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Impact of machine learning-based imputation techniques on medical datasets- a comparative analysis

Multimedia Tools and Applications 2025年第9期84卷 5905-5925页

作者： Tiwaskar, Shweta Rashid, Mamoon Gokhale, Prasad Department of Computer Engineering Faculty of Science and Technology Vishwakarma University Pune India

In the realm of medical datasets, particularly when considering diabetes, the occurrence of data incompleteness is a prevalent issue. Unveiling valuable patterns through medical data analysis is crucial for early and precise medical predictions. However, the quality of data and the proper handling of missing data hold significant significance. To address this challenge, imputation stands as a robust approach. The main goal of this paper aims to provide a comprehensive investigation into the effects brought about by Machine Learning (ML) based imputation techniques, specifically K Nearest Neighbor Imputation (KNNI), Multiple Imputation by Chained Equations (MICE), and MissForest. Results of all three techniques are compared with the complete dataset for five missing rates (10% to 50%), and evaluated using four categories of evaluation criteria i.e. (1) model performance, (2) imputation error rate (Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Coefficient of Determination (R^2) values), (3) Pearson correlation analysis and, (4) model selection basis (Bayesian information criterion (BIC), Akaike information criterion (AIC), values). Model performance includes accuracy, precision, recall, F1 score, and Matthews Correlation Coefficient (Mcoff) score of four ML classifiers viz. (a) Random Forest (RF), (b) Support vector machine (SVM), (c) AdaBoost, (d) XGBoost (XGB). For all missing rate cases, the MissForest technique is better than the KNNI and MICE in accuracy and Mcoff in 80% of cases, precision in 40% of cases, recall in 60% of cases, F1 score, MAE, RMSE, R^2 in 100% of cases, AIC in 80% of cases, and BIC values in 100% of cases. Also, the correlation analysis confirms that the MissForest imputation preserves association between the variables, like the complete dataset. Overall, our findings suggest that MissForest is a better machine learning-based imputation technique for handling missing data in diabetes research. © The Author(s), under exclusive lice

关键词： Adaptive boosting

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Application of human activity/action recognition: a review

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Multimedia Tools and Applications 2025年 1-30页

作者： sedaghati, Nazanin ardebili, Sondos Ghaffari, Ali Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering Faculty of Engineering and Natural Science Istinye University Istanbul Turkey Department of Computer Science Khazar University Baku Azerbaijan

Human activity recognition is a crucial domain in computer science and artificial intelligence that involves the Detection, Classification, and Prediction of human activities using sensor data such as accelerometers, gyroscopes, etc. This field utilizes time-series signals from sensors present in smartphones and wearable devices to extract human activities. Various types of sensors, including inertial HAR sensors, physiological sensors, location sensors, cameras, and temporal sensors, are employed in diverse environments within this domain. It finds valuable applications in various areas such as smart homes, elderly care, the Internet of Things (IoT), personal care, social sciences, rehabilitation engineering, fitness, and more. With the advancement of computational power, deep learning algorithms have been recognized as effective and efficient methods for detecting and solving well-established HAR issues. In this research, a review of various deep learning algorithms is presented with a focus on distinguishing between two key aspects: activity and action. Action refers to specific, short-term movements and behaviors, while activity refers to a set of related, continuous affairs over time. The reviewed articles are categorized based on the type of algorithms and applications, specifically sensor-based and vision-based. The total number of reviewed articles in this research is 80 sources, categorized into 42 references. By offering a detailed classification of relevant articles, this comprehensive review delves into the analysis and scrutiny of the scientific community in the HAR domain using deep learning algorithms. It serves as a valuable guide for researchers and enthusiasts to gain a better understanding of the advancements and challenges within this field. © The Author(s) 2025.

