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Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

computers, Materials & Continua 2022年第11期73卷 4193-4210页

作者： Nagwan Abdel Samee El-Sayed M.El-Kenawy Ghada Atteia Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Noha E.El-Attar Tarek Gaber Adam Slowik Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura 35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityEgypt School of Science Engineeringand EnvironmentUniversity of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailia41522Egypt Koszalin University of Technology Poland Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the *** of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of *** analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm *** lung region was segmented from the original chest X-ray images and augmented using various transformation ***,the augmented images were fed into the VGG19 deep network for feature *** the other hand,a feature selection method is proposed to select the most significant features that can boost the classification ***,the selected features were input into an optimized neural network for *** neural network is optimized using the proposed hybrid *** experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection *** addition,a deep statistical analysis is performed to study the performance and stability of the proposed *** results confirm the effectiveness and superiority of the proposed approach.

关键词： Covid-19 feature selection dipper throated optimization particle swarm optimization deep learning

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High-quality Arabidopsis thaliana Genome Assembly with Nanopore and HiFi Long Reads

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Genomics, Proteomics & Bioinformatics 2022年第1期20卷 4-13页

作者： Bo Wang Xiaofei Yang Yanyan Jia Yu Xu Peng Jia Ningxin Dang Songbo Wang Tun Xu Xixi Zhao Shenghan Gao Quanbin Dong Kai Ye MOE Key Laboratory for Intelligent Networks&Network Security Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China School of Life Science and Technology Xi’an Jiaotong UniversityXi’an 710049China School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China Genome Institute the First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China

Arabidopsis thaliana is an important and long-established model species for plant molecular biology,genetics,epigenetics,and ***,the latest version of reference genome still contains a significant number of missing ***,we reported a high-quality and almost complete Col-0 genome assembly with two gaps(named Col-XJTU)by combining the Oxford Nanopore Technologies ultra-long reads,Pacific Biosciences high-fidelity long reads,and Hi-C *** total genome assembly size is 133,725,193 bp,introducing 14.6 Mb of novel sequences compared to the TAIR10.1 reference *** five chromosomes of the Col-XJTU assembly are highly accurate with consensus quality(QV)scores>60(ranging from 62 to 68),which are higher than those of the TAIR10.1 reference(ranging from 45 to 52).We completely resolved chromosome(Chr)3 and Chr5 in a telomere-to-telomere ***4 was completely resolved except the nucleolar organizing regions,which comprise long repetitive DNA *** Chrl centromere(CEN1),reportedly around 9 Mb in length,is particularly challenging to assemble due to the presence of tens of thousands of CEN180 satellite *** the cutting-edge sequencing data and novel computational approaches,we assembled a 3.8-Mb-long CEN1 and a 3.5-Mb-long *** also investigated the structure and epigenetics of *** clusters of CEN180 monomers were detected,and the centromere-specific histone H3-like protein(CENH3)exhibited a strong preference for CEN180 Cluster ***,we observed hypomethylation patterns in CENH3-enriched *** believe that this high-quality genome assembly,Col-XJTU,would serve as a valuable reference to better understand the global pattern of centromeric polymorphisms,as well as the genetic and epigenetic features in plants.

关键词： Centromere architecture CENH3 Bacterial artificial chromosome Telomere-to-telomere Model plant

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Multimodal depression detection using a deep feature fusion network 3

Multimodal depression detection using a deep feature fusion ...

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3rd International Conference on computer science and Communication technology, ICCSCT 2022

作者： Sun, Guangyao Zhao, Shenghui Zou, Bochao An, Yubo School of Information and Electronics Beijing Institute of Technology Beijing China School of Computer and Comunication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9781510661240

Currently, more and more people are suffering from depression with the increase of social pressure, which has become one of the most severe health issues worldwide. Therefore, timely diagonosis of depression is very important. In this paper, a deep feature fusion network is proposed for multimodal depression detection. Firstly, an unsupervised autoencoder based on transformer is applied to derive the sentence-level embedding for the frame-level audiovisual features;then a deep feature fusion network based on a cross-modal transformer is proposed to fuse the text, audio and video features. The experimental results show that the proposed method achieves superior performance compared to state-of-the-art methods on the English database DAIC-WOZ. © 2022 SPIE.

关键词： Feature extraction

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Alzheimer Disease Detection System using AlexNet a novel Eight Layered Convolutional Neural Network

Alzheimer Disease Detection System using AlexNet a novel Eig...

