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Applying Customized Convolutional Neural Network to Kidney Image Volumes for Kidney Disease Detection

computer Systems science & engineering 2023年第11期47卷 2119-2134页

作者： Ali Altalbe Abdul Rehman Javed Department of Computer Science Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon

Kidney infection is a severe medical issue affecting individuals worldwide and increasing mortality *** Kidney Disease(CKD)is treatable during its initial phases but can become irreversible and cause renal *** the various diseases,the most prevalent kidney conditions affecting kidney function are cyst growth,kidney tumors,and *** significant challenge for the medical community is the immediate diagnosis and treatment of kidney *** failure could result from kidney disorders like tumors,stones,and cysts if not often identified and ***-assisted diagnostics are necessary to support clinicians’and specialists’medical assessments due to the rising prevalence of chronic renal illness,the lack of experts,and the rising rates of assessment and monitoring,mainly in developing *** Intelligence(AI)approaches such as machine,and deep learning has been used in literature for kidney disease detection;however,they still lack *** paper implements a deep learning-based Convolutional Neural Network(CNN)model for the classification and prognosis of kidney *** use a benchmark Computed Tomography(CT)kidney dataset for *** data is pre-processed,and then CNN extracts the features from the *** reveal that the proposed approach accurately classifies kidney disease with a considerable accuracy of 0.992%,0.994%precision,0.982%recall,and 0.987%*** study suggests using the proposed fine-tuned CNN model for kidney disease detection.

关键词： Kidney disease convolutional neural network computed tomography feature extraction deep learning machine learning

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Multi-Layered Deep Learning Features Fusion for Human Action Recognition

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computers, Materials & Continua 2021年第12期69卷 4061-4075页

作者： Sadia Kiran Muhammad Attique Khan Muhammad Younus Javed Majed Alhaisoni Usman Tariq Yunyoung Nam Robertas Damaševicius Muhammad Sharif Department of Computer Science HITEC University TaxilaTaxilaPakistan College of Computer Science and Engineering University of Ha’ilHa’ilSaudi Arabia College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Faculty of Applied Mathematics Silesian University of TechnologyGliwicePoland Department of Computer Science COMSATS University IslamabadWah CampusPakistan

Human Action Recognition(HAR)is an active research topic in machine learning for the last few *** surveillance,robotics,and pedestrian detection are the main applications for action *** vision researchers have introduced many HAR techniques,but they still face challenges such as redundant features and the cost of *** this article,we proposed a new method for the use of deep learning for *** the proposed method,video frames are initially pre-processed using a global contrast approach and later used to train a deep learning model using domain transfer *** Resnet-50 Pre-Trained Model is used as a deep learning model in this *** are extracted from two layers:Global Average Pool(GAP)and Fully Connected(FC).The features of both layers are fused by the Canonical Correlation Analysis(CCA).Then features are selected using the Shanon Entropy-based threshold *** selected features are finally passed to multiple classifiers for final *** are conducted on five publicly available datasets as IXMAS,UCF Sports,YouTube,UT-Interaction,and *** accuracy of these data sets was 89.6%,99.7%,100%,96.7%and 96.6%,*** with existing techniques has shown that the proposed method provides improved accuracy for ***,the proposed method is computationally fast based on the time of execution.

关键词： Action recognition transfer learning features fusion features selection classification

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An analytical and comparative study of hospital re-admissions in digital health care

Lecture Notes on Data Engineering and Communications Technol...

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Lecture Notes on Data engineering and Communications Technologies 2021年 66卷 717-733页

作者： Urooj, Aksa Nafis, Md Tabrez Ahmad, Mobin Department of Computer Science and Engineering Jamia Hamdard New Delhi India Faculty of Science Jazan University Jazan Saudi Arabia

Medical re-admissions are expensive and indicate poor quality of hospitals. A remarkable re-admission rate has a huge financial impact on the patients and the hospital. Through the increasing use of medical health records, enormous data is available to us for review which can identify high-risk patients effectively and decrease the mortality rate. According to the 2010 Affordable Care Act, hospitals may be penalized for re-admitting patients within 30 days of discharge. However, hospitals claim that the root cause of re-admissions lies similar to the populations served. In this paper, various re-admission prediction techniques that have been used earlier to estimate the re-admission rate of patients after being discharged from the hospital have been discussed. This article will also give a summary of various socioeconomic and demographic factors that play a vital role in medical re-admissions. Moreover, it will cover some of the measures that can be taken to reduce hospital re-admissions. © The Author(s),.

关键词： Hospitals

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New Speed Sensorless ANN-IFOC Flux Hybrid Estimator Controller for Induction Motor 3

New Speed Sensorless ANN-IFOC Flux Hybrid Estimator Controll...

