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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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Proposing a digital twin-based sustainable water governance system for rural Indian villages

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1777-1783页

作者： Singh, Dineshkumar Sharma, Vishnu Department of Computer Science and Engineering Faculty of Computer Science & Engineering (FCE) Poornima University Jaipur India Department of Computer Science and Application Faculty of Computer Science & Engineering (FCE) Poornima University Jaipur India

A sustainably governed water-ecosystem at village-level is crucial for the community's well-being. It requires understanding natures’ limits to store and yield water and balance it with the stakeholders’ needs, in the context of population growth, climate change, and unsustainable agricultural practices. Maharashtra villages like Randullabad and Karanjkhop are impacted by limited groundwater availability due to the geological structure of the Deccan Traps basaltic rock formation and are recharged by the most rainfall during the monsoon season. Traditional water management practices are inadequate, with old wells causing conflicts among villagers due to fluctuating water levels. In order to achieve a sustainable water system for rural Indian villages, this paper proposes a Digital Twin-Based Water Governance System (DToWGS) with the aim of balancing nature's boundaries with stakeholders' interests to provide real-time water insights, encourage community engagement, transparency in water allocation and decision-making, and promote sustainable practices. These measured indicators can potentially link local governance outcomes to global goals like Viksit Bharat 2047 and UN Sustainable Development Goal 6 (SDG 6). © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Digital twin Empathy map Remote sensing SDG6 Stakeholders Village water environment

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Robust subspace clustering via two-way manifold representation

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Multimedia Tools and Applications 2025年第16期84卷 16339-16356页

作者： Ezeora, Nnamdi Johnson Anichebe, Gregory Emeka Nzeh, Royransom Chiemela Uzo, Izuchukwu Uchenna Department of Computer Science University of Nigeria Nsukka Nigeria School of Computer Science and Communication Engineering JiangSu University JiangSu 212013 China

Subspace clustering has shown great potential in discovering the hidden low-dimensional subspace structures in high-dimensional data. However, most existing methods still face the problem of noise distortion and overlapping subspaces. To tackle this problem and ensure that each sample is only assigned to a single subspace, a new method is proposed in this paper. Specifically, a two-way learning technique is introduced by inducing data manifold via two representative structures. The first is a low-rank structure learned directly from original data. The second structure is an affinity matrix obtained via a k symmetric nearest neighbor graph. By further introducing a dual regularization term, both structures are allowed to guild themselves adaptively to find robust clustering directly without spectral post-processing. In order to evaluate the effectiveness of the proposed method, several experiments are conducted on multiple benchmark datasets. The experimental results, evaluated using six standard metrics, clearly demonstrate that the proposed method outperforms state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Clustering algorithms

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A transformer based real-time photo captioning framework for visually impaired people with visual attention

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Multimedia Tools and Applications 2024年第41期83卷 88859-88878页

作者： Muhammed Kunju, Abubeker Kiliyanal Baskar, S. Zafar, Sherin A R, Bushara S, Rinesh A, Shafeena Karim Kerala Kanjirapally India Faculty of Engineering Department of Electronics and Communication Engineering Karpagam Academy of Higher Education Coimbatore India Department of Computer and Electronics Engineering SEST- Jamia Hamdard New Delhi India Department of Electronics and Communication Engineering KMEA College of Engineering Kerala Cochin India Department of Computer Science and Engineering V.S.B College of Engineering Technical Campus Coimbatore India

In recent years, transformer-based photo captioning frameworks plays a crucial role in improving individuals’ overall well-being, self-reliance, and inclusivity by giving them access to visual content via written and voiced explanations. This research investigates a two-layer transformer architecture to capture extensive relationships in visual data effectively. Using a two-layer transformer design, visual attention mechanism enables the model to autonomously identify relevant regions within a picture while generating a particular word within the anticipated caption. To get the features from the images, a pre-trained Inception V3 model is used, and the feature maps are extracted from the final convolutional block. The dataset used in this research was Flick8k, Flick30k, among bespoke datasets. The model was trained on a TPU platform and implemented on a Raspberry Pi 4B graphics processing unit. To assess the efficacy of the two-layer transformer model for photo captioning, a series of experiments were undertaken using benchmark datasets and with state-of-the-art models. The quality of the generated captions was evaluated using quantitative criteria such as BLEU, BLEUR, ROUGE and F1 score. Furthermore, a series of qualitative assessments were conducted, employing human evaluators, to measure the overall coherence, and fluency of the captions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： computer vision

