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Optimal Deep Dense Convolutional Neural Network Based Classification Model for COVID-19 Disease

Computers, Materials & Continua 2022年第1期70卷 2031-2047页

作者： A.Sheryl Oliver P.Suresh A.Mohanarathinam Seifedine Kadry Orawit Thinnukool Department of Computer Science and Engineering St.Joseph’s College of Engineering600119ChennaiIndia Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Faculty of Engineering Karpagam Academy of Higher EducationCoimbatore641021India Faculty of Applied Computing and Technology Noroff University CollegeKristiansand4608Norway Research Group of Embedded Systems and Mobile Application in Health Science College of ArtsMedia and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Early diagnosis and detection are important tasks in controlling the spread of COVID-19.A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images and ***,these methods suffer from biased results and inaccurate detection of the ***,the current research article developed Oppositional-based Chimp Optimization Algorithm and Deep Dense Convolutional Neural Network(OCOA-DDCNN)for COVID-19 prediction using CT images in IoT *** proposed methodology works on the basis of two stages such as pre-processing and ***,CT scan images generated from prospective COVID-19 are collected from open-source system using IoT *** collected images are then preprocessed using Gaussian *** filter can be utilized in the removal of unwanted noise from the collected CT scan ***,the preprocessed images are sent to prediction *** this phase,Deep Dense Convolutional Neural Network(DDCNN)is applied upon the pre-processed *** proposed classifier is optimally designed with the consideration of Oppositional-basedChimp Optimization Algorithm(OCOA).This algorithm is utilized in the selection of optimal parameters for the proposed ***,the proposed technique is used in the prediction of COVID-19 and classify the results as either COVID-19 or *** projected method was implemented in MATLAB and the performances were evaluated through statistical *** proposed method was contrasted with conventional techniques such as Convolutional Neural Network-Firefly Algorithm(CNN-FA),Emperor Penguin Optimization(CNN-EPO)*** results established the supremacy of the proposed model.

关键词： Deep learning deep dense convolutional neural network covid-19 CT images chimp optimization algorithm

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Modified pentagonal fractal antenna for ultra-wideband applications in communication systems

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International Journal of Electronics Letters 2024年第4期12卷 413-421页

作者： Singh, Gurmeet Kumar, Ashish Jit Singh, Mandeep Singh, A.P. School of Computing Science and Engineering Galgotias University Greater Noida India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Electrical Electronic and Systems Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia UKM Selangor Bangi Malaysia Department of Electronics and Communication Engineering Sant Longowal Institute of Engineering and Technology Punjab Longowal India

Due to the recent expansion in the generations of the communications systems, it is utmost priority to transmit a large amount of data over a wideband frequency spectrum. For this single ultra-wideband (UWB) antennas are the prime choice as compared to the multiple narrow band antennas. A modified pentagonal fractal patch antenna with a modified ground plane covering ultra-wideband has been proposed. The antenna has a compact size of 25 mm × 25 mm × 1.6 mm with peak gain of 2.2 dB. The UWB coverage of the design is achieved by implementing modified co-planar ground, modified fractal geometry and coplanar feed. The proposed design possesses the significant resonance and radiation performance with very compact size on low-cost substrate while covering the whole UWB. Finally, the simulated resonating and radiation characteristics have been validated with the measured characteristics of the fabricated prototype and the results are quite promising. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： antenna co-planer feed fractal Ultra-wideband

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An Efficient Automated Technique for Classification of Breast Cancer Using Deep Ensemble Model

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Computer Systems science & engineering 2023年第7期46卷 897-911页

作者： Muhammad Zia Ur Rehman Jawad Ahmad Emad Sami Jaha Abdullah Marish Ali Mohammed A.Alzain Faisal Saeed Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City University15 Bartholomew RowBirminghamB55JUUK

