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A Modified Deep Residual-Convolutional Neural Network for Accurate Imputation of Missing Data

computers, Materials & Continua 2025年第2期82卷 3419-3441页

作者： Firdaus Firdaus Siti Nurmaini Anggun Islami Annisa Darmawahyuni Ade Iriani Sapitri Muhammad Naufal Rachmatullah Bambang Tutuko Akhiar Wista Arum Muhammad Irfan Karim Yultrien Yultrien Ramadhana Noor Salassa Wandya Intelligent System Research Group Faculty of Computer ScienceUniversitas SriwijayaPalembang30139Indonesia

Handling missing data accurately is critical in clinical research, where data quality directly impacts decision-making and patient outcomes. While deep learning (DL) techniques for data imputation have gained attention, challenges remain, especially when dealing with diverse data types. In this study, we introduce a novel data imputation method based on a modified convolutional neural network, specifically, a Deep Residual-Convolutional Neural Network (DRes-CNN) architecture designed to handle missing values across various datasets. Our approach demonstrates substantial improvements over existing imputation techniques by leveraging residual connections and optimized convolutional layers to capture complex data patterns. We evaluated the model on publicly available datasets, including Medical Information Mart for Intensive Care (MIMIC-III and MIMIC-IV), which contain critical care patient data, and the Beijing Multi-Site Air Quality dataset, which measures environmental air quality. The proposed DRes-CNN method achieved a root mean square error (RMSE) of 0.00006, highlighting its high accuracy and robustness. We also compared with Low Light-Convolutional Neural Network (LL-CNN) and U-Net methods, which had RMSE values of 0.00075 and 0.00073, respectively. This represented an improvement of approximately 92% over LL-CNN and 91% over U-Net. The results showed that this DRes-CNN-based imputation method outperforms current state-of-the-art models. These results established DRes-CNN as a reliable solution for addressing missing data.

关键词： Data imputation missing data deep learning deep residual convolutional neural network

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Development of a machine learning model for predicting abnormalities of commercial airplanes

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Data science and Management 2024年第3期7卷 256-265页

作者： Rossi Passarella Siti Nurmaini Muhammad Naufal Rachmatullah Harumi Veny Fara Nissya Nur Hafidzoh Department of Computer Engineering Faculty of Computer ScienceUniversitas Sriwijaya30662Indonesia Intelligent System Research Group(ISysRG) Faculty of Computer ScienceUniversitas Sriwijaya30662Indonesia Department of Informatics Faculty of Computer ScienceUniversitas Sriwijaya30662Indonesia College of Engineering Studies Universiti Teknologi MARA(UiTM)Shah Alam40450Malaysia

Airplanes are a social necessity for movement of humans,goods,and *** are generally safe modes of transportation;however,incidents and accidents occasionally *** prevent aviation accidents,it is necessary to develop a machine-learning model to detect and predict commercial flights using automatic dependent surveillance–broadcast *** study combined data-quality detection,anomaly detection,and abnormality-classification-model *** research methodology involved the following stages:problem statement,data selection and labeling,prediction-model development,deployment,and *** data labeling process was based on the rules framed by the international civil aviation organization for commercial,jet-engine flights and validated by expert commercial *** results showed that the best prediction model,the quadratic-discriminant-analysis,was 93%accurate,indicating a“good fit”.Moreover,the model’s area-under-the-curve results for abnormal and normal detection were 0.97 and 0.96,respectively,thus confirming its“good fit”.

关键词： Automatic dependent surveillance-broadcast data Commercial airplanes accident Data-labeling Machine learning Prediction model

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Advanced Machine Learning Approaches for Breast Cancer Detection with Neutrosophic Sets

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Neutrosophic Sets and systems 2025年 81卷 273-284页

作者： Elbehiery, Hussam Fathi, Hanaa Eassa, Mohamed Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Mahmoud, Hadeer Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt College of Information Technology Amman Arab University Amman Jordan Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Artificial Intelligence Faculty of Computers and Artificial Intelligence Modern University for Technology & Information Cairo Egypt

