intrinsic mode functions. Then, it adopts an adaptive SG filter as a data pre-processing tool to eliminate noise and extreme points in such functions. Afterwards, it adopts bidirectional and grid LSTM networks to capture bidirectional features and dimension ones, respectively. Finally, it adopts a multi-head attention mechanism to explore importance of different data dimensions. VAMBiG aims to predict resource usage and workloads in highly variable traces in clouds. Extensive experimental results demonstrate that it achieves higher-Accuracy prediction than several advanced prediction approaches with datasets from Google and Alibaba cluster traces. © 2016 IEEE.

关键词： Time series analysis

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Vibrating Particles System Algorithm for Solving Classification Problems

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Computer Systems Science & engineering 2022年第12期43卷 1189-1206页

作者： Mohammad Wedyan Omar Elshaweesh Enas Ramadan Ryan Alturki Department of Autonomous Systems Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan Department of Software Engineering Information Technology CollegeAL-Hussein bin Talal UniversityMa’an71111Jordan Department of Computer Science Prince Abdullah Bin Ghazi Faculty of Communication and Information TechnologyAl-Balqa Applied UniversityAl-Salt19117Jordan Department of Information Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21961Saudi Arabia

Big data is a term that refers to a set of data that,due to its largeness or complexity,cannot be stored or processed with one of the usual tools or applications for data management,and it has become a prominent word in recent years for the massive development of *** immediately thereafter,the term“big data mining”emerged,i.e.,mining from big data even as an emerging and interconnected field of *** is an important stage in data mining since it helps people make better decisions in a variety of situations,including scientific endeavors,biomedical research,and industrial *** probabilistic neural network(PNN)is a commonly used and successful method for handling classification and pattern recognition *** this study,the authors proposed to combine the probabilistic neural network(PPN),which is one of the data mining techniques,with the vibrating particles system(VPS),which is one of the metaheuristic algorithms named“VPS-PNN”,to solve classi-fication problems more *** data set is eleven common benchmark medical datasets from the machine-learning library,the suggested method was *** suggested VPS-PNN mechanism outperforms the PNN,biogeography-based optimization,enhanced-water cycle algorithm(E-WCA)and the firefly algorithm(FA)in terms of convergence speed and classification accuracy.

关键词： Vibrating particles system(VPS) probabilistic neural network(PNN) classification problem data mining

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PERSONALIZED TOURISM RECOMMENDATIONS: LEVERAGING USER PREFERENCES AND TRUST NETWORK

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Interdisciplinary Journal of information, Knowledge, and Management 2024年 19卷

作者： Shambour, Qusai Yousef Abualhaj, Mosleh M. Abu-Shareha, Ahmad Adel Kharma, Qasem M. Department of Software Engineering Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan Department of Networks and Cybersecurity Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan Department of Data Science and Artificial Intelligence Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan

Aim/Purpose This study aims to develop a solution for personalized tourism recommendations that addresses information overload, data sparsity, and the cold-start problem. It focuses on enabling tourists to choose the most suitable tourism-related facilities, such as restaurants and hotels, that match their individual needs and preferences. Background The tourism industry is experiencing a significant shift towards digitalization due to the increasing use of online platforms and the abundance of user data. Travelers now heavily rely on online resources to explore destinations and associated options like hotels, restaurants, attractions, transportation, and events. In this dynamic landscape, personalized recommendation systems play a crucial role in enhancing user experience and ensuring customer satisfaction. However, existing recommendation systems encounter major challenges in precisely understanding the complexities of user preferences within the tourism domain. Traditional approaches often rely solely on user ratings, neglecting the complex nature of travel choices. Data sparsity further complicates the issue, as users might have limited interactions with the system or incomplete preference profiles. This sparsity can hinder the effectiveness of these systems, leading to inaccurate or irrelevant recommendations. The cold-start problem presents another challenge, particularly with new users who lack a substantial interaction history within the system, thereby complicating the task of recommending relevant options. These limitations can greatly hinder the performance of recommendation systems and ultimately reduce user satisfaction with the overall experience. Methodology The proposed User-based Multi-Criteria Trust-aware Collaborative Filtering (UMCTCF) approach exploits two key aspects to enhance both the accuracy and coverage of recommendations within tourism recommender systems: multicriteria user preferences and implicit trust networks. Multi-criteria ratings

关键词： Collaborative filtering

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Deep Neural Networks Hydrologic and Hydraulic Modeling in Flood Hazard Analysis

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Water Resources Management 2025年

作者： Hawamdeh, Alaa Tarawneh, Anwar Sharrab, Yousef Al-Fraihat, Dimah School of Science University of Jordan Amman Jordan Faculty of Engineering Al-Zaytoonah University of Jordan Amman Jordan Department of Data Science and Artificial Intelligence Faculty of Information Technology Isra University Amman11622 Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman11622 Jordan

Natural disasters can have devastating effects on the environment and natural resources, making flood inundation mapping and hydraulic modeling essential for forecasting critical characteristics like flood depth and water surface elevation. This study investigated key flood-influencing factors including rainstorm intensity, precipitation depth, soil type, geologic setting, and topographic features, while conducting hydraulic modeling of storm flows for 50- and 100-Year return periods, which estimated water depths in Wadi Al Wala reaching 15 m during 50-Year storms and 25 m during 100-Year storms. To enhance prediction capabilities, a Deep Neural Network (DNN) model was developed using 1980–2018 meteorological data, featuring three hidden layers (10, 20, and 30 neurons) with ReLU activation, trained on 80% of data and validated on 20% using Python’s Keras library. Using relative humidity, rainfall, wind speed, and wet bulb temperature as inputs, the model achieved 92% prediction accuracy based on MSE metrics. The integration of HEC-HMS hydrological modeling, HEC-RAS hydraulic modeling, and DNN analysis produced effective flood hazard predictions that support strategic infrastructure planning and improved disaster management in vulnerable regions. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Deep neural networks

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