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Breast Mammogram Analysis and Classification Using Deep Convolution Neural Network

computer systems Science & engineering 2022年第10期43卷 275-289页

作者： V.Ulagamuthalvi G.Kulanthaivel A.Balasundaram Arun Kumar Sivaraman Department of Computer Science and Engineering Sathyabama UniversityChennai600119India Department of Electrical Electronics and Communication EngineeringNITTTRChennai600113India Centre for Cyber Physical Systems School of Computer Science and EngineeringVellore Institute of Technology(VIT)Chennai600127India School of Computer Science and Engineering Vellore Institute of Technology(VIT)Chennai600127India

One of the fast-growing disease affecting women’s health seriously is breast *** is highly essential to identify and detect breast cancer in the earlier *** paper used a novel advanced methodology than machine learning algorithms such as Deep learning algorithms to classify breast cancer *** learning algorithms are fully automatic in learning,extracting,and classifying the features and are highly suitable for any image,from natural to medical *** methods focused on using various conventional and machine learning methods for processing natural and medical *** is inadequate for the image where the coarse structure matters *** of the input images are downscaled,where it is impossible to fetch all the hidden details to reach accuracy in *** deep learning algorithms are high efficiency,fully automatic,have more learning capability using more hidden layers,fetch as much as possible hidden information from the input images,and provide an accurate *** this paper uses AlexNet from a deep convolution neural network for classifying breast cancer in mammogram *** performance of the proposed convolution network structure is evaluated by comparing it with the existing algorithms.

关键词： Medical image processing deep learning convolution neural network breast cancer feature extraction classification

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Intelligent Smart Grid Stability Predictive Model for Cyber-Physical Energy systems

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computer systems Science & engineering 2023年第2期44卷 1219-1231页

作者： Ashit Kumar Dutta Manal Al Faraj Yasser Albagory Mohammad zeid M Alzamil Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

A cyber physical energy system(CPES)involves a combination of pro-cessing,network,and physical *** smart grid plays a vital role in the CPES model where information technology(IT)can be related to the physical *** the same time,the machine learning(ML)modelsﬁnd useful for the smart grids integrated into the CPES for effective decision ***,the smart grids using ML and deep learning(DL)models are anticipated to lessen the requirement of placing many power plants for electricity *** this aspect,this study designs optimal multi-head attention based bidirectional long short term memory(OMHA-MBLSTM)technique for smart grid stability predic-tion in *** proposed OMHA-MBLSTM technique involves three subpro-cesses such as pre-processing,prediction,and hyperparameter *** OMHA-MBLSTM technique employs min-max normalization as a pre-proces-sing ***,the MBLSTM model is applied for the prediction of stability level of the smart grids in *** the same time,the moth swarm algorithm(MHA)is utilized for optimally modifying the hyperparameters involved in the MBLSTM *** ensure the enhanced outcomes of the OMHA-MBLSTM technique,a series of simulations were carried out and the results are inspected under several *** experimental results pointed out the better outcomes of the OMHA-MBLSTM technique over the recent models.

关键词： Stability prediction smart grid cyber physical energy systems deep learning data analytics moth swarm algorithm

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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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Spatial Attention Integrated EfficientNet Architecture for Breast Cancer Classification with Explainable AI

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computers, Materials & Continua 2024年第9期80卷 5029-5045页

作者： Sannasi Chakravarthy Bharanidharan Nagarajan Surbhi Bhatia Khan Vinoth Kumar Venkatesan Mahesh Thyluru Ramakrishna Ahlam AlMusharraf Khursheed Aurungzeb Department of Electronics and Communication Engineering Bannari Amman Institute of TechnologySathyamangalam638402India School of Computer Science Engineering and Information Systems(SCORE) Vellore Institute of TechnologyVellore632014India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India Department of Management College of Business AdministrationPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpura140401India

Breast cancer is a type of cancer responsible for higher mortality rates among *** cruelty of breast cancer always requires a promising approach for its earlier *** light of this,the proposed research leverages the representation ability of pretrained EfficientNet-B0 model and the classification ability of the XGBoost model for the binary classification of breast *** addition,the above transfer learning model is modified in such a way that it will focus more on tumor cells in the input ***,the work proposed an EfficientNet-B0 having a Spatial Attention Layer with XGBoost(ESA-XGBNet)for binary classification of *** this,the work is trained,tested,and validated using original and augmented mammogram images of three public datasets namely CBIS-DDSM,INbreast,and MIAS *** accuracy of 97.585%(CBISDDSM),98.255%(INbreast),and 98.91%(MIAS)is obtained using the proposed ESA-XGBNet architecture as compared with the existing ***,the decision-making of the proposed ESA-XGBNet architecture is visualized and validated using the Attention Guided GradCAM-based Explainable AI technique.

