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Optimal Deep Belief Network Based Lung Cancer Detection and Survival Rate Prediction

computer Systems Science & engineering 2023年第4期45卷 939-953页

作者： Sindhuja Manickavasagam Poonkuzhali Sugumaran Department of Information Technology Rajalakshmi Engineering CollegeChennai600125TamilnaduIndia Department of Computer Science and Engineering Rajalakshmi Engineering CollegeChennai600125TamilnaduIndia

The combination of machine learning(ML)approaches in healthcare is a massive advantage designed at curing illness of millions of *** efforts are used by researchers for detecting and providing primary phase insights as to cancer *** cancer remained the essential source of disease connected mortality for both men as well as women and their frequency was increasing around the *** disease is the unrestrained progress of irregular cells which begin off in one or both *** previous detection of cancer is not simpler procedure however if it can be detected,it can be curable,also finding the survival rate is a major challenging *** study develops an Ant lion Optimization(ALO)with Deep Belief Network(DBN)for Lung Cancer Detection and Classification with survival rate *** proposed model aims to identify and classify the presence of lung ***,the proposed model undergoes min-max data normalization approach to preprocess the input ***,the ALO algorithm gets executed to choose an optimal subset of *** addition,the DBN model receives the chosen features and performs lung cancer ***,the optimizer is utilized for hyperparameter optimization of the DBN *** order to report the enhanced performance of the proposed model,a wide-ranging experimental analysis is performed and the results reported the supremacy of the proposed model.

关键词： Lung cancer feature selection ant lion optimization classification disease diagnosis metaheuristics

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Optimizing Network Intrusion Detection Systems for IoT: Insights from Ensemble and Neural Network Models 3

Optimizing Network Intrusion Detection Systems for IoT: Insi...

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3rd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Teja, Chintakunta Pranay Karthik, P Murali Reddy, Kalam Santhosh Rout, Saroja Kumar Kumar, Kottu Santosh Thullimilli, Srisaiteja Department of Information Technology Hyderabad India Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331533205

The rapid increase in IoT devices has resulted in a significant rise in network traffic, creating new challenges in maintaining strong cybersecurity. To effectively combat both known and unknown cyber threats, such as DoS attacks, advanced network intrusion detection systems (NIDS) in IoT environments must ensure real-time and thorough detection. This study investigates the effectiveness of various machine learning and deep learning models in identifying DoS attacks using the TON_IoT dataset, specifically focusing on its network traffic component. The employed models included ensemble classifiers like the Extra Trees Classifier and Gradient Boosting Classifier, as well as various neural network architectures, including the Multi-Layer Perceptron (MLP) Classifier, Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), and Artificial Neural Networks (ANN). The experimental findings reveal that the Gradient Boosting Classifier achieved the highest performance with a flawless accuracy of 100%, while both the Extra Trees Classifier and MLP Classifier obtained commendable outcomes with accuracies of 90%. The CNN also performed well, achieving an accuracy of 95% due to its high precision and recall metrics. Conversely, both the DNN and ANN struggled with the complexity of the dataset, resulting in lower accuracies of 68% and 38%, respectively. In summary, the findings indicate that ensemble learning techniques, particularly the Gradient Boosting Classifier, are effective in detecting DoS attacks and contribute to the development of robust NIDS tailored for IoT contexts. Thus, this research adds valuable perspectives on creating efficient intrusion detection systems to address the distinctive security challenges commonly faced by IoT networks. © 2025 IEEE.