Breast cancer is one of the leading cancers among *** has the second-highest mortality rate in women after lung *** detection,especially in the early stages,can help increase survival ***,manual diagnosis of breast cancer is a tedious and time-consuming process,and the accuracy of detection is reliant on the quality of the images and the radiologist’s ***,computer-aided medical diagnosis has recently shown promising results,leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early *** research presented in this paper is focused on the multi-class classification of breast *** deep transfer learning approach has been utilized to train the deep learning models,and a pre-processing technique has been used to improve the quality of the ultrasound *** proposed technique utilizes two deep learning models,Mobile-NetV2 and DenseNet201,for the composition of the deep ensemble *** learning models are fine-tuned along with hyperparameter tuning to achieve better ***,entropy-based feature selection is *** cancer identification using the proposed classification approach was found to attain an accuracy of 97.04%,while the sensitivity and F1 score were 96.87%and 96.76%,*** performance of the proposed model is very effective and outperforms other state-of-the-art techniques presented in the literature.

关键词： Breast cancer image enhancement ensemble model transfer learning feature selection

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Generative AI, IoT, and blockchain in healthcare: application, issues, and solutions

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Discover Internet of Things 2025年第1期5卷 1-23页

作者： Mazhar, Tehseen khan, Sunawar Shahzad, Tariq khan, Muhammad Amir Saeed, Mamoon M. Awotunde, Joseph Bamidele Hamam, Habib School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science International Islamic University Islamabad44000 Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Sana’a Yemen Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Science and Information Technology School Education Department Government of Punjab 31200 Layyah Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa Av. Grandes Ecoles LibrevilleBP 1989 Gabon Bridges for Academic Excellence Spectrum Tunis Tunisia

This article discusses Blockchain and Generative AI in healthcare, including their uses, difficulties, and solutions. Blockchain technology improves EHR security, privacy, and interoperability, while smart contracts streamline supply chain management and administrative procedures. Blockchain verifies and secures IoT data, improving medical care and treatment, according to case studies. Generative AI systems like ChatGPT have transformed healthcare by personalizing therapy, diagnostics, and predictive analytics. AI systems can examine massive databases to diagnose diseases early, anticipate dangers, and personalize therapies. By providing timely information, boosting treatment adherence, and giving continuous support, AI-powered virtual health assistants have enhanced patient involvement. Generative AI has additionally enhanced medical research and drug development, cutting the time and expense of introducing new medicines. Generative AI and Blockchain provide safe patient data storage, high-quality AI training datasets, and efficient healthcare operations. Scalability, energy usage, and interoperability issues remain. Scalable Blockchain designs and standardized data integration and exchange protocols are suggested by this study. These technologies could improve medical research and therapy by making them safer, more effective, and more individualized. © The Author(s) 2025.

关键词： Electronic health record

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Analytical Model for Underwater Wireless Sensor Network Energy Consumption Reduction

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Computers, Materials & Continua 2022年第7期72卷 1611-1626页

作者： Huma Hasan Rizvi Sadiq Ali Khan Rabia Noor Enam Kashif Nisar Muhammad Reazul Haque Computer Science Department UBIT Karachi UniversityKarachi75270Pakistan Sir Syed University of Engineering and Technology Karachi75300Pakistan Faculty of Computing and Informatics University Malaysia SabahJalan UMS88400Kota KinabaluSabahMalaysia Faculty of Computing&Informatics Multimedia UniversityPersiaran Multimedia63100CyberjayaSelangorMalaysia

In an Underwater Wireless Sensor Network(UWSN),extreme energy loss is carried out by the early expiration of sensor nodes and causes a reduction in efficiency in the submerged acoustic sensor *** based on clustering strategies,instead of each node sending information by itself,utilize cluster heads to collect information inside the clusters for forwarding collective information to *** can effectively minimize the total energy loss during *** environment of UWSN is 3D architecture-based and follows a complex hierarchical clustering strategy involving its most effecting unique parameters such as propagation delay and limited transmission *** base clustering strategy works in rounds,where each round comprises three fundamental stages:cluster head selection,grouping or node association,and data aggregation followed by forwarding data to the *** UWSN,the energy consumed during the formation of clusters has been considered casually or completely evaded in the previous *** this paper,the cluster head setup period has been considered the main contributor to extra energy utilizer.A numerical channel model is proposed to compute extra *** is performed by using a UWSN broad *** results have shown that extra maximum energy consumption is approximately 12.9 percent of the system total energy consumed in information transmissions.

