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Deep Optimal VGG16 Based COVID-19 Diagnosis Model

computers, Materials & Continua 2022年第1期70卷 43-58页

作者： M.Buvana K.Muthumayil S.Senthil kumar Jamel Nebhen Sultan S.Alshamrani Ihsan Ali Department of Computer Science and Engineering PSNA College of Engineering and TechnologyDindigul624622India Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and TechnologyChennai600073India Prince Sattam Bin Abdulaziz University College of Computer Engineering and SciencesAlkharj11942Saudi Arabia Department of Information Technology College of Computer and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala Lumpur50603Malaysia

Coronavirus(COVID-19)outbreak was first identified in Wuhan,China in December *** was tagged as a pandemic soon by the WHO being a serious public medical *** spite of the fact that the virus can be diagnosed by qRT-PCR,COVID-19 patients who are affected with pneumonia and other severe complications can only be diagnosed with the help of Chest X-Ray(CXR)and Computed Tomography(CT)*** this paper,the researchers propose to detect the presence of COVID-19 through images using Best deep learning model with various *** features like Speeded-Up Robust Features(SURF),Features from Accelerated Segment Test(FAST)and Scale-Invariant Feature Transform(SIFT)are used in the test images to detect the presence of *** optimal features are extracted from the images utilizing DeVGGCovNet(Deep optimal VGG16)model through optimal learning *** task is accomplished by exceptional mating conduct of Black Widow *** this strategy,cannibalism is *** this phase,fitness outcomes are rejected and are not satisfied by the proposed *** results acquired from real case analysis demonstrate the viability of DeVGGCovNet technique in settling true issues using obscure and testing ***16 model identifies the imagewhich has a place with which it is dependent on the distinctions in *** impact of the distinctions on labels during training stage is studied and predicted for test *** proposed model was compared with existing state-of-the-art models and the results from the proposed model for disarray grid estimates like Sen,Spec,Accuracy and F1 score were promising.

关键词： COVID 19 multi-feature extraction vgg16 optimal learning rate

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Presenting a hybrid method based on dark/bright channel, fuzzy and luminance mapping estimation for color image enhancement 8

Presenting a hybrid method based on dark/bright channel, fuz...

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8th International Iranian Conference on Signal Processing and Intelligent Systems, ICSPIS 2022

作者： Rezvan, Mohammad Reza Gholanloo, Majid Heidari Sorkhi, Ali Ghanbari Mazandaran University of Science and Technology Faculty of Electrical and Computer Engineering Behshahr Iran

ISBN: (纸本)9781665476232

The lighting conditions at the environment are not favorable for imaging, and the problem of light is one of the challenges of imaging in the environment. The lack of sufficient light in the images reduces the quality and details of recognition by humans. In this paper, a hybrid method for image enhancement in low light is proposed. At first, dark/bright channel algorithm based on low-light images is used for minimum/maximum brightness images. In the next step, the fuzzy algorithm is used to weight different levels of brightness, and finally, the brightness mapping estimation method called LIME is used along with a denoising to improve the image. To evaluate the proposed method, the Universal Image Quality Index (UQI) indices were used, and the results obtained on the base images proved the performs of the method we proposed is better than the other novel methods. © 2022 IEEE.

关键词： Image enhancement

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Performance Analysis of Feature Extraction and Deep Learning Approaches on Whole and Segmented Histopathological Images for Cancer Grade Classification 2

Performance Analysis of Feature Extraction and Deep Learning...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Kumaraswamy, Eelandula Kumar, Sumit Sharma, Shallu Lovely Professional University School of Electronics and Electrical Engineering Punjab Phagwara India Bennett University School of Computer Science Engineering and Technology U.P Greater Noida India