关键词： Network intrusion

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Improving Prediction of Chronic Kidney Disease Using KNN Imputed SMOTE Features and TrioNet Model

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computer Modeling in engineering & Sciences 2024年第6期139卷 3513-3534页

作者： Nazik Alturki Abdulaziz Altamimi Muhammad Umer Oumaima Saidani Amal Alshardan Shtwai Alsubai Marwan Omar Imran Ashraf Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department College of Computer Science and Engineering University of Hafr Al-BatinHafarAl-Batin39524Saudi Arabia Department of Computer Science&Information Technology The Islamia University of BahawalpurP.O.Box 63100BahawalpurPakistan Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia Information Technology and Management Illinois Institute of TechnologyChicagoIL 60616-3793USA Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Chronic kidney disease(CKD)is a major health concern today,requiring early and accurate *** learning has emerged as a powerful tool for disease detection,and medical professionals are increasingly using ML classifier algorithms to identify CKD *** study explores the application of advanced machine learning techniques on a CKD dataset obtained from the University of California,UC Irvine Machine Learning *** research introduces TrioNet,an ensemble model combining extreme gradient boosting,random forest,and extra tree classifier,which excels in providing highly accurate predictions for ***,K nearest neighbor(KNN)imputer is utilized to deal withmissing values while synthetic minority oversampling(SMOTE)is used for class-imbalance *** ascertain the efficacy of the proposed model,a comprehensive comparative analysis is conducted with various machine learning *** proposed TrioNet using KNN imputer and SMOTE outperformed other models with 98.97%accuracy for *** in-depth analysis demonstrates the model’s capabilities and underscores its potential as a valuable tool in the diagnosis of CKD.

关键词： Precisionmedicine chronic kidney disease detection SMOTE missing values healthcare KNNimputer ensemble learning

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Exploring image data association: A hybrid mining approach

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Multimedia Tools and Applications 2025年第9期84卷 5725-5740页

作者： Parashar, Nishtha Tiwari, Akhilesh Gupta, Rajendra Kumar Department of Computer Science and Engineering Madhav Institute of Technology & Science M.P. Gwalior India Department of Information Technology Madhav Institute of Technology & Science M.P. Gwalior India

In this paper, a new approach for mining image association rules is presented, which involves the fine-tuned CNN model, as well as the proposed FIAR and OFIAR algorithms. Initially, the image transactional database is generated using feature vectors obtained from the fine-tuned CNN architecture. The proposed FIAR algorithm is used to generate hash-indexed image association rules, which are further optimized using the proposed OFIAR algorithm. This methodology combines the strengths of the CNN model to extract histogram features from images, the FIAR algorithm to efficiently mine frequent image itemsets, and the OFIAR algorithm to optimize image association rules. The proposed methodology can be used to discover hidden relationships among images, leading to new insights in image processing and analysis. Efficient results were obtained with a minimum support of 0.50 and a minimum confidence of 0.50. Experiments were performed on the fruits image dataset consisting of 2618 images from six different classes, and the results show that image mining is feasible and can produce strong optimized image association rules that can be further used for classification purposes. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Association rules

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Advanced IoT and Machine Learning System for Real-Time Crop and Fertilizer Recommendation 14

Advanced IoT and Machine Learning System for Real-Time Crop ...

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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Khemnar, Vijaya R. Bendre, Mininath R. Kolekar, Vikas K. Department of Computer Engineering Pravara Rural Engineering College Loni India Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798331531935

The integration of Internet of Things (IoT) and Machine Learning (ML) technology has the capacity to transform agriculture via the improvement of precision agricultural methods. The proposed research work explores integration of IoT and ML in field of crop and fertilizer recommendation. Proposed system’s primary goal is to develop predictive models for determining the appropriate crop and fertilizer types based on various environmental and soil features. Using ML techniques, this research work aims to recommend the best crop and fertilizer combinations to optimize agricultural yield and sustainability. The collected data of IoT sensors give immediate soil health information, while ML algorithms analyze this data to recommends the most suitable crops and suggests the appropriate fertilizers. This integration promises to enhance agricultural productivity and sustainability through data-oriented decision taking and resource efficient utilization. The proposed system has used four ML models as Random Forest, Gradient Boosting, CatBoost, and XGBoost for predictions by using precision, recall, accuracy and F1 score. The obtained results shows that best accuracy value among the models for recommending crop is 0.9909 and for recommending fertilizer is 0.9886. © 2025 IEEE.

