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A novel deep learning approach (Bi-xBcNet-96) considering green AI to discover breast cancer using mammography images

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Neural Computing and Applications 2024年第21期36卷 12701-12723页

作者： El-Mawla, Nesma Abd Berbar, Mohamed A. El-Fishawy, Nawal A. El-Rashidy, Mohamed A. Communication and Electronics Engineering Department Nile Higher Institute for Engineering and Technology Mansoura Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Shebeen El-Kom Egypt Department of Computer Arab East Colleges Riyadh Saudi Arabia

Clinical decision support systems (CDSSs) can effectively detect illnesses such as breast cancer (BC) using a variety of medical imaging techniques. BC is a key factor contributing to the rise in the death rate among women worldwide. Early detection will lessen its impact, which may motivate patients to have quick surgical therapy. computer-aided diagnosis (CAD) systems are designed to provide radiologists recommendations to assist them in diagnosing BC. However, it is still restricted and limited, the interpretability cost, time consumption, and complexity of architecture are not considered. These limitations limit their use in healthcare devices. Therefore, we thought of presenting a revolutionary deep learning (DL) architecture based on recurrent and convolutional neural networks called Bi-xBcNet-96. In order to decrease carbon emissions while developing the DL model for medical image analysis and meet the objectives of sustainable artificial intelligence, this study seeks to attain high accuracy at the lowest computing cost. It takes into consideration the various characteristics of the pathological variation of BC disease in mammography images to obtain high detection accuracy. It consists of six stages: identifying the region of interest, detecting spatial features, discovering the effective features of the BC pathological types that have infected nearby cells in a concentrated area, identifying the relationships between distantly infected cells in some BC pathological types, weighing the extracted features, and classifying the mammography image. According to experimental findings, Bi-xBcNet-96 beat other comparable works on the benchmark datasets, attaining a classification accuracy of 98.88% in DDSM dataset, 100% in INbreast dataset with 5.08% and 0.3% improvements over the state-of-the-art methods, respectively. Furthermore, a 95.79% reduction in computing complexity was achieved. © The Author(s) 2024.

关键词： computer aided diagnosis

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Cepstral and acoustic ternary pattern based hybrid feature extraction approach for end-to-end bangla speech recognition

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16903-16919页

作者： Dua, Mohit Akanksha Dua, Shelza Department of Computer Engineering National Institute of Technology Kurukshetra India Department of Electronics Communication and Engineering Punjab Engineering College Chandigarh Chandigarh India

In the last three decades, a lot of work has been done for building Automatic Speech Recognition (ASR) systems for well-established languages such as English, Chinese, etc. However, for implementing a Large Vocabulary Continuous Speech Recognition (LVCSR) system for low resource languages, the research work is also growing rapidly in corpus-focused areas. Hence, there is a requirement of benchmarking large corpus in case of low-resource language like Bangla such that prejudice results can be avoided and limitations can be handled. In the proposed work, an openly available large-scale Bangla speech corpus provided by Google has been used. The work in this paper proposes a combination of image inspired features with well explored cepstral features to build front end feature extraction phase. It uses integration of Convolutional Neural Network (CNN) and bi-directional Long-short Term Memory (bi-LSTM) with Connectionist Temporal Classification (CTC) loss function to implement backend acoustic model. The experiments employ static and dynamic features of Mel-frequency Cepstral Coefficients (MFCC), Constant Q Cepstral Coefficients (CQCC), and Gammatone Cepstral Coefficients (GTCC) techniques, one by one, with Acoustic Ternary Patterns (ATP) features. The proposed work investigates the effect of these various hybrid front-end approaches with CNN, bi-LSTM and integration of these two models. The novelty of this paper lies in the fact that fusion of ATP with cepstral features improves performance of the proposed low resource language ASR system, where the proposed combination of ATP-dynamic CQCC features with integrated backend acoustic model shows a relative improvement of 10–15% in Word Error Rate (WER) over all other experimented combinations. Further to exploit the noise robust nature of GTCC features, the ATP-dynamic GTCC features with integrated CNN-bi-LSTM back-end model are evaluated in noisy scenario, also. © 2023, The Author(s), under exclusive licence to Springer-V

关键词： Long short-term memory

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Two dimensional cuckoo search optimization algorithm based despeckling filter for the real ultrasound images