ISBN: (纸本)9798350360523

Breast cancer is a leading cause of cancer-related deaths globally, necessitating effective diagnostic measures. Tissue biopsy examination and histopathology image analysis are pivotal in clinical cancer diagnosis. Various feature extraction approaches and classifiers have been introduced for histopathology analysis by the research community. This study focuses on enhancing tissue classification systems through two distinct pipelines applied to the Databiox dataset. In the first pipeline, histopathological images undergo segmentation using the watershed algorithm. Subsequently, features are extracted from the segmented images using the gray level co-occurrence matrix and VGG16 model. The extracted features are then classified using two classifiers, namely dense layer of CNN and Random Forest. The second pipeline involves extracting features directly from entire histopathological images, with subsequent classification using gray level co-occurrence matrix and VGG16 model. Dense layer and RF classifiers are employed to classify the features from whole images. Additionally, Principal Component Analysis is employed to select crucial feature vectors. The evaluation of direct and segmented histopathological images from the Databiox dataset, with a focus on segmentation, reveals a gap in existing literature. Experimental results demonstrate that the VGG16 model, coupled with a classifier, yields superior outcomes when applied to entire histopathological images compared to segmented images. © 2024 IEEE.

关键词： Feature extraction

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Improving Online Education in Educational Institutions through the Integration of Internet of Things (IoT) 8

Improving Online Education in Educational Institutions throu...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Ramaraj, Kottaimalai Mathews, Ann Lynsha Helena Pratheeba, Hp Ramani, U. Sujitha, K.L. Thilagaraj, M. Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamilnadu Krishnankoil India Cmr Institute of Technology Department of Computer Science and Engineering Bengaluru India K Ramakrishnan College of Engineering Department of Electrical and Electronics Engineering Trichy India Mvj College of Engineering Department of Computer Science and Engineering Bengaluru India Mvj College of Engineering Department of Industrial Internet of Things Bengaluru India

ISBN: (纸本)9798350367904

A key facilitating infrastructure for building intelligent structures that enable efficient in-person and virtual learning environments is the Internet of Things (IoT). The shift to smart learning, that includes IoT and Artificial Intelligence (AI) into the educational system, is enticing since it directly affects the motivation of learners, enrolment, participation, and in-depth understanding. Teaching and learning, management, examination, and class monitoring are only a few of the numerous issues that afflict conventional education. Many ingenious approaches to many facets of our lives are being made possible by modern advancements in information and communication technology (ICT);nonetheless, these imaginative concepts aren't adequately incorporated into the framework of education. The COVID-19 pandemic scenario, in specific, has brought out the need for innovative, creative approaches in the field of teaching. The present research examines previous investigations in the field and discusses the following topics: (i) issues with the current educational system and potential fixes;(ii) the shift to smart learning;and (iii) scientific hurdles (such as technological and cultural barriers) in making the move to smart learning. In accordance with these outcomes, creative approaches to the issues with the conventional system-such as smart teaching, smart evaluation, smart school environment, and smart management-are presented. The business and academic community may now include IoT, AI, and ICT into smart learning owing to the aforementioned experimental research's novel developments. © 2024 IEEE.

关键词： Federated learning

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The Importance of Project-Scale Scaffolding for Retention and Experience in Computing Courses 53

The Importance of Project-Scale Scaffolding for Retention an...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： De Souza, Juliana Goncalves Flavell, Mikaila Suleiman, Ahmad Daudu Shepherd, David Dewaters, Jan Small, Mary Margaret Liu, Yu Hou, Daqing Virginia Commonwealth University Computer Science Richmond United States Clarkson University Electrical and Computer Engineering Potsdam United States Louisiana State University Computer Science Baton Rouge United States Institute for Stem Education Clarkson University Potsdam United States

ISBN: (纸本)9798350336429

Teaching students complex problem-solving skills using large-scale, real-world problems is challenging for both students and teachers alike. As a result, most courses use small, well-specified, toy-like problems, which are not representative of what students will encounter in the workforce. One approach that allows teachers to use large-scale problems in class is by introducing scaffolding. Scaffolding breaks a larger problem into smaller steps, which students can solve independently, while deemphasizing tangential concepts such as the complex configuration files needed to compile open-source software systems. Strong scaffolding supports student learning, preventing them from getting bogged down with unnecessary tasks or overwhelmed by complexity. This work investigates a scaffolded problem-based-learning module for computing courses, using a realistically-sized project with characteristics representative of the industry. The project was implemented in a computer science course with roughly 100 students, and the results speak to the importance of scaffolding for student success. In fact, there were two student assignments that lacked sufficient scaffolding, compared with other tasks, and the reduction in student scoring and persistence shows that project scaffolding is necessary when implementing these types of assignments. Most students felt the project helped prepare them for a job in their chosen field. © 2023 IEEE.