关键词： EfficientNet mammograms breast cancer Explainable AI deep-learning transfer learning

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Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 686-698页

作者： Zhaolin Yuan Xiaorui Li Di Wu Xiaojuan Ban Nai-Qi Wu Hong-Ning Dai Hao Wang the Beijing Advanced Innovation Center for Materials Genome Engineering Institute of Artificial IntelligenceBeijing Key Laboratory of Knowledge Engineering for Materials ScienceSchool of Computer and Communication EngineeringUniversity of Science and Technology Beijing the Department of ICT and Natural Science Norwegian University of Science and Technology IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and Technology the Department of Computing and Decision Sciences Lingnan University the Department of Computer Science Norwegian University of Science and Technology

It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the *** proliferation of industrial sensors and the availability of thickening-system data make this ***,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive *** address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening *** a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental *** results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system *** proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.

关键词： Industrial 24 paste thickener ordinary differential equation(ODE)-net recurrent neural network time series prediction

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Lung Cancer Diagnosis and Classification Using Hybrid Neural Network Techniques

Lung Cancer Diagnosis and Classification Using Hybrid Neural...

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2023 International Conference on Sustainable communication Networks and Application, ICSCNA 2023

作者： Yasmin, M. Andrew Roobert, A. Francis Xavier Engineering College Department of Computer Science and Business Systems Tamil Nadu Tirunelveli India Francis Xavier Engineering College Department of Electronics and Communication Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350313987

Lung cancer remains a formidable global health challenge, with early and accurate diagnosis being a critical determinant of patient outcomes. In response to this pressing need, this research presents a novel approach for lung cancer diagnosis and classification utilizing hybrid neural network techniques. By harnessing the synergistic power of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), we have developed an integrated model capable of effectively processing diverse data sources to enhance diagnostic precision. The first component, CNNs, is employed for feature extraction from medical imaging data, such as X-ray images and computed tomography (CT) scans. These networks excel in capturing intricate patterns and subtle abnormalities in medical images, contributing to the early detection of lung cancer. The second component, RNNs, leverages sequential patient data, including clinical histories, genetic information, and longitudinal records, providing valuable contextual information for comprehensive analysis. The hybrid neural network model facilitates a holistic approach to lung cancer diagnosis and classification, as it unifies the strengths of both data-driven imaging analysis and sequential data interpretation. Experimental evaluations of this approach reveal its potential to achieve superior accuracy, outperforming traditional methods, and thus allowing for timely and precise cancer diagnosis. This advancement holds significant promise in improving the prognosis and management of lung cancer patients, potentially leading to more personalized treatment strategies and ultimately reducing the burden of this devastating disease. As such, this research contributes to the ongoing efforts to enhance lung cancer diagnosis, making strides towards improved patient care and survival rates. © 2023 IEEE.

关键词： computed tomography Scan Images Convolution Neural Networks Lung Cancer Recurrent Neural Networks X-ray Images

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Pancreatic Cancer Diagnosis using Swarm Optimization Combined with Machine Learning Techniques 3

Pancreatic Cancer Diagnosis using Swarm Optimization Combine...

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3rd International Conference on Smart Technologies, communication and Robotics, STCR 2023

作者： Bharanidharan, N. Aggarwal, Pratham Batra, Aryan Shah, Deep Sannasi Chakravarthy, S.R. Rajaguru, Harikumar Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India Bannari Amman Institute of Technology Department of Electronics and Communication Engineering Sathyamangalam India

ISBN: (纸本)9798350370867

When compared to different types of malignant tumors, pancreatic malignancy is the 12th widespread tumor disease among humans globally. Pancreatic cancer can be identified at the earlier stages using microarray gene analysis. The objective of this work is to classify the gene samples as normal and tumoral through the usage of Particle Swarm Optimization technique along with the supervised machine learning classifiers. A variance-based feature selection is employed to select the most appropriate features and the selected features are transformed using particle swarm optimization to improve the classification performance. Four different machine learning supervised algorithms are realized as classification techniques. When these four vanilla classifiers are considered, the random forest algorithm performs relatively well with balanced accuracy score of 87.5%. This score is improved to 94.4% through the usage of the proposed technique. In addition, the proposed technique enhances the prediction of the other two classifiers as well. © 2023 IEEE.

