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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Rallapalli, Sreekanth Kumar Tiwari, Vikas Vidya Sagar, S.D. Kumar Pareek, Piyush Nitte Meenakshi Institute of Technology Department of Master of Computer Applications Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798350313413

When it comes to digitally transforming classic sectors towards Industry 4.0, the Industrial Internet of Things (IIoT) is crucial. The IIoT improves the efficiency and productivity of businesses by connecting sensors, equipment used in industries to the Internet for the purposes of data collecting, data analysis, and automated control. Because of the intricate nature of the IIoT infrastructure, anomaly detection has emerged as a crucial method for guaranteeing the platform's viability. As IIoT is inherently suited to continuously growing networks, graph-level anomaly detection has proven to be an effective method for detecting and predicting abnormalities across a wide range of application domains, including transportation, energy, and the factory. This research work suggests an optimization strategy using a combination of the fuzzy technique for order of favorite by similarity to ideal solution (FTOPSIS). For network anomaly detection, the selected characteristics are fed into a modified convolutional neural network (MConvNet). The approach is put through its paces using NBaIoT-balanced benign and malicious network traffic so that it can pick up on the differences between the two. When compared to state-of-the-art models, the findings show that the suggested model outperforms them thanks to the optimization approach's feature selection. © 2023 IEEE.

关键词： Anomaly detection Fuzzy analytic hierarchy process Fuzzy Logic Technique Industrial Internet of Things modified convolutional neural network

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IGHOA Based modified convolutional neural network for Prediction of Cardiovascular Disease

IGHOA Based Modified Convolutional Neural Network for Predic...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Dileep, M.R. Jaiswal, Riyansh Vishwanatha, C.R. Thatikonda, Ramya Avula, Sashikanth Reddy Nitte Meenakshi Institute of Technology Department of Master of Computer Applications Bengaluru India University of the Cumberlands Department of Information Technology CharlotteNC United States

ISBN: (纸本)9798350313413

Prediction of heart disease is more crucial for reducing mortality rates. Many other conditions can contribute to heart disease, including clogged arteries, heart attacks, chest discomfort, and strokes. Bypass surgery or coronary artery surgery is performed to correct issues with the heart's muscles, valves, or rhythm caused by heart disease. Lack of early diagnosis of heart disease in humans is a leading cause of mortality. Using a pre-processing and classification strategy, accurately forecast cardiac issues. In this study, we utilise the UCI heart disease dataset and a real-time dataset to evaluate deep learning algorithms against conventional approaches. As a first step, we examined the interplay and dependency between a variety of clinical characteristics and coronary heart disease. Finally, we use both the whole set of features and a smaller subset to conduct an in-depth examination of the modified convolutional neural network (MCNN) classification model. In order to enhance the CNN's classification capabilities, an IGHOA model is applied to it. Accuracy, the ROC curve, and the F1-Score were used to classifiers. The classification outcomes demonstrated that pertinent characteristics had a significant effect on organization precision. When compared to replicas trained on a complete feature set, the presentation of classification models trained with a smaller feature set better melodramatically with less training period. © 2023 IEEE.

关键词： Correlation dependence Deep Learning Heart Disease Improved Grasshopper optimization algorithm modified convolutional neural network

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HEL-MCNN: Hybrid extreme learning modified convolutional neural network for allocating suitable donors for patients with minimized waiting time

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期232卷

作者： Gnanasambandhan, Sangeeetha Balasubramanian, Vanathi SRM Valliammai Engn Coll Dept Comp Sci & Engn Chennai Tamil Nadu India

Organ transplantation is the ultimate option to treat terminal illness by transplanting the deceased or damaged organs with healthy organs for improving the patient's life expectancy. The number of organs needed and the organs available for transplantation vary enormously. The tremendous advancements in utilizing big data analytics in the healthcare system make it efficient to explore decision-making information. To make optimal decisions in organ transplantation, this paper proposes a modified convolutional neural network-hybrid extreme learning machine (MCNN-HELM) based prediction model. The proposed MCNN-HELM model utilizes three different real-time datasets as inputs which contain records of liver, heart, and lung transplantation details of the donor and recipient. At first, the missing values and inaccurate data present in real-time datasets are removed via pre-processing. The pre-processed data are then trained using the MCNN-HELM model that efficiently determines the suitable donor for the recipient by minimizing the waiting time of the recipient for the matching organ donor. Moreover, the MCNN-HELM model gives initial preference to patients with high-risk rates to improve their quality of life. The proposed MCNN-HELM model achieves training accuracy of 97.5% with a computational time of 2.2 s, while the precision value of estimated factual outcomes, potential outcomes, and the accuracy of the best donor type are obtained by 16.3582, 16.1401, and 0.6784 which are more efficient than other state-of-the-art methods.