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3rd IEEE Industrial Electronics and Applications Conference, IEACon 2022

作者： Sepeeh, Muhamad Syazmie Zulkifli, Shamsul Aizam Sim, Sy Yi Chiu, Huang-Jen Jumaat, Siti Amely Universiti Tun Hussein Onn Malaysia Faculty of Electrical and Electronics Engineering Department of Electrical Power Engineering Johor Batu Pahat Malaysia Faculty of Engineering Technology Department of Electrical Engineering Technology Johor Pagoh Malaysia National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipe Taiwan

ISBN: (数字)9781665480062

ISBN: (纸本)9781665480062

This paper is to suggest a new version of sensorless control for induction motor using flux estimator generation to represent the speed input for speed control. The induction motor that been used is been presented as small EV motor in order to replicate a motor EV. This flux-speed hybrid controller is used with ANN-IFOC to replace the actual speed sensor in order to increase the tracking speed and trajectory effect while reduce the cost operation based on flux estimation speed equation. The flux parameter is been selected because any changes on the load the flux will automatically change simultaneously that is good to be applied to the EV motor. The speed controller is still needed in after the speed signal has been generate at ANN-IFOC for flux parameter but without speed sensor. At the end, all the results show, this new sensorless improved version has better time response to track the speed reference even though the load is changes. © 2022 IEEE.

关键词： Speed control

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Synergistic effects of nano-enhanced waste transformer oil and hydrogen premixing on CI engine performance and emissions

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International Journal of Hydrogen Energy 2024年 56卷 484-497页

作者： Sathish, T. Karthikeyan, Sivakumar Sathyamurthy, Ravishankar Rajaram, K. Sandeep Kumar, S. Suresh Kumar, P. Giri, Jayant Amesho, Kassian T.T. Department of Mechanical Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai India Department of Mechanical Engineering SRM Valliammai Engineering College Tamil Nadu Chennai603203 India Department of Mechanical Engineering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore641407 India Department of Mechanical Engineering University Centre for Research and Development Chandigarh University Punjab Mohali India Department of Physics Gandhigram Rural Institute Deemed to be University Tamil Nadu Dindigul624302 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh 522502 India Department of Mechanical Engineering R. V. R. and J.C. College of Engineering Andhra Pradesh Guntur522019 India Institute of Environmental Engineering National Sun Yat-Sen University Kaohsiung804 Taiwan Center for Emerging Contaminants Research National Sun Yat-Sen University Kaohsiung804 Taiwan Tshwane School for Business and Society Faculty of Management of Sciences Tshwane University of Technology Pretoria South Africa The International University of Management Centre for Environmental Studies Main Campus Dorado Park Ext 1 Windhoek Namibia Regent Business School Durban4001 South Africa Destinies Biomass Energy and Farming Pty Ltd SwakopmundP.O. Box 7387 Namibia Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Maharashtra Nagpur India

Waste materials possess untapped potential when harnessed effectively. One such resource is waste transformer oil, derived from electrical transformers, which can be converted into a valuable fuel source for compression-ignition (CI) engines using pyrolysis techniques. In this study, we explored the utilization of a 50 % blend of waste transformer oil with diesel, augmented with Titanium dioxide nanoparticles (referred to as "nano fuel of waste transformer oil"), in an unmodified CI engine. To further enhance the performance of this nano fuel, we introduced hydrogen (at flow rates of 2.5 LPM, 5.0 LPM, and 7.5 LPM) into the inlet section during the suction phase, promoting premixing within the cylinder. Hydrogen premixing significantly improved combustion dynamics. Notably, when 7.5 LPM of hydrogen was introduced alongside waste transformer oil-derived nano fuel, a remarkable 37.30 % increase in brake thermal efficiency was achieved. Moreover, this configuration exhibited a 15.33 % reduction in brake-specific fuel consumption (BSFC), a 27.09 % decrease in carbon monoxide (CO) emissions, a 37.50 % decrease in unburned hydrocarbon (UHC) emissions, and a 28.53 % reduction in smoke opacity compared to traditional diesel operation. These results highlight the potential for enhanced CI engine performance and reduced emissions through the strategic combination of waste transformer oil-based nano-fuel and hydrogen premixing. Consequently, we recommend further exploration of hydrogen premixing in conjunction with nano-enhanced waste transformer oil for superior CI engine performance. © 2023 Hydrogen Energy Publications LLC

关键词： Insulating oil

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Textual Emotion Recognition: Advancements and Insights

Textual Emotion Recognition: Advancements and Insights

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Novel Intelligent and Leading Emerging sciences Conference (NILES)