关键词： Gene expression

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Deep Learning for Diabetic Retinopathy (DR) Classifier 11th

Deep Learning for Diabetic Retinopathy (DR) Classifier

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11th International Conference on Robotics, Vision, Signal Processing and Power Applications, RoViSP 2021

作者： Kho, Sin Hui Mashohor, Syamsiah Hanafi, Marsyita Department of Computer and Communication Systems Engineering Faculty of Engineering Universiti Putra Malaysia Selangor Serdang43400 Malaysia

ISBN: (纸本)9789811681288

High prevalence in diabetes contributed to the rising of the complications such as diabetic retinopathy (DR) which cause the patient to suffer from vision loss and this vision loss may become permanent if it is not well manage and treat. Including other medication non-adherence factor, this rising trend has been made more challenging for physician in keep apace with demand using manual method of retina screening to diagnose DR. Therefore, this project aimed to develop a classifier of deep learning for DR in 5 different level of severity which are normal (no DR), mild, moderate, severe and proliferative DR, to speed up the clinical assessment for early DR detection or DR monitoring progression of disease. The classification was applied using Scottish grading with consider three categories for fundus abnormalities which are hemorrhage, lipid exudates and microaneurysms for a training from scratch model and a pre-trained model using Inception V3. Verified and trustable dataset of retina screening or fundus image available online such as *** used to train and test of the system. Besides, the execution of this project was completed via on cloud training using Google Cloud Platform (GCP). The obtained results are measuring the classifier performance tested in term of accuracy, sensitivity, and specificity. Transfer learning using Inception V3 has been shown a better performance compare to own training model with accuracy 81.2% using transfer learning Inception V3 and 74.7% for own training model. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Grading

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Drone Gesture Control using OpenCV and Tello 8

Drone Gesture Control using OpenCV and Tello

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8th Conference on Information communication Technology and Society, ICTAS 2024

作者： Khoza, Nokuthula Owolawi, Pius Malele, Vusumuzi Soshanguve Campus Tshwane University of Technology Faculty of Information and Communication Technology Department of Computer Systems Engineering Pretoria South Africa North-West University Unit for Data Science and Computing School of Computer Science and Information Systems Vanderbijlpark Campus Vanderbijlpark South Africa

ISBN: (纸本)9798350314892

Tracking a person with an onboard camera is a very difficult and perhaps technically impossible if one camera is used. In this regard, real-life projects use a series of cameras to achieve the task. The advent of camera installed drone technology has made it possible for researchers and designers to resolve the tracking issues of a single or multiple stagnant devices. This paper presents a method that makes it possible for a drone to recognize hand gestures as control mechanism and follow the person who control it. Furthermore, the method assists in drone object detection using an integrated depth camera that provides video capturing. The OpenCV and Tello were used in this project. A stabilized depth video is for use with current person trackers like the OpenCV tracker. The estimated Tello pose deduced from vision to stabilize the depth image by warping it to a virtual-static camera. This method allows the Tello to obtain not only the tracked person's position and orientation but also their full body pose, which can then be used, for instance, to recognize hand gestures and alter the Tello's behavior. This work has a potential of contributing or enhancing cutting-edge applications such as hands-free photography and videography. © 2024 IEEE.

关键词： Drones

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Hybrid Optimization for Enhanced Retinal Image Classification Using Neural Networks

Hybrid Optimization for Enhanced Retinal Image Classificatio...

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2024 International Conference on Innovative Computing, Intelligent communication and Smart Electrical systems, ICSES 2024

作者： Anwar Basha, H. Deepak, R. Babu, M. Bhanu, Shaik Thasleem Amutha, B. Madhavan, R. Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamilnadu Chennai India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Karnataka Bengaluru India Rajalakshmi Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Mathematics Tamilnadu Chennai India Jeppiaar Institute of Technology Department of Computer Science and Business Systems Tamilnadu Chennai India

ISBN: (纸本)9798331543617

The present study proposes a hybrid optimization algorithm that involves the integration of Neural Networks (NN), Genetic Algorithms(GA), and Particle Swarm Optimization(PSO) to improve the accuracy and efficiency of retinal image analysis. First, a wide range of features are extracted from retinal images and normalized. Then a NN is constructed with its weights and biases initialized to process these features. The fitness of each feature subset is measured with the help of a network's output, quantified by a Mean Squared Error(MSE)-based fitness function. Receiving fitness values, the GA component picks out the most important subsets of features, which are refined with crossover and mutation operations to retain diversity and improve the search process. In a parallel process, PSO dynamically adjusts the position of particles representing subsets of features through modification of velocity and position vectors under the influences of cognition and society. With the proposed integrated approach, iteratively, an optimal feature subset is generated by minimizing error and maximizing classification performance. Finally, after convergence, this last optimized subset of features is used in the final tuning of the NN to achieve a far-reaching improvement in the accuracy of classification. This method takes advantage of the complementary strengths of NN, GA, and PSO;therefore, it is able to provide an effective solution for the optimization of features within medical image analysis. © 2024 IEEE.

关键词： Ophthalmology

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