关键词： Analytical model submerged acoustic sensor systems energy loss round base clustering UWSN extra energy

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Quick-MIMIC:A Multimodal Data Extraction Pipeline for MIMIC with Parallelization

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Big Data Mining and Analytics 2024年第4期7卷 1333-1346页

作者： Yutao Dou Wei Li Yangtao Zheng Xiaojun Yao Huanxiang Liu Albert Y.Zomaya Shaoliang Peng College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082Chinaand also with Centre for Distributed and High Performance ComputingSchool of Computer ScienceThe University of SydneyDarlingtonNSW 2008Australia College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China Centre for Distributed and High Performance Computing School of Computer ScienceThe University of SydneyDarlingtonNSW 2008Australia Faculty of Applied Sciences Macao Polytechnic UniversityMacao 999078China

Medical big data with artificial intelligence are vital in advancing digital ***,the opaque and non-standardised nature embedded in most medical data extraction is prone to batch effects and has become a significant obstacle to reproducing previous *** paper aims to develop an easy-to-use time-series multimodal data extraction pipeline,Quick-MIMIC,for standardised data extraction from MIMIC *** method can fully integrate different data structures into a time-series table,including structured,semi-structured,and unstructured *** also introduce two additional modules to Quick-MIMIC,a pipeline parallelization method and data analysis methods,for reducing the data extraction time and presenting the characteristics of the extracted data *** extensive experimental results show that our pipeline can efficiently extract the needed data from the MIMIC dataset and convert it into the correct format for further analytic tasks.

关键词： MIMIC dataset data extraction pipeline data integration

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Online Learning Behavior Analysis and Prediction Based on Spiking Neural Networks

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Journal of Social computing 2024年第2期5卷 180-193页

作者： Yanjing Li Xiaowei Wang Fukun Chen Bingxu Zhao Qiang Fu the Institute of Education Science Research Heilongjiang UniversityHarbin 150080China the School of Cyberspace Security Shandong University of Political Science and LawJinan 250014China the School of Computer Science and Technology Harbin Engineering UniversityHarbin 150001China the Faculty of Electrical and Computer Engineering University of VictoriaVictoriaV8P 5C2Canada. the Guangxi Key Lab of Brain-Inspired Computing and Intelligent Chips School of Electronic and Information EngineeringGuangxi Normal UniversityGuilin 541004China

The vast amount of data generated by large-scale open online course platforms provide a solid foundation for the analysis of learning behavior in the field of *** study utilizes the historical and final learning behavior data of over 300000 learners from 17 courses offered on the edX platform by Harvard University and the Massachusetts Institute of Technology during the 2012-2013 academic *** have developed a spike neural network to predict learning outcomes,and analyzed the correlation between learning behavior and outcomes,aiming to identify key learning behaviors that significantly impact these *** goal is to monitor learning progress,provide targeted references for evaluating and improving learning effectiveness,and implement intervention measures *** results demonstrate that the prediction model based on online learning behavior using spiking neural network achieves an impressive accuracy of 99.80%.The learning behaviors that predominantly affect learning effectiveness are found to be students’academic performance and level of participation.

关键词： online learning learning outcomes prediction learning behavior analysis spiking neural network

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A Gamified Virtual Learning Environment to Enhance Online Teaching and Learning Experience 7

A Gamified Virtual Learning Environment to Enhance Online Te...

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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Tharaka, W.C.M.K. Dilanka, R.M.T. Perera, H.D.D.S. Rathnayake, R.H.C.S. Kodagoda, Nuwan Suriyawansa, Kushnara Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Sri Lanka