Breast Cancer (BC) remains a significant health challenge for women and is one of the leading causes of mortality worldwide. Accurate diagnosis is critical for successful therapy and increased survival rates. Recent advances in medical imaging and computational technologies have enabled more precise methods of detecting and evaluating breast cancer. Accurate analysis and diagnosis utilizing medical imaging have developed as essential research topics, providing important help in clinical decision-making for various illnesses, including breast cancer. Machine learning (ML) can accurately predict breast cancer. But the breast cancer data has vague and uncertainty information. So, the neutrosophic sets (NSs) are used in this study to deal with uncertainty data. We convert the original dataset into neutrosophic data with three components such as truth, indeterminacy, and falsity values. Then we applied four ML models with N-data such as logistic regression, gradient boosting (GB), k-nearest neighbor (KNN), and support vector machines (SVM), to improve diagnostic accuracy. Then we compared the ML models with and without using N-data. The results show the logistic regression has higher accuracy with 98.6% with the N-data and 95.80% without N-data. So, the NSs can improve the accuracy of ML models. © 2025, University of New Mexico. All rights reserved.

关键词： Lung cancer

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Yolact-based Approach for Real-Time Fetal Heart Segmentation

Yolact-based Approach for Real-Time Fetal Heart Segmentation

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2023 International Conference on Data science and Its Applications, ICoDSA 2023

作者： Sapitri, Ade Iriani Nurmaini, Siti Rachmatullah, Muhammad Naufal Darmawahyuni, Annisa Firdaus, Firdaus Islami, Anggun Universitas Sriwijaya Intelligent System Research Group Faculty of Computer Science Palembang Indonesia

ISBN: (纸本)9798350305197

The detection and identification of anomalies in fetal hearts require extensive expertise, clinical experience, and significant time. In this context, deep learning (DL) technology based on instance segmentation has become increasingly popular and effective in accelerating the detection of fetal cardiac anomalies. This research aims to develop a DL model based on instance segmentation using the YOLACT approach to detect fetal cardiac anomalies. Specifically, it focuses on anomalies in the septal wall of the fetal heart using ultrasound (USG) images. Fetal cardiac USG images are used as the dataset to train the DL model. The test results demonstrate that the proposed DL model based on the YOLACT approach is capable of detecting defects in the septal wall of the fetal heart with a mean average precision (mAP) of 93.44% and an intersection over union (IoU) of 82.02%. In this regard, the YOLACT technique enables the identification of abnormalities at the pixel level, providing detailed visualization of the affected areas. This research holds the potential to be a breakthrough in the field of fetal cardiac anomaly detection, as it can reduce dependence on experts and expedite the diagnostic process. By utilizing deep learning technology and the YOLACT approach, it is expected that there will be improvements in accuracy and efficiency in identifying fetal cardiac anomalies, thereby enabling early and better medical interventions for patients. © 2023 IEEE.

关键词： Anomaly detection

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A Deep Learning Approach for Automated Prediction of Cardiac Arrest from Vital Sign Data of Intensive Care Unit Patients

A Deep Learning Approach for Automated Prediction of Cardiac...

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2023 International Conference on Data science and Its Applications, ICoDSA 2023

作者： Islami, Anggun Firdaus, Firdaus Tutuko, Bambang Nurmaini, Siti Darmawahyuni, Annisa Rachmatullah, Muhammad Naufal Sapitri, Ade Iriani Oktaviani, Haqiqi Universitas Sriwijaya Intelligent System Research Group Faculty of Computer Science Palembang Indonesia

ISBN: (纸本)9798350305197

Cardiac arrest, abbreviated as CA, is a leading cause of death worldwide, emphasizing the significance of early detection for prompt treatment and improved patient outcomes. Early detection is critical as it allows for timely intervention that saves lives. However, detecting CA can be challenging due to its non-specific symptoms, and time, which is the essence. By doing so, we can implement preventive measures before such incidents occur. In this study, we propose a prediction CA in a patient method based on employing a deep learning algorithm is convolutional neural networks (CNN) method to learn the underlying patterns in the data and to generate an accurate prediction of CA in vital sign data. To evaluate the performance of our prediction methods, we utilized publicly accessible database known as the Medical Information Mart for Intensive Care III (MIMIC III) v1.4. Our proposed model has been developed to analyze 51,577 ICU stays' worth of data, focusing on eight essential vital sign features: body temperature, heart rate, respiration rate, systolic blood pressure, diastolic blood pressure, mean blood pressure, oxygen saturation, and glucose. Through this evaluation, we demonstrate the effectiveness of our prediction techniques in enhancing the accuracy of predictive models and compared it with another state-of-The-Art prediction method is Bidirectional Long Short-Term Memory (Bi-LSTM). The test results demonstrate that the accuracy of the proposed deep learning model based on CNN is 98%, whereas Bi-LSTM is 82%. Our results demonstrate that the CNN method outperforms other methods in terms of accuracy for predicting CA. © 2023 IEEE.