作者： Ahmed A. Esmail Sayed M. Abdallah Mohamed A. Elsayed Amr E. Ghaith Magy Elbanhawy Aya M. Al-Zoghby Ibrahim F. Moawad Faculty of Computer Science and Engineering Artificial Intelligence Science New Mansoura University Dakhlia Egypt Faculty of Computers and Information Mansoura University Dakhlia Egypt Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Faculty of Engineering Portsaid University Egypt Computer Science Department Faculty of Computers and Artificial Intelligence Damietta University Damietta Egypt Information Systems Faculty of Computer and Information Sciences Ain shams University Cairo Egypt Department of Artificial Intelligence College of Computer Science and Engineering Taibah University Medina Saudi Arabia

ISBN: (数字)9798350378511

ISBN: (纸本)9798350378528

In recent years, there has been a significant increase in attention toward emotion detection in text analysis, driven by its broad applications across marketing, political science, psychology, human-computer interaction, and artificial intelligence. This growing interest is primarily due to the critical role of textual expression as a repository of human emotions and sentiments. The development of sophisticated natural language processing (NLP) techniques has emphasized the importance of exploring emotion detection and recognition within textual data. By utilizing a wide range of sources, including social media content, microblogs, news articles, and customer feedback, text mining aims to reveal the underlying emotional currents within the text. However, existing models often struggle to capture the complicated emotional nuances woven into words. Addressing this challenge, the innovative semantic emotion neural network (SENN) architecture has been introduced. The SENN model marks a significant advancement, featuring two synergistic sub-networks: a bidirectional long short-term memory (BiLSTM) network that extracts contextual information and a convolutional neural network (CNN) that analyzes and extracts emotional features, highlighting the text's intrinsic emotional connections. The SENN model's performance has been thoroughly evaluated on widely used real-world datasets, benchmarked against Ekman's six fundamental emotions. Results demonstrated its superiority, showing that the SENN model excels in emotion recognition accuracy and quality in conjunction with additional techniques. It also holds potential for enhancement by incorporating more comprehensive emotional word embedding, suggesting a promising future for text-based emotion analysis. The proposed paper presents goals for detecting sentiment in text data and introduces a novel architecture that effectively captures the complexity of emotional nuances. We create an abstract model and compare three types of m

关键词： Training Emotion recognition Logistic regression Accuracy Text analysis Text recognition Machine learning Feature extraction Natural language processing Long short term memory

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Optimizing Feature Selection in Machine Learning with E-BPSO: A Dimensionality Reduction Approach 3rd

Optimizing Feature Selection in Machine Learning with E-BP...

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3rd International Semantic Intelligence Conference, ISIC 2023

作者： Shenbaga Moorthy, Rajalakshmi Arikumar, K.S. Prathiba, Sahaya Beni Pabitha, P. Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Chennai600 116 India Department of Data Science and Business Systems SRM University – Kattankulathur Chennai India Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India Department of Computer Technology Anna University Madras Institute of Technology Campus Chennai600044 India

ISBN: (纸本)9789819773558

In the era of informatics, the effectiveness of machine learning models is compromised due to the challenge of dimensionality in the data. The presence of redundant and irrelevant features significantly increases computational complexity, posing a central obstacle in the extraction of valuable insights from the extensive dataset. Any machine learning model’s performance suffers because of the issue of the plague of dimensionality. To improve the classifier’s performance, feature selection is applied beforehand on applying the machine learning model. Feature selection is accomplished using Enhanced Binary Particle Swarm Optimization (E-BPSO) with the aid of boosting the performance of the K-Nearest Neighbor (K-NN) classifier and is experimented on benchmarking real-world datasets. The conventional BPSO suffers from the problem of exploration which leads to premature convergence. In order to overcome the drawbacks of conventional BPSO, E-BPSO is proposed. The enhancement is made by integrating the self-adaptive velocity to drive the particle with the aid to balance exploration and exploitation. The performance of the proposed E-BPSO is evaluated against the traditional binary particle swarm optimization algorithm and genetic algorithm, considering metrics like accuracy, fitness, root mean square error, and dimensionality reduction ratio. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Dimensionality reduction

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Remote Sensing Scene Classification Based on Hybrid Convolutional Neural Network and Attention Module

Remote Sensing Scene Classification Based on Hybrid Convolut...

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Advancement in Data science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Muhammad Yusuf Kardawi Alexander Agung Santoso Gunawan Profesional Engineer Program Faculty of Engineering Bina Nusantara University Jakarta Indonesia Computer Science Department Faculty of Computer Science University of Indonesia Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Remote sensing images present classification challenges due to the complexity of their structural and spatial patterns. This research explores a hybrid approach that combines convolutional neural network (CNN) and attention through feature fusion to improve scene classification accuracy in remote sensing images. The proposed architecture utilizes EfficientNet and VGGNet to extract depth features separately. The extracted features are then integrated with Dynamic Selfattention (DSA), which dynamically focuses the model on the most relevant information in the image. DSA allows the model to adaptively assign weights to different parts of the image, thus improving the discriminative ability of the model. Furthermore, a feature fusion technique is applied to combine information from different layers of the CNN and DSA modules. Experiments conducted on the UC Merced dataset showed accuracies of 0.9181 and 0.9167. These results show that the combination of CNN, DSA, and feature fusion is an effective and robust approach for remote sensing image classification.