关键词： Human Action Detection Human Activity Recognition (HAR) Human–computer Interaction (HCI) Sensors Smartphones

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A NOTE ON THE QUALITATIVE BEHAVIOUR OF AN ODE MODEL RELATED TO ENDOTOXIN TOLERANCE

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2024年第4期86卷 127-130页

作者： Olteanu, Mircea Ştefan, Radu Department of Mathematical Methods and Models Faculty of Applied Sciences University "Politehnica" of Bucharest Romania Department of Automatic Control and Systems Engineering Faculty of Automatic Control and Computer Science University "Politehnica" of Bucharest Romania

关键词： Differential equations

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VoteDroid: a new ensemble voting classifier for malware detection based on fine-tuned deep learning models

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Multimedia Tools and Applications 2025年第12期84卷 10923-10944页

作者： Bakır, Halit Faculty of Engineering and Natural Sciences Department of Computer Engineering Sivas University of Science and Technology Sivas Turkey

In this work, VoteDroid a novel fine-tuned deep learning models-based ensemble voting classifier has been proposed for detecting malicious behavior in Android applications. To this end, we proposed adopting the random search optimization algorithm for deciding the structure of the models used as voter classifiers in the ensemble classifier. We specified the potential components that can be used in each model and left the random search algorithm taking a decision about the structure of the model including the number of each component that should be used and its location in the structure. This optimization method has been used to build three different deep learning models namely CNN-ANN, pure CNN, and pure ANN. After selecting the best structure for each DL model, the selected three models have been trained and tested using the constructed image dataset. Afterward, we suggested hybridizing the fine-tuned three deep-learning models to form one ensemble voting classifier with two different working modes namely MMR (Malware Minority Rule) and LMR (Label Majority Rule). To our knowledge, this is the first time that an ensemble classifier has been fine-tuned and hybridized in this way for malware detection. The results showed that the proposed models were promising, where the classification accuracy exceeded 97% in all experiments. © The Author(s) 2024.

关键词： Android malware

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ComMatch: A semi-supervised learning classification algorithm based on model calibration

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Journal of Intelligent and Fuzzy systems 2025年第6期48卷 811-822页

作者： Li, Ye Zhou, Jingkang School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China Department of Control Science and Engineering University of Shanghai for Science and Technology Shanghai China

Semi-supervised learning (SSL) aims to reduce reliance on labeled data. Achieving high performance often requires more complex algorithms, therefore, generic SSL algorithms are less effective when it comes to image classification tasks. In this study, we propose ComMatch, a simpler and more effective algorithm that combines negative learning, dynamic thresholding, and predictive stability discriminations into the consistency regularization approach. The introduction of negative learning is to help facilitate training by selecting negative pseudo-labels during stages when the network has low confidence. And ComMatch filters positive and negative pseudo-labels more accurately as training progresses by dynamic thresholds. Since high confidence does not always mean high accuracy due to network calibration issues, we also introduce network predictive stability, which filters out samples by comparing the standard deviation of the network output with a set threshold, thus largely reducing the influence of noise in the training process. ComMatch significantly outperforms existing algorithms over several datasets, especially when there is less labeled data available. For example, ComMatch achieves 1.82% and 3.6% error rate reduction over FlexMatch and FixMatch on CIFAR-10 with 40 labels respectively. And with 4000 labeled samples, ComMatch achieves 0.54% and 2.65% lower error rates than FixMatch and MixMatch, respectively. © 2025 – IOS Press. All rights reserved.

关键词： Semi-supervised learning

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Dynamic Multi-Graph Spatio-Temporal Graph Traffic Flow Prediction in Bangkok:An Application of a Continuous Convolutional Neural Network

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computer Modeling in engineering & sciences 2025年第1期142卷 579-607页

作者： Pongsakon Promsawat Weerapan Sae-dan Marisa Kaewsuwan Weerawat Sudsutad Aphirak Aphithana Department of Civil Engineering Faculty of EngineeringRamkhamkaeng UniversityBangkok10240Thailand Department of Computer Engineering Faculty of EngineeringRamkhamkaeng UniversityBangkok10240Thailand Department of Statistics Faculty of ScienceRamkhamkaeng UniversityBangkok10240Thailand

The ability to accurately predict urban traffic flows is crucial for optimising city ***,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility *** learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal ***,these models often become overly complex due to the large number of hyper-parameters *** this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction *** comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest *** the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 ***,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer *** Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time *** numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.

