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7th International Conference on computer and Communication Systems, ICCCS 2022

作者： Wang, Hongbin Zhu, Fangyuan Ma, Zhiqiang Li, Leixiao Gao, Jing Inner Mongolia University of Technology College of Data Science and Application Hohhot China Inner Mongolia Agricultural University College of Computer and Information Engineering Hohhot China

ISBN: (数字)9781665450607

ISBN: (纸本)9781665450607

Nowadays, cloud computing technology and applications have become more and more popular. By integrating various types of inexpensive computing resources, cloud computing can form a resource pool with huge computing power, providing people with on-demand, self-service and scalable services. Effectively reduce the investment cost of IT infrastructure and improve the efficiency of enterprise IT work. Cluster server is an important part of cloud computing system. This paper aims to monitor the physical cluster servers of and provide help for the resource scheduling of the cloud platform. This paper introduces a cluster server monitoring system based on microservices. The purpose is to monitor various resource information of physical servers, change equipment information, and monitor alarms. In order to verify the performance of the proposed monitoring system, we built a clustered cloud platform is built in the data center and conducted tests. The test results verified the feasibility and performance of the system. © 2022 IEEE.

关键词： Monitoring

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A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan Khaled Sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： Distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

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Heart Disease Prediction Model using various Supervised Learning Algorithm

Heart Disease Prediction Model using various Supervised Lear...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Sachin Upadhyay Anjali Dwivedi Ashutosh Verma Vatsya Tiwari Department of Computer Engineering & Application GLA University Mathura Computer Science and Engineering Government Girls Polytechnic Amethi Computer Science & Engineering G.L. BAJAJ Group of Institutions Mathura Department of Computer Science & Applications BSA College of Engineering & Technology Mathura

A computer can learn from data and improve its predictive abilities without requiring human interaction through artificial intelligence (AI) derived from machine learning (ML). Machine learning’s core concept is to replicate how the brain functions. ML approaches have proven to be effective predictors in a variety of application fields, including healthcare and medicine. In this study, supervised machine learning approaches for predicting cardiac disorders were analyzed and compared using medical records from the UCI Machine Learning repository. This research examines the effectiveness of various models, including K-Nearest Neighbor (KNN), Logistic Regression (LR) models and Support Vector Machines (SVM), and The (Area Under ROC Curve) AUC score was used to evaluate the effectiveness of various algorithms. AUC is a measurement. In order to evaluate the effectiveness of various algorithms, we used the (Area Under ROC Curve) AUC score. The choice to adopt a machine learning algorithm is made if the AUC score is more than 0.5, which is an evaluation metric that aids in validating how effective the algorithm is. AUC scores for logistic regression are the highest of all ML algorithms in the trial (0.87).

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A Data-Driven Analytical Approach on Digital Adoption and Digital Policy for Pharmaceutical Industry in India 7th

A Data-Driven Analytical Approach on Digital Adoption and Di...

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7th International Conference on Microelectronics and Telecommunication engineering, ICMETE 2023

作者： Rana, Anup Sarkar, Bikramjit Parida, Raj Kumar Adhikari, Saurabh Anandha Lakshmi, R. Akila, D. Pal, Souvik Liverpool Business School Liverpool John Moores University Liverpool United Kingdom Department of Computer Science and Engineering JIS College of Engineering Kalyani India School of Computing Science and Engineering Galgotias University Greater Noida India School of Engineering Swami Vivekananda University Kolkata India Department of Computer Application Anna Adarsh College for Women Chennai India Department of Computer Applications Saveetha College of Liberal Arts and Sciences SIMATS Deemed University Chennai India Department of Management Information Systems Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai India Kolkata India

ISBN: (纸本)9789819995615

The Indian pharmaceutical industry has witnessed exponential growth over the past few decades to become the third largest globally in volume terms and a leading supplier of affordable generics. This paper provides a comprehensive assessment of the industry’s evolution, structure, performance metrics, priorities, challenges and future outlook. Secondary data from government reports, industry associations, company documents and academic literature is synthesized to develop a holistic perspective. The analysis indicates that innovation, research productivity, clinical trials, ethical practices and sustainability shape the industry’s development. Strategic priorities include strengthening R&D capabilities, expanding exports, leveraging digital technologies across operations, ensuring robust supply chains and adopting global quality and ESG standards. Investments in innovation have risen but remain below international benchmarks. While generics account for majority revenues, emerging segments like biosimilars and complex drugs offer growth opportunities. Digital adoption is increasing with data analytics and AI gaining prominence. However, issues related to regulations, pricing control, IP protection, skill shortages and environmental impact need resolution through appropriate reforms and public–private collaboration. India’s vision to become a leading pharmaceutical innovation and manufacturing hub would require reinforcing policy frameworks around IP protection, pricing, regulatory efficiency, skill building and technology adoption. Overall, the industry holds immense potential for beneficial growth but realizing it would necessitate concentrated efforts from stakeholders to build organizational capabilities, foster collaboration and imbibe global best practices. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Supply chains

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Deep Learning Approaches for Real-Time Data Analytics in IoT Sensor Networks 3rd

Deep Learning Approaches for Real-Time Data Analytics in IoT...