关键词： Organ transplantation Donor Recipient modified convolutional neural network Prairie dog optimization algorithm Extreme learning machine

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modified convolutional neural network WITH GLOBAL AVERAGE POOLING FOR INTELLIGENT FAULT DIAGNOSIS OF INDUSTRIAL GEARBOX

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EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY 2020年第1期22卷 63-72页

作者： Li, Yaxin Wang, Kesheng Univ Elect Sci & Technol China Dept Mech & Elect Engn Chengdu 610059 Peoples R China

Gearboxes are key transmission components and widely used in various industrial applications. Due to the possible operational conditions, such as varying rotational speeds, long period of heavy loads, etc., gearboxes may easily be prone to failure. Condition Monitoring (CM) has been proved to be an effective methodology to improve the safety and reliability of gearboxes. Deep learning approaches, nowadays, further enable the CM with more powerful capability to exploit faulty information from massive data and make intelligently diagnostic decisions. However, for most of conventional deep learning models, such as convolutional neural network (CNN), a large amount of labelled training data is a prerequisite, while to obtain the labelled data is usually a laborious and time-consuming job and sometimes even unattainable. In this paper, to handle the case of only a limited labelled data is available, a modified convolutional neural network (MCNN) is proposed by integrating global average pooling (GAP) to reduce the number of trainable parameters and simplify the architecture of deep learning model. The proposed MCNN improves the traditional CNN's ability in fault diagnosis with limited labelled data. Two experimental gearbox datasets are utilized to demonstrate the effectiveness of the proposed MCNN method. Compared with traditional deep learning approaches, namely LSTM, CNN and its variant methods, the experimental results show that the proposed MCNN with higher discrimination and generalization ability in fault classification and diagnostics under the scenario of limited labelled training samples.

关键词： modified convolutional neural network global average pooling intelligent fault diagnosis industrial Gearbox

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Iris-based Biometric Recognition using modified convolutional neural network 11

Iris-based Biometric Recognition using Modified Convolutiona...

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International Conference on Advanced Technologies for Communications (ATC)

作者： Thuong Le-Tien Hanh Phan-Xuan Phu Nguyen-Duy Loc Le-Ba HCM City Univ Technol Dept EE Engn Hcm City Vietnam

ISBN: (纸本)9781538665428

This paper presents an iris-based biometric identification system based on a combination of modified convolutional neural network (CNN) and Softmax classifier. The system comprises: segmentation using threshold method and Hough transform, horizontal normalization of iris segmentation part and histogram equalization to form normalized images with dimensions 100x100 and 150x150. While other CNN models update the parameters in all layers, we propose a modified CNN model by splitting into two parts: feature extraction and recognition. Each normalized image is input into a CNN model, Resnet50, to extract feature vectors. Later, the extracted feature vectors are added to a Fully-Connected layers for training process and finally using Softmax layer to perform recognition task. The parameters are only updated in the fully connected layer and Softmax layer. It greatly improves computational time but still achieves high performance. The paper also performs a comparison of the identification performance of the system with different CNN models to choose the best result for iris recognition task. In addition, comparisons based on the overall accuracy performance is also conducted between the proposed system with the previous works to overview on the efficient of our approach.

关键词： Iris-based biometric recognition modified convolutional neural network Hough transform Resnet50 Softmax Classifier

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IoT sensor-influenced intelligent waste management system using deep learning and multi-objective shortest pathrouting

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INTELLIGENT BUILDINGS INTERNATIONAL 2025年

作者： Kona, V. V. Satyanarayana Subramoniam, M. Binson, V. A. Sathyabama Inst Sci & Technol Fac Elect & Elect Engn Chennai 600119 India Saintgits Coll Engn Dept Elect & Commun Engn Kottayam India

This paper plans to implement an IoT-enabled waste management system employing a deep learning approach. The images are collected with different Internet of Things (IoT)-based sensor devices. The collected images are given into the convolutional neural network (CNN) variants like ResNet, Visual Geometry Group 19 (VGG-19), and Inception approach to obtain the features from the pooling layer of three CNN variants. Then, the optimal feature selection takes place with the hybrid optimization algorithm named Sea Lion Electric Fish Optimization (SLnEFO) to ensure the optimal features. These obtained optimal features are used in the fully connected layer of modified CNN to classify the types of wastes. It helps to determine the weights of classified wastes, which are considered in the developed multi-objective shortest path routing algorithm. The simulation analysis is carried out for the effective performance of the developed waste management system with various quantitative measures.