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2023 IEEE Global Conference on information Technologies and Communications, GCITC 2023

作者： Jenifel, M. Geetha Shyamala Devi, M. Umanandhini, D. Srm Institute of Science and Technology Data Science and Business Systems School of Computing Tamil Nadu Chengalpattu603203 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Computer Science & Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350308167

Alzheimer's disease (AD), an incurable brain ailment that is irreversible, affects thinking and memory while also shrinking the size of the mind as a whole. Alzheimer's is a neurological illness that causes severe memory loss and makes it difficult to carry out regular chores. The development of more effective treatments for AD depends on an early diagnosis of the condition. Finding Alzheimer's disease is a difficult and time-consuming task that requires a brain imaging report and competent people. This time-consuming and frequently inaccurate method of diagnosing Alzheimer's is traditional. There has been discussion of a different strategy that is quick, inexpensive, and more dependable. Better medical care and solutions can be provided with the use of artificial intelligence technologies. Due to distractions, technological flaws, cognitive biases, and weariness, human diagnosis performance suffers. However, AI-based diagnosis tools support physicians' safe detection and decision-making and are less prone to error. This study offers a clever and trustworthy method for identifying Alzheimer's disease and its potential early stage, moderate mental impairment. The given system accurately diagnoses Alzheimer's disease and its early stages from structural MRI images and is based on deep learning. The ability to predict whether patients with moderate cognitive impairment (MCI) may develop Alzheimer's disease is essential in clinical practice and has the potential to significantly improve clinical studies. To classify Alzheimer's disease and its prodromal stages, this initiative suggests combining MRI data with the Mini-Mental State Examination (MMSE), a neuropsychological test. © 2023 IEEE.

关键词： AlexNet Alzheimer Disease Convolutional Neural Network Mild Cognitive Impairment Mini-Mental State Examination

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Design and Optimization of Feedback Controllers for Motion Control in the Manufacturing System for Digital Twin

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SN computer science 2023年第6期4卷 782页

作者： Ramesh, P.S. Vanteru, Madhu Kumar Rajinikanth, E. Ramesh, J.V.N. Bhasker, B. Begum, A. Yasmine Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of ECE Balaji Institute of Technology and Science Narsampet Telangana Warangal India Department of ECE Sathyabama Institute of Science and Technology Jeppiaar Nagar Sholinganallur Tamil Nadu Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur District India Department of Information Technology MLR Institute of Technology Hyderabad India Department of Electronics and Communication Engineering School of Engineering Mohan Babu University Andhrapradesh Tirupati India

If the classic fatal development approach's conventional distribution procedure reveals structural flaws, digital twin (DT) can enable hardware-in-the-loop modeling of individual actual devices and cyber models that may be exploited to prevent the significant expense of production method transformation. A quad-play CMCO (configuration, motion, control, and optimization) concept structure is proposed for the development of the flow-type smart manufacturing system in the Industry 4.0 environment, which is dependent on the DT concept. The CMCO architecture system repetition mechanism is explained. The comprehensive embedding of the quad-play CMCO paradigm and the DT approach are two significant design approaches for permitting the customizable and development tools construction of flow category smart manufacturing systems. Depending on the CMCO concept, a version of the DT (digital twin) system-developed proposed methods are demonstrated. A literature review of the glass industrial sector is used to support the validity of the development cost that relies on DTs. The outcome demonstrates the viability and efficacy of the DT system-based design scheme. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Design approach Digital twin Motion control Optimization Smart manufacturing system

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Maximum Bandwidth Allocation in Underwater Fibre Optic Networks 1

Maximum Bandwidth Allocation in Underwater Fibre Optic Netwo...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Meshram, Vishal A. Yadav, Dhananjay Vinitha, A. Murari, Krishn Kalra, Hitesh Srisathirapathy, S. Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttarpradesh India Department of Computer Science and Information Technology Karnataka Bangalore India Vivekananda Global University Department of Civil Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798350359756

This paper offers an overview of the most Bandwidth Allocation hassle in underwater fiber optic networks. The bandwidth in optical fiber networks is traditionally allotted according to the users' needs for the guaranteed satisfaction of the carrier. However, because of the specific characteristics of underwater optical fibred networks and the constrained capability of the hyperlink, it is miles necessary to layout exceptional bandwidth allocation algorithms for allocating frequencies reusing the subsea optical fiber network efficiently and looking forward to the wishes of a couple of users. While traditional techniques treat all users equally, the maximum Bandwidth Allocation algorithm does not forget traits like a priority, call for size, occupancy element, and others to allocate the available bandwidth for this reason. These paper opinions the distinctive methods of the most Bandwidth Allocation trouble and their application for Underwater Optical Fibre Networks. Furthermore, it offers numerous scenarios to acquire the premier wide variety of slots for allocating customers based on their priority and demand size. The paper also presents specific techniques for allocating and adapting the to-be-had network slots to meet customers' changing needs. This paper demonstrates the promising performance of the most Bandwidth Allocation set of rules compared to conventional algorithms, showing the desire to customize those procedures for precise packages, including underwater optical fibre networks. © 2023 IEEE.