关键词： Precision agriculture

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Differently processed modality and appropriate model selection lead to richer representation of the multimodal input

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International Journal of information technology (Singapore) 2024年第7期16卷 4505-4516页

作者： Panda, Saroj Kumar Diwan, Tausif Kakde, Omprakash G. Department of Computer Science and Engineering Indian Institute of Information Technology Tata Consultancy Services

We aim to effectively solve and improvise the Meta Meme Challenge for the binary classification of hateful memes detection on a multimodal dataset launched by Meta. This problem has its challenges in terms of individual modality processing and its impact on the final classification of hateful memes. We focus on feature-level fusion methodologies in proposing the solutions for hateful memes detection in comparison with the decision-level fusion as feature-level fusion generates richer features’ representation for further processing. Appropriate model selection in multimodal data processing plays an important role in the downstream tasks. Moreover, inherent negativity associated with the visual modality may not be detected completely through the visual processing models, necessitating the differently processed visual data through some other techniques. Specifically, we propose two feature-level fusion-based methodologies for the aforesaid classification problem, employing VisualBERT for the effective representation of textual and visual modality. Additionally, we employ image captioning generating the textual captions from the visual modality of the multimodal input which is further fused with the actual text associated with the input through the Tensor Fusion Networks. Our proposed model considerably outperforms the state of the arts on accuracy and AuROC performance metrics. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Deep learning Hateful memes Multimodality Text tensor fusion VisualBERT

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Enhancing Secure Development in Globally Distributed Software Product Lines: A Machine Learning-Powered Framework for Cyber-Resilient Ecosystems

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computers, Materials & Continua 2024年第6期79卷 5031-5049页

作者： Marya Iqbal Yaser Hafeez Nabil Almashfi Amjad Alsirhani Faeiz Alserhani Sadia Ali Mamoona Humayun Muhammad Jamal University Institute of Information Technology PMAS-Arid Agriculture UniversityRawalpindiPakistan Department of Software Engineering College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Science College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Engineering&Networks College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaAl Jouf72388Saudi Arabia Department of Mathematics Pir Mehr Ali Shah Arid Agriculture UniversityRawalpindiPakistan

Embracing software product lines(SPLs)is pivotal in the dynamic landscape of contemporary software ***,the flexibility and global distribution inherent in modern systems pose significant challenges to managing SPL variability,underscoring the critical importance of robust cybersecurity *** paper advocates for leveraging machine learning(ML)to address variability management issues and fortify the security of *** the context of the broader special issue theme on innovative cybersecurity approaches,our proposed ML-based framework offers an interdisciplinary perspective,blending insights from computing,social sciences,and ***,it employs ML for demand analysis,dynamic feature extraction,and enhanced feature selection in distributed settings,contributing to cyber-resilient *** experiments demonstrate the framework’s superiority,emphasizing its potential to boost productivity and security in *** digital threats evolve,this research catalyzes interdisciplinary collaborations,aligning with the special issue’s goal of breaking down academic barriers to strengthen digital ecosystems against sophisticated attacks while upholding ethics,privacy,and human values.

关键词： Machine Learning variability management cybersecurity digital ecosystems cyber-resilience

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Adaptive isomap feature extractive gradient deep belief network classifier for diabetic retinopathy identification

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Multimedia Tools and Applications 2025年第9期84卷 6349-6370页

作者： Singh, Alka Kumar, Rakesh Gandomi, Amir H. Department of Computer Science and Engineering Madan Mohan Malaviya University of Technology Gorakhpur India Faculty of Engineering & Information Technology University of Technology Sydney SydneyNSW Australia Óbuda University Budapest1034 Hungary