关键词： Graph neural networks convolutional neural network deep learning dynamic multi-graph spatio-temporal

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Comprehensive multimodal approach for Parkinson’s disease classification using artificial intelligence: insights and model explainability

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Soft Computing 2025年第3期29卷 1845-1877页

作者： Balaha, Hossam Magdy Hassan, Asmaa El-Sayed Ahmed, Rawan Ayman Balaha, Magdy Hassan Bioengineering Department J.B. Speed School of Engineering University of Louisville LouisvilleKY United States Computer Science and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Mathematics and Engineering Physics Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Obstetrics and Gynecology Faculty of Medicine Tanta University Tanta Egypt

Parkinson’s disease (PD) is a debilitating neurodegenerative disorder affecting millions worldwide. Early detection is vital for effective management, yet remains challenging. In this study, we investigated four distinct datasets for PD detection. Through comprehensive experimentation employing ensemble methods and feature selection, we achieved high classification accuracies across the datasets. For the Oxford Parkinson’s Disease Detection Dataset, an accuracy of 95.67%, precision of 97.59%, recall of 84.5%, specificity of 99.32%, and F1-score of 90.57% were achieved. For the Alzheimer Parkinson Diseases 3 Class Dataset, the "Stacking" approach surpasses individual models, reaching an accuracy of 99.85%, precision of 99.81%, recall of 99.81%, specificity of 99.86%, and F1 of 99.81%. For the NewHandPD dataset, Regarding the Spiral category, The "Base-P32-384" model surpasses others with an accuracy of 97.35%, precision of 96.50%, recall of 98.57%, and F1-score of 97.53%. The collective "Stacking" approach proves highly effective regarding the Circle category, achieving 100% across all performance metrics. Regarding the Meander category, the "Base-P16-224" model achieves an accuracy of 97.35%, precision of 99.26%, recall of 95.71%, specificity of 99.19%, and F1 of 97.45%. The Mobile Device Voice Recordings at King’s College London (MDVR-KCL) dataset contains two datasets. Regarding the "SpontaneousDialogue" dataset, accuracy, BAC, precision, recall, specificity, and F1-score were computed, resulting in values of 94.03%, 92.83%, 90.78%, 100.0%, and 85.67%, respectively. Regarding the "ReadText" dataset, accuracy, BAC, precision, recall, specificity, and F1-score were computed, resulting in values of 91.89%, 90.62%, 87.5%, 100.0%, and 81.25%, respectively. Our findings highlight the efficacy of leveraging diverse data sources and advanced machine learning techniques to enhance PD detection accuracy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germa

关键词： Neurodegenerative diseases

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A learning automata based edge resource allocation approach for IoT-enabled smart cities

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Digital Communications and Networks 2024年第5期10卷 1258-1266页

作者： Sampa Sahoo Kshira Sagar Sahoo Bibhudatta Sahoo Amir H.Gandomi Department of Computer Science and Engineering C.V.Raman Global UniversityBhubaneswarIndia Department of Computer Science and Engineering SRM UniversityAmaravatiIndia Department of Computing Science Umea UniversityUmea90187Sweden Department of Computer Science and Engineering NIT RourkelaIndia Faculty of Engineering and Information Technology University of Technology SydneyAustralia University Research and Innovation Center(EKIK) Obuda University1034 BudapestHungary

The development of the Internet of Things(IoT)technology is leading to a new era of smart applications such as smart transportation,buildings,and smart ***,these applications act as the building blocks of IoT-enabled smart *** high volume and high velocity of data generated by various smart city applications are sent to flexible and efficient cloud computing resources for ***,there is a high computation latency due to the presence of a remote cloud *** computing,which brings the computation close to the data source is introduced to overcome this *** an IoT-enabled smart city environment,one of the main concerns is to consume the least amount of energy while executing tasks that satisfy the delay *** efficient resource allocation at the edge is helpful to address this *** this paper,an energy and delay minimization problem in a smart city environment is formulated as a bi-objective edge resource allocation ***,we presented a three-layer network architecture for IoT-enabled smart ***,we designed a learning automata-based edge resource allocation approach considering the three-layer network architecture to solve the said bi-objective minimization *** Automata(LA)is a reinforcement-based adaptive decision-maker that helps to find the best task and edge resource *** extensive set of simulations is performed to demonstrate the applicability and effectiveness of the LA-based approach in the IoT-enabled smart city environment.