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3rd International Conference on Universal Threats in Expert applications and Solutions, UNI-TEAS 2024

作者： Mary, P. Arockia Sharma, Avinash Dekka, Satish Gowda, V. Dankan Singh, Mandeep Department of Information Technology V.S.B. Engineering College Tamilnadu Karur India Director of Computer Application Chandigarh Group of Colleges Sahibzada Ajit Singh Nagar India Department of Computer Science and Engineering Lendi Institute of Engineering and Technology Andhra Pradesh Vizianagaram India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bangalore India Department of University Institute of Computing Chandigarh University Mohalli Punjab Gharuan India

ISBN: (纸本)9789819751457

The rise of the Internet of Things (IoT) has made sensor networks ubiquitous, generating vast amounts of real-time data. However, the challenge lies not only in collecting this data but also in swiftly extracting meaningful insights. Traditional data processing methods often fall short in handling real-time IoT data streams. This research delves into deep learning algorithms as a solution for real-time analytics in IoT sensor networks. We examine suitable architectures for time-series data, such as 1D CNNs, GRUs, and LSTMs. The study also emphasizes the importance of efficient model training, rapid inference techniques, and data preprocessing. Our tests demonstrate that deep learning outshines traditional methods in accuracy and speed. Hence, when applied effectively, deep learning can offer dependable real-time analytics for IoT sensor networks, paving the way for smarter decisions in various IoT applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data Analytics

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TB Chest X-Ray Diagnostic Technique Using Deep Learning 3

TB Chest X-Ray Diagnostic Technique Using Deep Learning

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Goyal, Manish Kumar Yadav, Dhananjay Kumar Brar, Khushmeen Kaur Mittal, Manisha Ojha, Ananta Alzubaidi, Laith H. Pathani, Ashish Vivekananda Global University Dept. of Computer Science & Application Jaipur India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chandigarh University Dept. of Electronics and Communication Engineering Mohali India Guru Tegh Bahadur Institute of Technology New Delhi India Dept. of Computer Science and Information Technology Bangalore India The Islamic University Najaf Iraq Uttaranchal Institute of Technology Uttaranchal University Department of Civil Engineering Dehradun India

ISBN: (纸本)9798331518592

The bacteria Mycobacterium tuberculosis is responsible for the infectious illness tuberculosis (TB). Although the lungs are the primary organs affected, the kidneys, bones, and brain may also be affected. Whenever an infected person coughs, sneezes, or talks, small droplets comprising the bacteria are released into the air and spread through the airborne system. The elimination of tuberculosis (TB) remains a problem due to factors such medication resistance, co-infection with HIV, and scarce resources in high-burden configurations, even with substantial efforts to treat the disease. As a major worldwide health concern, tuberculosis (TB) necessitates quick and precise diagnosis in order to be effectively managed. This study aimed to develop a TB detection model using chest X-ray images obtained from ***, utilizing Google's Collaboration Platform. The model was trained to recognize patterns within the TB chest X-rays to efficiently recognize TB within patients in order to be treated in time. We introduce a technique for transfer learning that proves preferable to the traditional methodology of adopting ImageN et weights. The hybrid methodology utilizes best first search technique in fusion with ResNet 50 model. The SVM classifier is used for categorizing the classes. An accuracy of 93.91 % is attained. © 2024 IEEE.

关键词： Deep learning

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Improved Power Conversion Efficiency and Stability of Perovskite Solar Cells Induced by Dual Interface Passivation

Improved Power Conversion Efficiency and Stability of Perovs...

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2023 International Conference on Optoelectronic Materials and Devices, ICOMD 2023

作者： Guan, Nianci Han, Qizhuo Institute of Biomedical Materials & Engineering Northwestern Polytechnical University Xi'an710072 China The School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground- Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech and Image Information Processing Northwestern Polytechnical University Xi’an710072 China

ISBN: (纸本)9781510674509

Efficient and stable Perovskite solar cells (PSCs) have been successfully produced using a dual-layer modified (DLM) perovskite thin film. This method simultaneously modifies both the bottom (SnO2/perovskite) and top (perovskite/SpiroOMeTAD) interfaces of the perovskite. The crystallinity of the perovskite film is enhanced after the DLM treatment. Additionally, thin barrier layers are formed on both its bottom and top surfaces, which reduce surface defect density and non-radiative recombination of carriers. The bottom and top modifier layers prevent direct contact between the hole transport layer and the electron transport layer, forming a high-energy barrier that promotes carrier transmission in PSCs. Consequently, the methylammonium acetate (MAAc)-PSC achieves an efficiency of 21.96%. © 2024 SPIE. All rights reserved.