关键词： IoT sensors waste management system optimal feature selection sea lion electric fish optimization modified convolutional neural network

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Deep Learning based Improved Generative Adversarial network for Addressing Class Imbalance Classification Problem in Breast Cancer Dataset

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INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS 2023年第3期31卷 387-412页

作者： Subasree, S. Sakthivel, N. K. Shobana, M. Tyagi, Amit Kumar Nehru Inst Engn & Technol Dept Comp Sci & Engn Coimbatore 641103 Tamil Nadu India SRM Inst Sci & Technol Dept Networking & Commun Chennai Tamil Nadu India Natl Inst Fash Technol Dept Fash Technol New Delhi Delhi India

The breast cancer diagnosis is one of the challenging tasks of medical field. Especially, the breast cancer diagnosis among younger women (under 40 years old) is more complicated, because their breast tissue is generally denser than the older women. The Breast Cancer Wisconsin image dataset contains two classes: (i) Benign (Minority class), (ii) Malignant (Majority class). The imbalanced class distribution leads to a deterioration in the classifier model performance owing to the biased classification towards the majority class. Therefore, in this article, an improved generative adversarial network (I-GAN) is proposed to overcome the class imbalance problem. Here, the proposed method is the consolidation of deep convolutional generative adversarial network (DCIGAN) and modified convolutional neural network, (MCNN), therefore it is known as DCIGAN-MCNN method. First, the DCIGAN is utilized for balancing the dataset by generating more samples in the training dataset. Then, this training dataset based the classification of Breast cancer is developed using the modified convolutional neural network. The proposed method is executed in MATLAB. The performance analysis are carried out in Breast Cancer Wisconsin (Prognostic) Data Set provides Maximum Geometry Mean (MGM) as 24.058%, 9.582%, Matthews's correlation coefficient (MCC) as 78.623%, 30.357% higher than the existing methods, like CI-BC-RK-SVM, CI-BC-GA, CI-BC-DC-CNN, CI-BC-RF and CI-BC-BMIC-Net respectively. Finally, the simulation results prove that the proposed method can be able to find the optimal solutions efficiently and accurately.

关键词： Improved generative adversarial network modified convolutional neural network Maximum Geometry Mean Matthews's correlation coefficient

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An agile autonomous car driving assistance using hybrid optimization-based kernel support vector convolutional network

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartC期237卷

作者： Jeyalakshmi, Shunmugiah Ravikumar, Sethuraman Lakshmi, Ramachandran Vivekanandan, Govindarajan SRM Valliammai Engn Coll Dept Artificial Intelligence & Data Sci Chennai India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram 522502 Andhra Pradesh India Sri Sairam Inst Technol Dept Comp Sci & Engn Chennai India

In robotic control systems, autonomous car driving is considered a complicated task. The conventional modular techniques necessitate precise localization, planning, and mapping procedures to provide safe driving. These requirements make autonomous car driving tasks computationally ineffective and sensitive to ecological changes. In recent times, the emergence of deep learning-based approaches in autonomous car driving systems provided promising solutions for complex vision task interpretation, localization, and environmental perception. But, they create distribution mismatch issues and require huge perilous interaction information to offer safe driving with the prediction of future collisions. Therefore, to overcome this issue, a novel modified Kernel Support vector convolutional based Hybrid Grasshopper Harris hawk (MKSC-HGH) approach is proposed. The proposed MKSC-HGH approach efficiently forecasts the uncertainties of agile autonomous car driving and guides proper navigation details of the track to avoid the possibility of collision with on-road obstacles and fences. This system inputs camera images captured through sensors, car speed, and manual driving data to analyze and predict safety control and probable collision. The prominent feature representing the ability of modified convolutional neural network (MCNN) and complex boundary encoding process of Kernel Support Vector Machine (KSVM) with hyperparameter tuning operation of modified Grasshopper Optimization Algorithm based Improved Harris Hawk Optimization (MGOA-IHHO) algorithm made proposed system applicable to predict future states (ie. visual prediction, speed prediction, and collision prediction) more accurately. The efficiency of the proposed MKSC-HGH approach is investigated using evaluation measures namely completion time, average speed, top speed, and laps time. The simulation outcome shows the superiority of the proposed MKSC-HGH approach in predicting agile autonomous car driving over state of art