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 921-942页

作者： Gupta, Pradeep K. Lal, Shyam Kiran, Mustafa Servet Husain, Farooq Department of Electronics and Communication Engineering Moradabad Institute of Technology Uttar Pradesh Moradabad244001 India Department of Electronics and Communication Engineering National Institute of Technology Karnataka Surathkal Karnataka Mangaluru575025 India Department of Computer Engineering Faculty of Engineering Konya Technical University Konya Turkey Department of Computer Engineering Faculty of Engineering Selcuk University Konya Turkey

A clinical ultrasound imaging plays a significant role in the proper diagnosis of patients because, it is a cost-effective and non-invasive technique in comparison with other methods. The speckle noise contamination caused by ultrasound images during the acquisition process degrades its visual quality, which makes the diagnosis task difficult for physicians. Hence, to improve their visual quality, despeckling filters are commonly used for processing of such images. However, several disadvantages of existing despeckling filters discourage the use of existing despeckling filters to reduce the effect of speckle noise. In this paper, two dimensional cuckoo search optimization algorithm based despeckling filter is proposed for avoiding limitations of various existing despeckling filters. Proposed despeckling filter is developed by combining fast non-local means filter and 2D finite impulse response (FIR) filter with cuckoo search optimization algorithm. In the proposed despeckling filter, the coefficients of 2D FIR filter are optimized by using the cuckoo search optimization algorithm. The quantitative results comparison between the proposed despeckling filter and other existing despeckling filters are analyzed by evaluating PSNR, MSE, MAE, and SSIM values for different real ultrasound images. Results reveal that the visual quality obtained by the proposed despeckling filter is better than other existing despeckling filters. The numerical results also reveal that the proposed despeckling filter is highly effective for despeckling the clinical ultrasound images. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： FIR filters

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Image based Android Malware Detection System using ResNet50 and VGG16 15

Image based Android Malware Detection System using ResNet50 ...

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

作者： Gundla, Rajeshwari Gengaje, Sachin R. Department of Computer Science & Engineering Walchand Institute of Technology Solapur India Department of Electronics and Computer Engineering Walchand Institute of Technology Solapur India

ISBN: (纸本)9798331300579

Attacks on mobile devices, such as smartphones and tablets, have increased as a result of their rising popularity. One of the most significant risks is mobile malware, which may lead to both security breaches and financial losses. Mobile malware will probably continue to develop and spread since it is used to commit different types of cybercrimes on mobile devices. As the Android operating system has gained popularity, mobile malware targets it especially. Users' security is seriously jeopardised by the quick spread of Android malware apps, which also makes it challenging to manually and statically analyse harmful files. Therefore, it is essential to accurately identify and categorise Android harmful files. There has been some discussion of Convolutional Neural Network (CNN) based approaches in this area, however there is still opportunity for performance enhancement. In this work, using two well-known deep learning models, ResNet-50 and VGG16, we present a transfer learning strategy to effectively detect the Android malware files. By converting malicious and benign APK files into grayscale images, the proposed model is trained on the AndroHealthCheck dataset. On the AndroHealthCheck dataset, our model outperforms state-of-the-art works in terms of accuracy, recall, precision, and F1 measures. © Grenze Scientific Society, 2024.

关键词： Android malware

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Shadow Extraction and Elimination of Moving Vehicles for Tracking Vehicles

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computers, Materials & Continua 2023年第11期77卷 2009-2030页

作者： Kalpesh Jadav Vishal Sorathiya Walid El-Shafai Torki Altameem Moustafa HAly Vipul Vekariya Kawsar Ahmed Francis MBui Department of Electronics&Communication Engineering Faculty of Engineering and TechnologyParul Institute of Engineering and TechnologyParul UniversityVadodaraGujarat391760India Department Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Community CollegeKing Saud UniversityRiyadh11362Saudi Arabia Electronics and Communications Engineering Department College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt Department of Computer Engineering Faculty of Engineering and TechnologyParul Institute of Engineering and TechnologyParul UniversityVadodaraGujarat391760India Department of Electrical and Computer Engineering University of Saskatchewan57 Campus DriveSaskatoonSK S7N5A9Canada