关键词： Forecasting

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Static Analysis Techniques for Fixing Software Defects in MPI-Based Parallel Programs

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computers, Materials & Continua 2024年第5期79卷 3139-3173页

作者： Norah Abdullah Al-Johany Sanaa Abdullah Sharaf Fathy Elbouraey Eassa Reem Abdulaziz Alnanih Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Software Engineering and Distributed System Research Group King Abdulaziz UniversityJeddah21589Saudi Arabia

The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed ***, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.

关键词： High-performance computing parallel computing software engineering software defect message passing interface deadlock

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Enhanced Federated Learning Framework based on Deep Learning and Neutrosophic Set for Android Malware Classification

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Neutrosophic Sets and systems 2025年 82卷 781-799页

作者： Abdellah, Mohamed Refaat Oudah, Hasan H. Badawy, Ahmed Mohamed Ahmed Mohamed, Abdelfattah Abdelfattah M. Bedier, Shady Ahmed Metwaly, Ahmed A. Eassa, Mohamed Abdelhafeez, Ahmed The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt College of Arts Department of Journalism Ahl Al Bayt University Karbala Iraq Cybersecurity Department Faculty of Computers and Artificial Intelligence Helwan University Egypt Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Applied Science Research Center Applied Science Private University Amman Jordan

Malware detection is one of the critical tasks of cybersecurity, especially considering the growing popularity of mobile devices. The integrity and security of mobile ecosystems rely on the capacity to identify malware quickly and precisely, as security hacks against these devices become increasingly frequent. Due to the requirement for massive volumes of data aggregation, traditional centralized machine learning (ML) techniques for malware detection face challenges with data sharing, computational complexity, and privacy. This research addresses these challenges by proposing a novel model, called "CNN-Fed", which is based on Convolutional Neural Networks (CNN) with Federated Learning (Fed). The main goal of this work is to create a global classifier for Android malware detection that is highly accurate and does not require centralised data aggregation. Using four benchmark datasets—Drebin, Malgenome, Kronodroid, and Tuandromd—the CNN-Fed model is trained across many clients in a federated setting under the suggested architecture. Then we use multi-criteria decision-making (MCDM) methodology to evaluate the number of clients and number of rounds. We use two MCDM methods such as Entropy methodology to compute the criteria weights and the CoCoSo methodology to rank the alternatives. We use the interval-valued neutrosophic sets (IVNSs) to deal with uncertainty and vague information. We use five criteria such as accuracy, F1-score, precision, recall, and FP and six of alternatives refer to the number of clients and number of rounds. The results show the best alternative has a larger number of clients and rounds. © 2025, University of New Mexico. All rights reserved.

关键词： Android malware

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Vehicle Routing Problem with Time Windows using Hybrid Metaheuristic Dragonfly Algorithm and Variable Neighborhood Search: Work on Progress 11

Vehicle Routing Problem with Time Windows using Hybrid Metah...

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11th International Conference on Electrical Engineering, computer science and Informatics, EECSI 2024

作者： Yunita, Yunita Stiawan, Deris Rini, Dian Palupi Sriwijaya University Faculty of Computer Science Indonesia Sriwijaya University Intelligent System Research Group Faculty of Computer Science South Sumatera Palembang Indonesia Sriwijaya University Communication Network and Information Security Research Group Faculty of Computer Science South Sumatera Palembang Indonesia Sriwijaya University Image Processing Dan Pattern Recognition Laboratory Group Faculty of Computer Science South Sumatera Palembang Indonesia