关键词： Edge computing IoT Learning automata Resource allocation Smart city

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Optimizing Image Retrieval in Cloud Servers with TN-AGW: A Secure and Efficient Approach

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Journal of The Institution of Engineers (India): Series B 2025年第2期106卷 459-473页

作者： Ponnuviji, N.P. Nirmala, G. Kokila, M. L. Sworna Priyadharshini, S. Indra Department of Computer Science and Engineering RMK College of Engineering and Technology Puduvoyal Thiruvallur India Department of Computer Science and Engineering RMD Engineering College Chennai India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nādu Kattankulathur Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

The increasing use of cloud-based image storage and retrieval systems has made ensuring security and efficiency crucial. The security enhancement of image retrieval and image archival in cloud computing has received considerable attention in transmitting data and ensuring data confidentiality among cloud servers and users. Various traditional image retrieval techniques regarding security have developed in recent years but they do not apply to large-scale environments. This paper introduces a new approach called Triple network-based adaptive grey wolf (TN-AGW) to address these challenges. The TN-AGW framework combines the adaptability of the Grey Wolf Optimization (GWO) algorithm with the resilience of Triple Network (TN) to enhance image retrieval in cloud servers while maintaining robust security measures. By using adaptive mechanisms, TN-AGW dynamically adjusts its parameters to improve the efficiency of image retrieval processes, reducing latency and utilization of resources. However, the image retrieval process is efficiently performed by a triple network and the parameters employed in the network are optimized by Adaptive Grey Wolf (AGW) optimization. Imputation of missing values, Min–Max normalization, and Z-score standardization processes are used to preprocess the images. The image extraction process is undertaken by a modified convolutional neural network (MCNN) approach. Moreover, input images are taken from datasets such as the Landsat 8 dataset and the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset is employed for image retrieval. Further, the performance such as accuracy, precision, recall, specificity, F1-score, and false alarm rate (FAR) is evaluated, the value of accuracy reaches 98.1%, the precision of 97.2%, recall of 96.1%, and specificity of 917.2% respectively. Also, the convergence speed is enhanced in this TN-AGW approach. Therefore, the proposed TN-AGW approach achieves greater efficiency in image retrieving than other existing

关键词： Extraction

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Hybrid Deep Learning Approach for Automating App Review Classification:Advancing Usability Metrics Classification with an Aspect-Based Sentiment Analysis Framework

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computers, Materials & Continua 2025年第1期82卷 949-976页

作者： Nahed Alsaleh Reem Alnanih Nahed Alowidi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computer Science and EngineeringUniversity of HailHail81451Saudi Arabia

App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their *** the analysis of these reviews is vital for efficient review *** traditional machine learning(ML)models rely on basic word-based feature extraction,deep learning(DL)methods,enhanced with advanced word embeddings,have shown superior *** research introduces a novel aspectbased sentiment analysis(ABSA)framework to classify app reviews based on key non-functional requirements,focusing on usability factors:effectiveness,efficiency,and *** propose a hybrid DL model,combining BERT(Bidirectional Encoder Representations from Transformers)with BiLSTM(Bidirectional Long Short-Term Memory)and CNN(Convolutional Neural Networks)layers,to enhance classification *** analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance,with precision,recall,F1-score,and accuracy of 96%,87%,91%,and 94%,*** contributions of this work include a refined ABSA-based relabeling framework,the development of a highperformance classifier,and the comprehensive relabeling of the Instagram App Reviews *** advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.

关键词： Requirements engineering(RE) app review analysis usabilitymetrics hybrid deep learning BERT-BiLSTM-CNN

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