Shadow extraction and elimination is essential for intelligent transportation systems(ITS)in vehicle tracking *** shadow is the source of error for vehicle detection,which causes misclassification of vehicles and a high false alarm rate in the research of vehicle counting,vehicle detection,vehicle tracking,and *** of the existing research is on shadow extraction of moving vehicles in high intensity and on standard datasets,but the process of extracting shadows from moving vehicles in low light of real scenes is *** real scenes of vehicles dataset are generated by self on the Vadodara–Mumbai highway during periods of poor illumination for shadow extraction of moving vehicles to address the above *** paper offers a robust shadow extraction of moving vehicles and its elimination for vehicle *** method is distributed into two phases:In the first phase,we extract foreground regions using a mixture of Gaussian model,and then in the second phase,with the help of the Gamma correction,intensity ratio,negative transformation,and a combination of Gaussian filters,we locate and remove the shadow region from the foreground *** to the outcomes proposed method with outcomes of an existing method,the suggested method achieves an average true negative rate of above 90%,a shadow detection rate SDR(η%),and a shadow discrimination rate SDR(ξ%)of 80%.Hence,the suggested method is more appropriate for moving shadow detection in real scenes.

关键词： Change illuminations ImageJ software intelligent traffic systems mixture of Gaussian model National Institute of Health vehicle tracking

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CropSmart-A Crop Prediction Application Using Machine Learning 12th

CropSmart-A Crop Prediction Application Using Machine Learni...

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Madugula, Vidya Perumandla, Anshu Likhitha, Singari Gupta, Manu Dholvan, Mohan Department of Electronics and Computer Engineering Sreenidhi Institute of Science and Technology Hyderabad India

ISBN: (纸本)9789819601424

India is a country whose primary sources of income are agriculture and farming. Varied soil in the nation enables farmers to grow a wide range of crops all year round. The agricultural industry has been the focus of in-depth investigation in the past several years due to recent technological advances, including machine learning and smart computing. The proposed work aims to develop a web application for crop selection supporting farmers and improving agricultural productivity. Various supervised machine learning classifiers are used for crop prediction based on environmental factors and soil conditions. Performance metrics are included in the evaluation of several machine learning models. The results attained show that the bagging classifier achieved better results for crop prediction with an accuracy of 97%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart agriculture

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Classification of suspected objects and severity assessment of COVID-19 from chest X-ray images using deep transfer learning

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Research on Biomedical engineering 2023年第3期39卷 705-718页

作者： Verma, Karan Kumar, Ashok Swaraj, Aman Sagar, Aditi Department of Computer Science and Engineering National Institute of Technology Delhi Delhi India Department of Electronics and Communication Engineering Government Mahila Engineering College Rajasthan Ajmer India Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

Purpose: Coronavirus disease 2019 (COVID-19) has infected about 418 million people across the globe. So, the analysis of biomedical imaging accompanied with artificial intelligence (AI) approaches has transpired a vital role in diagnosing COVID-19. Until now, numerous classification approaches have been demonstrated for the detection of COVID-19. The assessment of COVID-19 patients according to severity level is not so far investigated. For this motivation, the classification of COVID-19 chest X-ray (CXR) images according to severity of the infection is presented in this work. Methods: Primarily, the 1527 CXR images are pre-processed to reshape images into unique size, denoised, and enhanced images through median filter and histogram equalization (HE) techniques, respectively. Afterward, reshaped, denoised, and enhanced CXR images are augmented using synthetic minority oversampling technique (SMOTE) to achieve the balanced dataset of 1752 CXR images. After augmentation, a pre-trained VGG16 and residual network 50 (Resnet50) deep transfer learning models with random forest (RF) and support vector machine (SVM) classifiers are utilized for feature extraction and classification of 1752 CXR images into diverse class labels such as normal, severe COVID-19, and non-severe COVID-19. Results: Our proposed ResNet50 model with SVM classifier provides the highest accuracy of about 95% for severity assessment and classification of COVID-19 CXR images as compared to other permutations. For the ResNet50 model with SVM classifier model, the average value of precision, recall, and F1-score are 91%, 94%, and 92%, respectively. Conclusion: The multi-class classification deep transfer learning models are presented to determine the severity assessment and classification of COVID-19 by using CXR images. Out of these proposed models, the ResNet50 model with SVM classifier will be highly favorable for doctors to classify patients according to their severity assessment and detection of COV

关键词： COVID-19

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Capacitor voltage unbalance minimization for three-phase five-level diode-clamped inverter using hexagonal hysteresis space vector modulation

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第2期7卷 661-671页

作者： Ramkumar, R. Singh, Manmohan Ramkumar, R. Usha, S. Thentral, T. M. Thamizh Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Chengalpattu India Department of Computer Engineering and Application GLA University Mathura India Department of Electrical and Electronics Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Trichy India Department of Renewable Energy University of Maroua National Advanced School of Engineering of Maroua Maroua Cameroon