Diabetic retinopathy (DR) is an infection that bases eternal visualization loss in patients with diabetes mellitus. With DR, the glucose level in the blood increases, as well as its viscosity, this results in fluid leakage into surrounding tissues in the retina. In other words, DR represents the pathology of capillaries and venules in the retina with leakage effects, being the main acute retinal disorder caused by diabetes. Many DR detection methods have been previously discussed by different researchers;however, accurate DR detection with a reduced execution time has not been achieved by existing methods. The proposed method, the Shape Adaptive box linear filtering-based Gradient Deep Belief network classifier (SAGDEB) Model, is performed to enhance the accuracy of DR detection. The objective of the SAGDEB Model is to perform an efficient DR identification with a higher accuracy and lower execution time. This model comprises three phases: pre-processing, feature extraction, and classification. The shape adaptive box linear filtering image pre-processing is carried out to reduce the image noise without removing significant parts of image content. Then, an isomap geometric feature extraction is performed to compute features of different natures, like shape, texture, and color, from the pre-processed images. After that, the Adaptive gradient Tversky Deep belief network classifier is to perform classification. The deep belief network is probabilistic and generative graphical model that consists of multiple layers such as one input unit, three hidden units, and one output unit. The extracted image featuresare considered in the input layer and these images are sent to hidden layers. Tversky similarity index is applied in hidden layer 1 to analyze the extracted features with testing features. Regarding the similarity value, the sigmoid activation function is determined in hidden layer 2 so different levels of DR can be identified. Finally, the adaptive gradient method is

关键词： Feature extraction

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Machine Learning Based Diabetes Detection Model for False Negative Reduction

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Biomedical Materials and Devices 2024年第1期2卷 427-443页

作者： Uddin, Md. Ashraf Islam, Md. Manowarul Talukder, Md. Alamin Hossain, Md. Al Amin Akhter, Arnisha Aryal, Sunil Muntaha, Maisha School of Information Technology Deakin University Department of Computer Science and Engineering Jagannath University

Diabetes is a chronic disease characterized by the inability of the pancreas to produce enough insulin or the body’s inability to use insulin efficiently. This disease is becoming increasingly prevalent worldwide and can result in severe complications such as blindness, kidney failure, and stroke. Early detection of diabetes can potentially save millions of lives globally, making it a crucial focus of research. In this study, we propose a machine learning model to aid in predicting diabetes. The model comprises several machine learning methods: Linear Regression (LnR), Logistic Regression (LR), k-nearest neighbor (KNN), Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT). Prior to feeding the pre-processed data into the machine learning model for evaluation, we conducted several pre-processing steps, such as removing null values, standardizing data using normalization, and labeling data using the label encoding process. Imbalanced datasets can adversely affect the accuracy of machine learning algorithms, and we address this problem by balancing the datasets using the Synthetic Minority Oversampling Technique (SMOTE) method. We assessed the model’s performance on two datasets and found that the random forest algorithm produced optimal results, with 97% accuracy on the diabetes dataset 2019 and 80% accuracy on the Pima Indian dataset. However, using a balanced dataset, we can significantly reduce the number of false-negative detections. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC 2023.

关键词： Balance data Features selection Machine learning Pre-processed data Random forest SMOTE

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Smart Techniques for LULC Micro Class Classification Using Landsat8 Imagery

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computers, Materials & Continua 2023年第3期74卷 5545-5557页

作者： Mutiullah Jamil Hafeez ul Rehman SaleemUllah Imran Ashraf Saqib Ubaid Department of Computer Science Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-si38541Korea

Wheat species play important role in the price of products and wheat production *** are several mathematical models used for the estimation of the wheat crop but these models are implemented without considering the wheat species which is an important independent *** task of wheat species identification is challenging both for human experts as well as for computer vision-based *** the use of satellite remote sensing,it is possible to identify and monitor wheat species on a large scale at any stage of the crop life *** this work,nine popular wheat species are identified by using Landsat8 operational land imager(OLI)and thermal infrared sensor(TIRS)*** thousand samples of eachwheat crop species are acquired every fifteen days with a temporal resolution of ten multispectral bands(band two to band eleven).This study employs random forest(RF),artificial neural network,support vector machine,Naive Bayes,and logistic regression for nine types of wheat *** addition,deep neural networks are also *** results indicate that RF shows the best performance of 91%accuracy while DNN obtains a 90.2%*** suggest that remotely sensed data can be used in wheat type estimation and to improve the performance of the mathematical models.

关键词： Mathematical model wheat crop estimation landsat8 remote sensing machine learning random forest deep neural network

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