关键词： Bandwidth

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MSKI-Net: Towards modality-specific knowledge interaction for glioma survival prediction

MSKI-Net: Towards modality-specific knowledge interaction fo...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Su, Ran Xiao, Jin Cui, Hui Xuan, Ping Feng, Xikang Wei, Leyi Jin, Qiangguo Tianjin University School of Computer Software College of Intelligence and Computing Tianjin China La Trobe University Department of Computer Science and Information Technology Melbourne Australia Shantou University School of Engineering Department of Computer Science China Northwestern Polytechnical University School of Software China Macao Polytechnic University AIDD Faculty of Applied Science China Xiamen University School of Information Xiamen China Yangtze River Delta Research Institute Northwestern Polytechnical University Taicang China

ISBN: (纸本)9798350386226

Gliomas hold a prominent position in neurooncology due to their high malignancy and poor survival rates. Accurately predicting the prognosis and survival risk of glioma patients is crucial for clinical treatment. Recent advances in survival prediction methods emphasize the importance of integrating complementary information from diverse modalities while neglecting the significant modality gap between pathological images and genomic data. To address this issue, we propose a modality-specific knowledge interaction network (MSKI-Net), which integrates whole slide images (WSI), RNA-Seq gene expression data, and copy number variation (CNV) data for glioma survival analysis. The MSKI-Net consists of a modality-specific feature enhancement (MSFE) module, a modality-interactive cross-attention (MICA) module, and a modality-specific knowledge-guided representation learning (MSKR) module. The three modules collaborate by complementing modality-specific features with modality-agnostic knowledge to improve the learning capability of MSKI-Net. Furthermore, we construct a dataset named TCGAmm, which combines WSI, RNA-Seq, and CNV data from The Cancer Genome Atlas (TCGA) to address the issue of data scarcity. Extensive experiments demonstrate that MSKI-Net achieves superior performance in predicting the survival risk of glioma cancer. © 2024 IEEE.

关键词： Gene expression

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Exploring the Benefits of Leveraging AI for Enhancing Big Data Retrieval Performance 1

Exploring the Benefits of Leveraging AI for Enhancing Big Da...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Sharma, Neeraj Kaur, Ashmeet Murali, M.J. Sakhare, Sachin R. Singh, Vaishali Haroon, Syed Vivekananda Global University Department of Electrical Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Department of Computer Science and Information Technology Karnataka Bangalore India

ISBN: (纸本)9798350359756

AI algorithms were used to improve massive information retrieval overall performance as they lessen the quantity of manual work involved in data processing and retrieval. These algorithms use numerous device studying (ML) strategies along with supervised and unsupervised learning, as well as deep gaining knowledge of, to procedure, analyze, and interpret big datasets. Device learning (ML) models can be used to manner the records and choose out the maximum essential functions, that are then used to create predictive models to improve large facts retrieval performance. The predictive fashions can discover styles and anomalies within the facts that may be used to form better seek queries and enhance results. This allows lessen the time taken for statistics retrieval and might cause better accuracy and performance. additionally, AI algorithms may be used to lessen storage necessities due to the fact they can organization redundant statistics, thereby enhancing seek *** fashions can also be used to become aware of styles which might be predictive but which may not be obvious from a records set. This may similarly improve search overall performance, as the styles may be used to create tailored seek requests and tailor search results in order that they may be more relevant to the user. © 2023 IEEE.

关键词： Big data

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On Finite Word Length Computing Error of Fixed-Point SAR Imaging Processing*

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Chinese Journal of Electronics 2025年第3期23卷 645-648页

作者： Yizhuang Xie Bocheng Zhu Wen Yan Weijie Zhang School of Information and Electronics Beijing Institute of Technology Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China Science and Technology on Electronic Information Control Laboratory Chengdu China

Synthetic aperture radar (SAR) imaging system is generally realized by fixed-point with the purpose of reducing system implementation scale and enhancing real-time performance. Finite word length computing error of fixed-point SAR imaging system is studied. The characteristic of computing error in SAR imaging system is analyzed. A finite word length computing error model of SAR imaging system is built, by means of which the empirical formula of system's output noise-to-signal ratio is derived. Based on the empirical formula, SAR imaging system processing word length is presented according to different processing granularities. The validity of this proposed finite word length computing error model of SAR imaging system is verified by system level fixed-point simulation.

关键词： Computational modeling Imaging Transforms Radar polarimetry Real-time systems Synthetic aperture radar System implementation

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Investigating the Benefits of Text Mining for information Analysis

Investigating the Benefits of Text Mining for Information An...

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2023 IEEE International Conference on Paradigm Shift in information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Singh, Vaishali Venkadeshwaran, K. Jha, Gaurav Arunadevi, R. Sharma, Yogesh K. Maheswaran, S. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka 562112 India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Kongu Engineering College Department of Electronics and Communication Engineering Tamil Nadu Erode India

ISBN: (纸本)9798350329773

Textual content mining is the manner of extracting significant information and insights from large volumes of unstructured textual information. Many businesses are beginning to recognize the capacity of text mining in gaining effective insights and presenting greater accurate statistics. This paper will talk the ability blessings of using text mining strategies in data analysis. It's going to present quite number possible applications, such as herbal language processing, sentiment evaluation, subject matter modeling, and predictive analytics. It also evaluation the numerous technical techniques and algorithms used for textual content mining, together with lateens semantic indexing, and will look at how text mining can be used to guide a range of various commercial enterprise targets. Sooner or later, the paper will address how text mining can help pick out useful styles and traits in unstructured information, and the way it may be utilized in tandem with conventional data processing methods to achieve higher effects. This paper will finish by way of analyzing the capacity challenges that may be encountered when using text mining and how ability dangers can be mitigated. © 2023 IEEE.

关键词： Semantics

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