关键词： Autonomous car driving Collision Speed Prediction modified convolutional neural network Kernel support vector machine modified grasshopper optimization algorithm based improved Harris hawk optimization algorithm

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Research on diagnosis algorithm of mechanical equipment brake friction fault based on MCNN-SVM

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MEASUREMENT 2021年 186卷 110065-110065页

作者： Zhang, Xunjie Zhang, Min Xiang, Zaiyu Mo, Jiliang Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Peoples R China Maintenance Key Lab Sichuan Prov Technol & Equipment Rail Transit Operat Chengdu 610031 Peoples R China

Brakes in mechanical equipment are crucial for operational safety, and their effects are directly affected by friction performance. The fault signal induced by friction interface presents the phenomenon of multi-source, and the fault samples are difficult to obtain in practical engineering. Both aspects yield unsatisfactory recognition performance of diagnosis models. To address the issues, in this article, we proposed an algorithm based on a modified convolutional neural network (CNN) and support vector machine (SVM). First, dynamic features were extracted from the friction factor and friction surface temperature as samples, which could effectively present the state of brake friction. Next, CNN was used to learn feature knowledge from dynamic feature set, the Mish activation function, batch normalisation and dropout were employed to complete the training of modified CNN (MCNN). Then, the dynamic feature set was input into the trained MCNN again to learn the feature representations of friction state. Finally, the feature representations were migrated to SVM to establish the mapping between feature space and label space, and the final fault recognition was completed. The proposed algorithm fully combined the powerful feature learning ability of MCNN and the excellent classification performance of SVM on small samples. Experiment results showed that MCNN-SVM had faster convergence speed, and the accuracy of the proposed algorithm reached 100%. Its diagnosis effect was better than counterpart algorithms.

关键词： Mechanical equipment Brake friction modified convolutional neural network Support vector machine Fault diagnosis

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Empowering immediate healthcare insights: a deep learning chatbot with modified-CNN and SA-MGO optimisation

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INTERNATIONAL JOURNAL OF EMBEDDED SYSTEMS 2024年第3-4期17卷 267-284页

作者： Rathee, Sonia Mehta, Shalu Yadav, Amita Dhand, Geetika Mohanty, Sachi Nandan Garanayak, Mamata Paikaray, Bijay Kumar Maharaja Surajmal Inst Technol Dept Comp Sci & Engn New Delhi India VIT AP Univ Sch Comp Sci & Engn Amaravati Andhra Pradesh India Kalinga Inst Social Sci Deemed Univ Dept Comp Sci Bhubaneswar Odisha India Siksha O Anusandhan Deemed Univ Ctr Data Sci Dept Comp Sci & Engn Bhubaneswar Odisha India

This article proposes a novel deep-learning framework designed to empower a highly efficient chatbot, capable of delivering immediate responses to patients seeking information about their medical conditions before their doctor's appointment. The core of the proposed model features a modified-CNN, which incorporates enhancements through the integration of a Gaussian kernel (GK), generalised divisive normalisation (GDN) layer, and the fast Fourier transformer (FFT) for improved feature learning performance. The dataset pre-processed is fed into the modified CNN for effective information extraction. To enhance the optimisation process, the self-adaptive mountain gazelle optimiser (SA-MGO) algorithm is employed, contributing to the overall efficiency of the modified CNN by fine-tuning the parameters of the M-CNN like learning rate, epoch, momentum, and batch-size. The integration of the SA-MGO algorithm further enhances the overall performance of the modified CNN, making the chatbot a valuable resource for individuals seeking immediate insights into their health concerns.

关键词： chatbot modified convolutional neural network generalised divisive normalisation layer fast Fourier transformer FFT self-adaptive mountain gazelle optimiser

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