关键词： Perovskite solar cells

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IIESC-Net: Incorporating Implicit and Explicit Structural Constraints for Hip Joint Landmark Detection in pelvic X-ray

IIESC-Net: Incorporating Implicit and Explicit Structural Co...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhang, Jing Guo, Lijun Ni, Lixin He, Xiuchao Wang, Jianhua Zhang, Rong Ningbo University The Faculty of Electrical Engineering and Computer Science Ningbo China The Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China The Affiliated Hospital of Medicine School of Ningbo University The Department of Radiology Ningbo China

ISBN: (纸本)9798350337488

The hip joint, a vital weight-bearing joint in the human body, is susceptible to various hip-related diseases. The accurate identification of anatomical landmarks in the hip joint is essential for both disease diagnosis and surgical planning. However, these landmarks are often inconspicuous in X-ray images, where irrelevant background interference increases detection difficulty. In this study, we proposed an IIESC-Net model that combined local and global features, enabling a hierarchical understanding of the implicit structural characteristics of the hip joint. Additionally, drawing from domain expertise based on the explicit physiological structure of the hip joint, we designed a Hip Morphology-Aware loss function to constrain large landmark errors through the application of high-confidence landmarks with robust distinctive identification, thereby achieving accuracy in automatic detection. Furthermore, we constructed a dataset comprising of 843 pelvic X-ray images. The experimental results demonstrated a substantial enhancement in hip joint landmark detection accuracy attributed to the proposed IIESC-Net. This innovation established state-of-the-art performance, notably excelling in attaining heightened successful detection rates under stringent error tolerance. This achievement has profound practical implications for clinical applications. © 2023 IEEE.

关键词： Global Features Hip joint Hip Morphology-Aware Loss Landmark Detection Local Features

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Prediction of rainfall-type debris flow in Jiangjiagou based on LSTM-Attention 3

Prediction of rainfall-type debris flow in Jiangjiagou based...

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3rd International Conference on computer Vision, Image and Deep Learning and International Conference on computer engineering and applications, CVIDL and ICCEA 2022

作者： Gao, Yuan Li, Yingna Kunming University of Science and Technology College of Faculty of Information Engineering and Automation Kunming China Key Laboratory of Computer Technology Application of Yunnan Province Kunming China

ISBN: (纸本)9781665459112

Through the neural network model, the rainfall-type debris flow in Jiangjiagou is predicted. Based on seven meteorological factors with time series characteristics, such as daily rainfall, previous effective rainfall, ground temperature, and humidity, which have been screened by features, the Jiangjiagou rainfall debris flow prediction and early warning model based on LSTM-Attention is constructed. The results show that: (1) LSTM-Attention model is 0.02 higher than that of the Attention-LSTM model, and the prediction effect is better. (2) The prediction accuracy of LSTM-Attention model for Jiangjiagou rainfall-type debris flow can reach 0.88, which is 0.05, 0.07, 0.04, 0.25, 0.13 higher than that of LSTM model, RNN model, RNN-Attention model, SVM model, and BP model, respectively. Research results: It provides a new idea and method for the prediction and research of rainfall-type debris flow, which can be referenced by scholars. © 2022 IEEE.

关键词： Long short-term memory

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Deep Learning Techniques for Identification of Pneumonia: A CNN Approach

Deep Learning Techniques for Identification of Pneumonia: A ...

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Advancements in Smart, Secure and Intelligent Computing (ASSIC), International Conference on

作者： Ruchika Das Debasish Swapnesh Kumar Nayak Chinmayee Priyadarshini Rout Lambodar Jena Tripti Swarnkar Department of Computer Science and Engineering Siksha ‘O’ Anusandhan University Bhubaneswar India Department of Computer Application Nalanda Institute of Technology Bhubaneswar India Department of Data Science Siksha 'O Anusandhan University Bhubaneswar India Department of Computer Application Siksha ‘O’ Anusandhan University Bhubaneswar India

Pneumonia is an inflammatory lung disease that mostly affects the tiny air sacs known as alveoli in human bodies. Pneumonia may be caused by bacteria, viruses, or fungi. Mild to severe symptoms may include a mucus-producing cough and a fever, appals, and difficulty to breath. The approach most usually used to diagnose pneumonia is chest X-ray (CXR) imaging. Images from a CXR screening can be used as an alternative or confirmation method because they are simple and affordable to acquire. Examining the chest X-rays however is a hard task that might be vulnerable to subjective variation. We used a deep learning technique in this investigation. Convolutional neural networks (CNNs) for deep learning were employed in this paper. In his work, the proposed model is applied to the original CXR image dataset and the augmented image dataset. We generated the augmented images from the existing CXR image dataset and deployed VGG-19, a component of the CNN model to create an autonomous Pneumonia detection system. The suggested techniques that have been employed include scaling the image, adding extra information, deep learning using Keras, batch normalization, and finally using weights from the pre-trained Keras model (VGG19). Finding pneumonia sickness is difficult and takes time. In this study, we address the issue and demonstrate how to detect pneumonia disease more quickly and accurately. Our proposed model shows an accuracy of 95% on the augmented CXR image dataset.

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