关键词： computer science pedagogy problem-based learning project-based learning scaffolding

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BatMobility: Towards Flying Without Seeing for Autonomous Drones 23

BatMobility: Towards Flying Without Seeing for Autonomous Dr...

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29th Annual International Conference on Mobile Computing and Networking, MobiCom 2023

作者： Sie, Emerson Liu, Zikun Vasisht, Deepak Electrical and Computer Engineering University of Illinois Urbana-Champaign ChampaignIL60000745 United States Computer Science University of Illinois Urbana-Champaign ChampaignIL60000745 United States

ISBN: (纸本)9781450399906

Unmanned aerial vehicles (UAVs) rely on optical sensors such as cameras and lidar for autonomous operation. However, such optical sensors are error-prone in bad lighting, inclement weather conditions including fog and smoke, and around textureless or transparent surfaces. In this paper, we ask: is it possible to fly UAVs without relying on optical sensors, i.e., can UAVs fly without seeing? We present BatMobility, a lightweight mmWave radar-only perception system for UAVs that eliminates the need for optical sensors. BatMobility enables two core functionalities for UAVs - radio flow estimation (a novel FMCW radar-based alternative for optical flow based on surface-parallel doppler shift) and radar-based collision avoidance. We build BatMobility using commodity sensors and deploy it as a real-time system on a small off-the-shelf quadcopter running an unmodified flight controller. Our evaluation1 shows that BatMobility achieves comparable or better performance than commercial-grade optical sensors across a wide range of scenarios. © 2023 ACM.

关键词： Real time systems

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A Parallel High-Utility Itemset Mining Algorithm Based on Hadoop

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Complex System Modeling and Simulation 2023年第1期3卷 47-58页

作者： Zaihe Cheng Wei Shen Wei Fang Jerry Chun-Wei Lin School of Internet of Things Wuxi Institute of TechnologyWuxi 214121China School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi 214122China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan UniversityWuxi 214122China Department of Computer Science Electrical Engineering and Mathematical SciencesWestern Norway University of Applied SciencesBergen 5020Norway

High-utility itemset mining(HUIM)can consider not only the profit factor but also the profitable factor,which is an essential task in data ***,most HUIM algorithms are mainly developed on a single machine,which is inefficient for big data since limited memory and processing capacities are available.A parallel efficient high-utility itemset mining(P-EFIM)algorithm is proposed based on the Hadoop platform to solve this problem in this *** P-EFIM,the transaction-weighted utilization values are calculated and ordered for the itemsets with the MapReduce *** the ordered itemsets are renumbered,and the low-utility itemsets are pruned to improve the dataset *** the Map phase,the P-EFIM algorithm divides the task into multiple independent *** uses the proposed S-style distribution strategy to distribute the subtasks evenly across all nodes to ensure ***,the P-EFIM uses the EFIM algorithm to mine each subtask dataset to enhance the performance in the Reduce *** are performed on eight datasets,and the results show that the runtime performance of P-EFIM is significantly higher than that of the PHUI-Growth,which is also HUIM algorithm based on the Hadoop framework.

关键词： pattern mining data mining Hadoop parallel high-utility itemset mining big data

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A Survey on Machine Learning Approaches in Water Analysis 13th

A Survey on Machine Learning Approaches in Water Analysis

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13th Mining Humanistic Data Workshop, MHDW 2024, 9th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st Workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 International Workshops on Artificial Intelligence Applications and Innovations, AIAI 2024

作者： Tsimpidi, Ilektra Sartjärvi, Rosa Juntunen, Petri Nikolakopoulos, George Robotics and AI Team Department of Computer Science Space and Electrical Engineering Luleå University of Technology Luleå Sweden Savonia University of Applied Sciences Kuopio Finland