ISBN: (纸本)9798350355314

One of the problems with Smart Transportation is the problem of cost and travel time. This problem is known as the Variable Routing Problem (VRP). In some real cases, in addition to considering route selection, there are limitations on the capacity and time period for the vehicle to serve each customer known as Time Windows, so VRP has developed into Variable Routing Problem with Time Windows (VRPTW). One way to solve VRPTW is to use metaheuristic methods. However, the metaheuristic method has a weakness, which are it can get stuck in local optimums and fail to find better global solutions. To overcome this, a combination with other methods or known as hybrid is needed. The formulation of this research problem is how to develop a Smart transportation model using a metaheuristic hybrid algorithm. The purpose of this study is to develop a Smart transportation model using a metaheuristic hybrid algorithm. The method used is to combine two metaheuristic algorithms, which are the Dragonfly Algorithm (DA) and the Variable Neighborhood Search (VNS). The result of this research is a new algorithm model which is a hybrid between DA and VNS. Through a combination of global exploration using DA and local exploitation using VNS, this algorithm is expected to be able to find a better solution and faster convergence in solving VRPTW problems © 2024 IEEE.

关键词： Travel time

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Deep Learning for Fetal QRS-complex Classification in Noninvasive Fetal Electrocardiogram

Deep Learning for Fetal QRS-complex Classification in Noninv...

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2023 International Conference on Data science and Its Applications, ICoDSA 2023

作者： Darmawahyuni, Annisa Tutuko, Bambang Rachmatullah, Muhammad Naufal Nurmaini, Siti Firdaus, Firdaus Ardiansyah, Muhammad Sapitri, Ade Iriani Islami, Anggun Sriwijaya University Intelligent System Research Group Faculty of Computer Science South Sumatera Palembang Indonesia

ISBN: (纸本)9798350305197

Fetal QRS-complex generated from an abdominal electrocardiogram (ECG) is an effective noninvasive tool for accurately measuring the fetus well-being during pregnancy. Classification of fetal QRS is significant to avoid fetal heart abnormalities, such as asphyxia, bradycardia, and hypoxia. However, accurately classifying the fetal QRS-complex is hard because the characteristic fetal ECG (fECG) has small amount of signal energy and it has undesired signal components, including recording devices, movement artifacts, and fetal brain activity. The sophisticated fECG signal processing for fetal QRS-complex classification has been used in the literature. However, they mostly use human intervention for feature extraction, leading to time-consume and raising the measurement error. Hence, this study investigates deep learning (DL) that performs automated feature extraction tasks excellently. DL has excellence amount of data scales, which is its ability to automatically learn and improve from data without the need for explicit programming. A sequence-to-sequence learning task was generated using a hybrid four convolution layers as a part of convolutional neural networks and bidirectional long short-term memory (ConvBiLSTM). As the results, the performance is outstanding;the accuracy, sensitivity, specificity, precision, and F1-score were 100%. The observed results show the proposed ConvBiLSTM model is worth evaluating their effectiveness for fetal QRS-complex classification. © 2023 IEEE.

关键词： Extraction

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A Novel Drone-Station Matching model in Smart Cities based on Strict Preferences

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无人系统（英文） 2023年第3期11卷 261-271页

作者： Debolina Nath Anjan Bandyopadhyay Ankit Rana Tarek Gaber Aboul Ella Hassanien Department of Computer Science and Engineering Amity University Kolkata KolkataWest BengalIndia Department of Computer Science and Engineering Gitam University BangaloreKarnatakaIndia School of Science Engineeringand Environment University of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailiaEgypt Scientific Research Group in Egypt(SRGE) Egypt Faculty of Computer and Artificial Intelligence Cairo UniversityCairoEgypt Scientific Research Group in Egypt(SRGE) Egypt

There has been a considerable increase in the use of drones,or unmanned aerial vehicles(UAVs),in recent times,for a wide variety of purposes such as security,surveillance,delivery,search and rescue operations,penetration of inaccessible or unsafe areas,*** increasing number of drones working in an area poses a challenge to finding a suitable charging or resting station for each drone after completing its task or when it goes low on its *** classical methodology followed by drones is to return to their pre-assigned charging station every time it requires a *** approach is found to be inefficient as it leads to an unnecessary waste of time as well as power,which could be easily saved if the drone is allotted a nearby charging station that is ***,we propose a drone-allocation model based on a preference matching algorithm where the drones will be allotted the nearest available station to land if the station is *** problem is modeled as three entities:Drones,system controllers and charging *** matching algorithm was then used to design a Drone-Station Matching *** simulation results of our proposed model showed that there would be considerably less power consumption and more time saving over the conventional *** would save its travel time and power and ensure more efficient use of the drone.

关键词： Unmanned Aerial Vehicle(UAV) stable matching resource allocation smart city matching algorithm

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