This article suggests a method for diminishing the voltage unbalance in a three-phase five-level diode-clamped inverter (DCI) through the use of hexagonal hysteresis space vector modulation (HHSVM). Capacitor voltage balancing leads to enhanced system efficiency, reduced stress on components, enhanced performance, abridged electromagnetic interference, and reduced total harmonic distortion. The proposed modulation technique and its implementation are thoroughly examined in this study, along with modeling and experiment data that show how efficient the method is at lowering the capacitor voltage unbalance in the proposed five-level DCI. Capacitor voltage unbalance is reduced with the use of this HHSVM approach to 0.95%, which is a superior reduction compared to traditional PWM methods. The paper also discusses the advantages of the proposed method over other existing methods, making it a promising solution for practical applications in power electronics systems.

关键词： Diode-clamped inverter (DCI) Capacitor voltage balancing Hexagonal hysteresis space vector modulation (HHSVM) Total harmonic distortion

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An Enhanced Integrated Method for Healthcare Data Classification with Incompleteness

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computers, Materials & Continua 2024年第11期81卷 3125-3145页

作者： Sonia Goel Meena Tushir Jyoti Arora Tripti Sharma Deepali Gupta Ali Nauman Ghulam Muhammad Electrical and Electronics Engineering(EEE)Department Maharaja Surajmal Institute of TechnologyNew-Delhi110058India Information Technology(IT)Department Maharaja Surajmal Institute of TechnologyNew-Delhi110058India Chitkara University Institute of Engineering and Technology Chitkara UniversityRajpuraPunjab140401India Department of Computer Science and Engineering Yeungnam UniversityGyeongsan-si38541Republic of Korea Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11421Saudi Arabia

In numerous real-world healthcare applications,handling incomplete medical data poses significant challenges for missing value imputation and subsequent clustering or classification *** approaches often rely on statistical methods for imputation,which may yield suboptimal results and be computationally *** paper aims to integrate imputation and clustering techniques to enhance the classification of incomplete medical data with improved *** classification methods are ill-suited for incomplete medical *** enhance efficiency without compromising accuracy,this paper introduces a novel approach that combines imputation and clustering for the classification of incomplete ***,the linear interpolation imputation method alongside an iterative Fuzzy c-means clustering method is applied and followed by a classification *** effectiveness of the proposed approach is evaluated using multiple performance metrics,including accuracy,precision,specificity,and *** encouraging results demonstrate that our proposed method surpasses classical approaches across various performance criteria.

关键词： Incomplete data nearest neighbor linear interpolation imputation clustering classification

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Dynamic Inverse Reinforcement Learning for Feedback-driven Reward Estimation in Brain Machine Interface Tasks 46

Dynamic Inverse Reinforcement Learning for Feedback-driven R...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Tan, Jieyuan Wang, Yiwen Hong Kong Univesrsity of Science and Technology Department of Electronics and Computer Engineering Hong Kong Hong Kong Univesrsity of Science and Technology Department of Electronics and Computer Engineering Department of Chemical and Biological Engineering Hong Kong

ISBN: (纸本)9798350371499

Reinforcement learning (RL)-based brain machine interfaces (BMIs) provide a promising solution for paralyzed people. Enhancing the decoding performance of RL-based BMIs relies on the design of effective reward signals. Inverse reinforcement learning (IRL) offers an approach to infer subjects' own evaluation from the observed behavior. However, applying IRL to extract reward information in complex BMI tasks requires consideration of the dynamics of subjects' goal during the control process. This dynamic nature of subjects' evaluation requires the IRL method to be able to estimate a time varying reward function. Previous IRL methods applied in BMI systems only estimated a static reward function. Existing IRL algorithms for dynamic reward estimation employ optimization methods to approximate the reward map for each state at each time, which demands substantial amounts of data to achieve convergence. In this paper, we propose a dynamic IRL method to estimate the feedback-driven reward of subjects during BMI tasks. We utilize a state-observation model to continuously infer the reward value for each state, with sensory feedback serving as the external input to model the transition process of the reward. We evaluate our proposed method on a simulated BMI fetch task, which is a multistep task with a time varying reward function. Our method demonstrates improved reward estimation close to the ground truth value, and it significantly outperforms the existing dynamic IRL method when the map size exceeds 25(p © 2024 IEEE.

关键词： Reinforcement learning

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