关键词： Adaptation models Scene classification Accuracy Electronic learning Data science Feature extraction Convolutional neural networks Data mining Remote sensing Information systems

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First wetland mapping at 10-m spatial resolution in South America using multi-source and multi-feature remote sensing data

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science China Earth sciences 2024年第10期67卷 3252-3269页

作者： Weiwei SUN Gang YANG Yuling HUANG Dehua MAO Ke HUANG Lin ZHU Xiangchao MENG Tian FENG Chao CHEN Yong GE Department of Geography and Spatial Information Techniques Ningbo UniversityNingbo 315211China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences&Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Natural Resources and Planning Bureau Shanghai Pudong New DistrictShanghai 200100China Key Laboratory of Wetland Ecology and Environment Northeast Institute of Geography and AgroecologyChinese Academy of SciencesChangchun 130102China Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China School of Geography Science and Geomatics Engineering Suzhou University of Science and TechnologySuzhou 215009China

Wetland degradation has been accelerating in recent years globally. Accurate information on the geographic distribution and categories of wetlands is essential for their conservation and management. Despite being the world′s fourth largest continent, South America has limited research on wetland mapping, and there is currently no available map that provides comprehensive information on wetland distribution and categories in the region. To address this issue, we used Sentinel-1, Sentinel-2 and SRTM data, developed a sample collection method and a wetland mapping method with a collection of multi-source features such as optical features, polarization features and shape features for South American wetlands. We produced a 10-m resolution wetland map based on the Google Earth Engine(GEE) platform. Our Level-1 wetland cover map accurately captured six wetland sub-categories with an overall accuracy of 96.24% and a kappa coefficient of 0.8649, while our Level-2 water cover map included five sub-categories with an overall accuracy of 97.23% and a kappa coefficient of0.9368. The results show that the total area of existing wetlands in South America is approximately 1,737,000 km~2, which is6.8% of the total land area. Among the ten wetland categories, shallow sea had the largest area(960,527.4 km~2), while aquaculture ponds had the smallest area 1513.6 km~2. Swamp had the second largest area(306,240.1 km~2). Brazil, Argentina,Venezuela, Bolivia, and Colombia were found to have the largest wetland areas, with Brazil and Colombia having the most diverse wetland categories. This product can serve as baseline data for subsequent monitoring, management, and conservation of South American wetlands.

关键词： Wetland mapping Google Earth Engine Sentinel imagery South America

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Tumor Infiltration of Microrobot using Magnetic torque and AI Technique 2

Tumor Infiltration of Microrobot using Magnetic torque and A...

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2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Shalini, R. Mishra, Laxmi Athulya, S. Chimankar, Abhijeet Gajanan Kandavalli, Sunanda Ratna. Keshav Kumar, K. Selvan, R. Senthamil School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Department of BCA & IT Chennai India Khwaja Moinuddin Chishti Language University Department of Electronics and Communication Engineering Uttar Pradesh India Christ Deemed to Be University Faculty of Engineering Department of Computer Science and Engineering Bangalore India Chatrapati Shivaji BCA and BCS College Department of B.Sc. Computer Science Maharashtra India Sri Padmavati Mahila Visva Vidyalayam Department of Electronics and Communication Engineering Tirupati India G. Narayanamma Institute of Technology and Science Department of Humanities and Mathematics Hyderabad India Annamacharya Institute of Technology & Sciences Department of Electronics and Communication Engineering Tirupati India

ISBN: (纸本)9798350347982

Because of their surroundings and lifestyle alternatives, human beings, these days be afflicted by a huge style of illnesses. thus, early contamination prediction will become crucial. on the other hand, primarily based just on signs, docs warfare to make correct forecasts. The most challenging issue is accurately forecasting illnesses, which is why machine learning is essential to accomplish this task. To identify concealed patterns within vast amounts of medical data, disease information is processed using data mining techniques. We evolved a extensive contamination prediction primarily based on the affected person's signs. We rent the device getting to know techniques Convolutional Neural network (CNN) and ANFIS to exactly count on sickness (adaptive community-based totally fuzzy inference machine). For an correct forecast, this trendy illness prediction considers the character's way of life picks and fitness history. ANFIS outperforms CNN's set of rules in phrases of popular infection prediction, with an accuracy price of 96.7%. additionally, CNN consumes extra memory and processing energy than ANFIS because it trains and assessments on facts from the UCI repository. The Anaconda notebook is a suitable tool for implementing Python programming as it contains a range of libraries and header files that enhance the accuracy and precision of the process. © 2023 IEEE.

关键词： Forecasting

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