ISBN: (纸本)9783031632266

The aim of this article is to present a survey on Machine Learning approaches for performing water analysis as in general integrating Artificial Intelligence in water analysis has a transformative potential for optimizing and sustaining water resources. Recent trends in water treatment and desalination applications effectively address pollution and scarcity challenges, while by combining AI with technologies like data analytics, water management complexities are simplified, ensuring sustainability and cost-effectiveness through predictive data assessment. This survey presents examples of the ML’s significance in water treatment and in water quality analysis, such as for enhancing accuracy in water quality index (WQI) assessments and examples of exploring and predicting pollutants in water processes, and aiding in the early detection and categorization of contaminants. Furthermore, this survey will reveal the immense potential in leveraging ML algorithms to enhance water analysis accuracy, speed, and efficiency, paving the way for reduced chemical usage and improved understanding of microbiological processes. As it will be presented, ML approaches offer valuable tools across various stages of water analysis, from identifying critical indicators to accurately measuring and predicting water parameters. The survey concludes by emphasizing the potential of AI and particularly ML to revolutionize water resource management, offering precision, efficiency, and foresight in addressing challenges of water scarcity and sustainable resource utilization. © IFIP International Federation for Information Processing 2024.

关键词： Machine learning

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Clustering-Based Under-Sampling with Normalization in Class-Imbalanced Data

Clustering-Based Under-Sampling with Normalization in Class-...

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2022 IEEE International Conference on Current Development in engineering and Technology, CCET 2022

作者： Akash, Abdul Hady Mahi, Fabiha Faiz Mondal, Tanmoy Rahman, Md. Naimur Ishrak, Ibne Farhan Rahman, Mohammad Azizur Arnob, Shad Alvee, Sajan Mahamud University of Asia Pacific Department of Computer Science and Engineering Dhaka Bangladesh Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh North South University Department of Electrical and Computer Engineering Dhaka Bangladesh

ISBN: (纸本)9781665454155

In some real-world data sets, there is a class imbalance where one class (the minority class) has a limited number of data points and the other class (the dominant class) has a large number of data points. With the state-of-the-art machine learning approaches, it is extremely challenging to build an efficient model without taking data preparation into account to balance the unbalanced data sets. To ensure that each class has the same number of data points, random under-sampling has been used in numerous research. During the data preparation phase of this study, this research experiments with under-sampling techniques that use a clustering technique. This Research uses under-sampling techniques with normalization in this non-interest-bearing imbalanced data collection with a majority and minority class. Both majority and minority classifications contain personal information focuses. This Research first uses k-fold cross-validation to separate this unbalanced data set into preparation and testing sets. This Research separates the data into a majority course subset and a minority course subset after normalizing it. The majority of lesson information evaluations are minimized by using a clustering-based under-sampling method. The minority lesson subset is then combined with the reduced lion's share course subset to create an updated preparation set. Then The author normalizes the information once more at that moment. The classifier is then independently prepared and evaluated using the updated preparation and testing sets. © 2022 IEEE.

关键词： Classification (of information)

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Leveraging federated learning and edge computing for recommendation systems within cloud computing networks 3

Leveraging federated learning and edge computing for recomme...

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3rd International Symposium on computer Applications and Information Systems, ISCAIS 2024

作者： Feng, Yuan Qi, Yaqian Li, Hanzhe Wang, Xiangxiang Tian, Jingxiao Interdisciplinary Data Science Duke University NC United States Quantitative Methods and Modeling Baruch Collegue CUNY Lexington Ave NY55 United States Computer Engineering New York University NY United States Computer Science University of Texas at Arlington ArlingtonTX United States Electrical and Computer Engineering San Diego State University SD United States

ISBN: (纸本)9781510681316

To enable large-scale and efficient deployment of artificial intelligence (AI), the combination of AI and edge computing has spawned Edge Intelligence, which leverages the computing and communication capabilities of end devices and edge servers to process data closer to where it is generated. A key technology for edge intelligence is the privacy-protecting machine learning paradigm known as Federated Learning (FL), which enables data owners to train models without having to transfer raw data to third-party servers. However, FL networks are expected to involve thousands of heterogeneous distributed devices. As a result, communication efficiency remains a key bottleneck. To reduce node failures and device exits, a Hierarchical Federated Learning (HFL) framework is proposed, where a designated cluster leader supports the data owner through intermediate model aggregation. Therefore, based on the improvement of edge server resource utilization, this paper can effectively make up for the limitation of cache capacity. In order to mitigate the impact of soft clicks on the quality of user experience (QoE), the authors model the user QoE as a comprehensive system cost. To solve the formulaic problem, the authors propose a decentralized caching algorithm with federated deep reinforcement learning (DRL) and federated learning (FL), where multiple agents learn and make decisions independently. © 2024 SPIE.

关键词： Edge computing

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