ISBN: (纸本)9781665421683

In-class teaching not only concentrates on lecture content delivery but also on maintaining strong mutuality between lecturer-students and student-student. Online lectures are gaining popularity due to the Covid-19 pandemic. However, the learning-teaching process has become ineffective because existing video conferencing solutions are not intended for academic purposes. This research was conducted to identify the pain points of online education and develop an enhanced software solution. A user survey confirmed that an isolated environment tends to diminish attentiveness during online lectures. Also, it is difficult for teachers to observe the attentiveness of all the students. As a solution, student attentiveness was measured using their facial expressions and collected data shown to lecturers through a virtual student behavioural environment. The physical separation causes students to feel isolated during lectures, which can negatively affect their academic development and social and psychological development. The developed application also provides a virtual group study environment as a feature. According to the results gathered in the user acceptance testing phase, it was found that the attention detection feature helps students keep their attention at a significant level. Further, 9 out of 10 teachers who participated in the testing acknowledged that the simulated student behavioural view could provide a more immersive experience. 71% of students preferred the new collaborative virtual environment, and students further elaborated that the virtual environment was more likely the physical group studies. 72% of students mentioned that the collaborative group study tool helped eliminate isolation during group studies. © 2022 IEEE.

关键词： E-learning

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Predictive Analysis of Consumer Behavior Using Supervised Learning Techniques 2

Predictive Analysis of Consumer Behavior Using Supervised Le...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Rekha, K. Sashi Amutha, T. Usharani, R. Pushparani, S. Sivagami, V.M. Saveetha Institute of Medical and Technical Sciences Simats Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence School of Computing Chennai India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350364699

The study presented here consists of an entire framework for prediction of consumers behavior through the machine training of supervised learning methods. The strategy involves the following: data collection, preprocessing, exploration, feature selection, model selection, training, evaluation, interpretation and deployment. The systematic process is tailored to clear the pathway for combining data about customer taste, sophisticated habits, and purchasing trends. Primary components include data preprocessing, training of machine learning models, feature selection, and ethical issues regarding data privacy and transparency. The outcomes of the implementation are a high accuracy in consumers clustering, efficient classification of customers' behavior, as well as unveiling a bunch of crucial predictors that affect the customers mood and behavior. As a result, the report stresses the necessity of integrated monitoring and redirection strategies to realize the potential of the created models. The idea of predictive analysis as an evolving tool that enables marketers to tailor their campaigns more closely, and improve the overall customer experience therefore also receives attention. The proposed system methodology constitutes a solid process framework for business defined to aim at using predictive analytics to gain competitive edge in the current dynamic marketplace. © 2024 IEEE.

关键词： Self-supervised learning

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Integrating digital twins and deep learning for medical image analysis in the era of COVID-19

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Virtual Reality & Intelligent Hardware 2022年第4期4卷 292-305页

作者： Imran AHMED Misbah AHMAD Gwanggil JEON School of Computing and Information Science Faculty of Science and EngineeringAnglia Ruskin UniversityEast RoadCambridgeEnglandUK Center of Excellence in IT Institute of Management SciencesHayatabadPeshawarPakistan Department of Embedded Systems Engineering Incheon National UniversityIncheonKorea

Background Digital twins are virtual representations of devices and processes that capture the physical properties of the environment and operational algorithms/techniques in the context of medical devices and *** twins may allow healthcare organizations to determine methods of improving medical processes,enhancing patient experience,lowering operating expenses,and extending the value of *** the present COVID-19 pandemic,various medical devices,such as X-rays and CT scan machines and processes,are constantly being used to collect and analyze medical *** collecting and processing an extensive volume of data in the form of images,machines and processes sometimes suffer from system failures,creating critical issues for hospitals and *** To address this,we introduce a digital-twin-based smart healthcare system in-tegrated with medical devices to collect information regarding the current health condition,configuration,and maintenance history of the device/machine/***,medical images,that is,X-rays,are analyzed by using a deep-learning model to detect the infection of *** designed system is based on the cascade recurrent convolution neural network(RCNN)*** this architecture,the detector stages are deeper and more sequentially selective against small and close false *** architecture is a multi-stage extension of the RCNN model and sequentially trained using the output of one stage for training the *** each stage,the bounding boxes are adjusted to locate a suitable value of the nearest false positives during the training of the different *** this manner,the arrangement of detectors is adjusted to increase the intersection over union,overcoming the problem of *** train the model by using X-ray images as the model was previously trained on another *** The developed system achieves good accuracy during the detection phase of *** experimental o

关键词： Digital twins Deep learning Healthcare COVID-19 Chest X-rays Artificial intelligence

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