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Falcon Optimization Algorithm-Based Energy Efficient communication Protocol for Cluster-Based Vehicular Networks

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computers, Materials & Continua 2024年第3期78卷 4243-4262页

作者： Youseef Alotaibi B.Rajasekar R.Jayalakshmi Surendran Rajendran Department of Software Engineering College of ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Electronics and Communication Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennai602105India

Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)*** networks give a safe and more effective driving experience by presenting time-sensitive and location-aware *** communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with ***,the scheme of an effectual routing protocol for reliable and stable communications is *** research demonstrates that clustering is an intelligent method for effectual routing in a mobile ***,this article presents a Falcon Optimization Algorithm-based Energy Efficient communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in *** FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the *** accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust *** the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR *** experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

关键词： Vehicular networks communication protocol clustering falcon optimization algorithm routing

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An Intrusion Detection System on The Internet of Things Using Deep Learning and Multi-objective Enhanced Gorilla Troops Optimizer

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Journal of Bionic engineering 2024年第5期21卷 2658-2684页

作者： Hossein Asgharzadeh Ali Ghaffari Mohammad Masdari Farhad Soleimanian Gharehchopogh Department of Computer Engineering Urmia BranchIslamic Azad UniversityUrmia63896-57169Iran Department of Computer Engineering Technical and Vocational University(TUV)TehranIran Department of Computer Engineering Tabriz BranchIslamic Azad UniversityTabriz***Iran Department of Computer Engineering Faculty of Engineering and Natural ScienceIstinye University34010IstanbulTurkey Department of Computer Engineering Khazar UniversityAZ1096BakuAzerbaijan

In recent years,developed Intrusion Detection Systems(IDSs)perform a vital function in improving security and anomaly *** effectiveness of deep learning-based methods has been proven in extracting better features and more accurate classification than other *** this paper,a feature extraction with convolutional neural network on Internet of Things(IoT)called FECNNIoT is designed and implemented to better detect anomalies on the ***,a binary multi-objective enhance of the Gorilla troops optimizer called BMEGTO is developed for effective feature ***,the combination of FECNNIoT and BMEGTO and KNN algorithm-based classification technique has led to the presentation of a hybrid method called *** the next step,the proposed model is implemented on two benchmark data sets,NSL-KDD and TON-IoT and tested regarding the accuracy,precision,recall,and Fl-score *** proposed CNN-BMEGTO-KNN model has reached 99.99%and 99.86%accuracy on TON-IoT and NSL-KDD datasets,*** addition,the proposed BMEGTO method can identify about 27%and 25%of the effective features of the NSL-KDD and TON-IoT datasets,respectively.

关键词： Intrusion detection Internet of Things-Convolutional neural network Multi-objective Gorilla troops optimizer

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Secure Data communication in a Clustered Network in Wireless Sensor Networks using AES Cipher 15

Secure Data Communication in a Clustered Network in Wireless...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Manjunatha, A.S. Venkatramana Bhat, P. Department of Computer and Communication Engineering India Department of Computer Science and Engineering India

ISBN: (纸本)9798331300579

Data is collected and forwarded to the cluster head by sensor nodes in the Wireless Sensor Network (WSN). Ensuring the confidentiality and integrity of the data that must be provided to the base station is tough. We recommend using a simple Caesar cipher and Advanced Encryption Standard (AES) technique at the cluster head node level for effective communication from the cluster head to the base station with little power consumption. © Grenze Scientific Society, 2024.

关键词： Sensor nodes

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Predicting Student Behavior Using a Neutrosophic Deep Learning Model

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Neutrosophic Sets and Systems 2025年 76卷 288-310页

作者： Shitaya, Ahmed Mohamed Wahed, Mohamed El Syed Abd El Khalek, Saied Helemy Ismail, Amr Shams, Mahmoud Y. Salama, A.A. Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt Department of Computer Science Faculty of computer and information sciences Suez Canal University Egypt Department. of Communication and Computer Engineering Air Defense University Egypt Department of Cybersecurity College of Engineering and Information Technology Buraydah Private Colleges Buraydah51418 Saudi Arabia Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt

We developed an information system using an object-oriented programming language and a distributed database (DDB) consisting of multiple interconnected databases across a computer network, managed by a distributed database management system (DDBMS) for easy access. An intelligent system was designed to assess the difficulty level of preliminary exams and select top-performing advanced students using a Neutrosophic Deep Learning Model. The dataset was randomly split into training (80%) and testing (20%) sets, and the model, trained with the Adam optimizer at a 0.001 learning rate over 50 epochs, incorporated early stopping based on validation loss. This system, implemented at a traditional Egyptian university, achieved a 95% accuracy in predicting student dropout. Student behavior, influenced by personal, environmental, and academic factors, is often evaluated subjectively, leading to inconsistent results. Traditional machine learning approaches struggle with the inherent uncertainty in behavioral data. To address this, we combined neutrosophic theory—a mathematical framework that accounts for truth, falsity, and indeterminacy—with deep learning, which excels at learning complex data relationships, to predict student outcomes such as dropout rates. Evaluating the model on student data, including attendance and grades, showed superior accuracy, achieving a determination coefficient of 0.95, demonstrating the approach's potential for identifying at-risk students and enabling targeted interventions. © 2025, University of New Mexico. All rights reserved.

关键词： Contrastive Learning

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Rice spikelet’s disease detection using hybrid optimization model and optimized CNN

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Soft Computing 2024年第21期28卷 12787-12806页

作者： Patel, Bharati Department of Computer Science and Engineering Faculty of Science and Technology ICFAI University Raipur India

Rice fields all across the world are affected by spikelet sterility, often known as rice spikelet's disease. It is characterized by the improper development of spikelet’s, which lowers grain output and quality. For optimal management and the avoidance of yield losses, this disease must be discovered early. In this study, a deep learning-based approach utilizing optimization techniques was proposed for accurate segmentation and disease detection in rice crops. The research aimed to address the vulnerability of rice crops to disease attacks from seed germination to mature spikelets. The proposed model consisted of three major phases: pre-processing, segmentation, feature extraction and disease detection. Gaussian filtering was employed to preprocess the raw rice crop images, while a new hybrid Whale Customized Gravitational Optimization Algorithm was utilized for segmentation. Disease detection was performed using a hybrid deep learning model called HybridNet, which combines convolutional neural networks (CNN) with an optimized recurrent neural network (RNN) model. The dataset intended for this proposed project is sourced from the CABI PlantwisePlus Knowledge Bank. The proposed model for rice spikelet disease detection achieved high accuracy with Sensitivity (0.9269), Specificity (0.9756), and Accuracy (0.9634). This indicates that the model effectively identifies and detects diseases in rice spikelets, demonstrating its reliable performance in disease management and crop protection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Data Analytics on Unpredictable Pregnancy Data Records Using Ensemble Neuro-Fuzzy Techniques

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computer Systems science & engineering 2023年第8期46卷 2159-2175页

作者： C.Vairavel N.S.Nithya Faculty of Information and Communication Engineering Anna UniversityChennai600025India Department of Computer Science and Engineering KSR College of EngineeringTiruchengode637215India

The immune system goes through a profound transformation during pregnancy,and certain unexpected maternal complications have been correlated to this *** ability to correctly examine,diagnoses,and predict pregnancy-hastened diseases via the available big data is a delicate problem since the range of information continuously increases and is *** approaches for disease diagnosis/classification have been established with the use of data mining ***,such methods do not provide an appropriate classification/diagnosis ***,single learning approaches are used to create the bulk of these *** issues may be made more accurate by combining predictions from many different *** a result,we used the Ensembling of Neuro-Fuzzy(E-NF)method to perform a high-level classification of medical diseases.E-NF is a layered computational model with self-learning and self-adaptive capabilities to deal with specific problems,such as the handling of imprecise and ambiguous data that may lead to uncertainty concerns that specifically emerge during the classification *** data,Training phase,Ensemble phase,and Testing phase make up the complete procedure for the suggested *** preprocessing includes feature extraction and dimensionality *** such processes,the training phase includes the fuzzification process of medical ***,training of input data was done using four types of NF techniques:Fuzzy Adaptive Learning Control Network(FALCON),Adaptive Network-based Fuzzy Inference System(ANFIS),Self Constructing Neural Fuzzy Inference Network(SONFIN)and/Evolving Fuzzy Neural Network(EFuNN).Later,in the ensemble phase,all the NF methods’predicted outcomes are integrated,and finally,the test results are evaluated in the testing *** outcomes indicate that the method could predict impaired glucose tolerance,preeclampsia,gestational hypertensive abnormalities,bacteriuria,and iron deficien

关键词： Pregnancy disorders ensemble neuro-fuzzy accuracy diagnostics impaired glucose tolerance and preeclampsia gestational hypertension abnormalities bacteriuria iron deficiency anaemia

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