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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Ranjai, Rishab Gupta, Sumita Amity School of Engineering and Technology Amity University Uttar Pradesh Department of Computer Science & Engineering U.P. Noida India

ISBN: (纸本)9798350344837

Health has a significant influence on one's wellbeing and quality of life. By assisting medical practitioners, suggesting possible therapies, and allowing early illness identification, machine learning techniques have the potential to completely transform the healthcare sector. This research assesses many machine learning approaches, such as Decision Tree classifier, Random Forest classifier, Logistic Regression, and Support Vector Machine, for identifying heart health concerns. It also offers a Convolutional Neural Network (CNN) model for brain tumor diagnosis. CNNs may detect tiny anomalies that may escape human inspection because of their expertise in processing medical pictures, such as MRI scans. The suggested 11-layer CNN model for cancer diagnosis has a 97.7% accuracy rate, a 96.9% recall rate, and a 1.36% false positive rate. Furthermore, by evaluating large amounts of patient data to pinpoint those who are at risk, machine learning algorithms also significantly contribute to the prediction of cardiac disease. © 2024 IEEE.

关键词： Logistic regression

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Book Genre Classification System Through Supervised Learning Technique 1

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3rd International Conference on Smart Computing and Cyber Security—Strategic Foresight, Security Challenges and Innovation, SMARTCYBER 2023

作者： Sethy, Abhisek Rout, Ajit Kumar Nayak, Soumya Ranjan Kumar, Raghvendra Department of Computer Science and Engineering Silicon Institute of Technology Odisha Bhubaneswar India Department of Information Technology GMR Institute of Technology Andhra Pradesh Rajam India School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar751024 India Department of Computer Science and Engineering GIET University Odisha Gunupur India

ISBN: (数字)9789819705733

ISBN: (纸本)9789819705726

Machine learning is used in this digitizing era so there is an ever-increasing desire for computers to execute human-like jobs. Text classification is rapidly becoming one of machine learning’s most significant tasks. Manually reading the entire book and classifying it based on its genre is, however, a time-consuming operation. As a result, machine learning methods are crucial for classification and in this paper, a book description-based text classification is depicted, where with wide range of wealth of information about the book’s contents. In the proposed system, a variety of classifiers including Multinomial Naive Bayes (MNB), Gradient Boosting (GB), and Random Forest (RF) are employed to categorize the book’s genre. According to the findings, the Naive Bayes Classifier surpasses other two techniques in terms of accuracy, while the other two classifiers provide comparable results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Supervised learning

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Deep Learning Image Classification Abiotic Plant Disease Detection Classification Using MobileNet 4

Deep Learning Image Classification Abiotic Plant Disease Det...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Aggarwal, Ritu Aggarwal, Eshaan Jain, Anurag Aluvalu, Rajanikanth Maheswari, Uma Choudhury, Tanupriya Department of Computer Science and Engineering India School of Computer Science and Engineering Iilm University Haryana Gurugram India Symbiosis Institute of Technology Hyderabad Campus Hyderabad India Symbiosis International University Pune India Chaitanya Bharathi Institute of Technology Department of Computer Science and Engineering Hyderabad India School of Computer Sciences University of Petroleum and Energy Studies Upes Uttarakhand Dehradun248007 India

ISBN: (纸本)9798350387490

In the field of agriculture sector the key role of feature food which has a major role in the emergent population with their economy. In the food production for plant disease, it may cause significant loss for the era of diversity in species and the crop production rate in the plant disease detection and diagnosis the accurate detecting techniques could increase the superiority in food productivity and reduce the economic loss for the image categorization and object recognition. As per computed results and analysis of the various techniques, deep learning is as best approach to get the accuracy means and classify the images. In this proposed work to tune the parameters required to train load balancing overload forecasting load balancing overload forecasting the models using CNN. The model performance is evaluated by confusion matrix parameters such as accolade balancing overload forecasting accuracy, precision, Recall, sensitivity, specificity, f-measure, etc. In the plants, the stress will destroy the crop's growth, and yields are affected negatively due to the water supply's suboptimal quantity, soil erection, and abnormal temperature due to other physiological conditions and changes in biochemicals. In this paper, the current model Mobile, which is used for image detection and classification in disease, achieves an accuracy of 99.8%. © 2024 IEEE.

关键词： Climate change Crop disease production Drought Heat stress Plant responses Stress management

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DPT‐tracker:Dual pooling transformer for efficient visual tracking

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CAAI Transactions on Intelligence technology 2024年第4期9卷 948-959页

作者： Yang Fang Bailian Xie Uswah Khairuddin Zijian Min Bingbing Jiang Weisheng Li Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and TelecommunicationsChongqingChina Department of Mechanical Precision Engineering Malaysia‐Japan International Institute of TechnologyUniversity of Technology MalaysiaKuala LumpurMalaysia Department of Electrical and Computer Engineering Inha UniversityIncheonRepublic of Korea School of Information Science and Technology Hangzhou Normal UniversityHangzhouChina

Transformer tracking always takes paired template and search images as encoder input and conduct feature extraction and target‐search feature correlation by self and/or cross attention operations,thus the model complexity will grow quadratically with the number of input *** alleviate the burden of this tracking paradigm and facilitate practical deployment of Transformer‐based trackers,we propose a dual pooling transformer tracking framework,dubbed as DPT,which consists of three components:a simple yet efficient spatiotemporal attention model(SAM),a mutual correlation pooling Trans-former(MCPT)and a multiscale aggregation pooling Transformer(MAPT).SAM is designed to gracefully aggregates temporal dynamics and spatial appearance information of multi‐frame templates along space‐time *** aims to capture multi‐scale pooled and correlated contextual features,which is followed by MAPT that aggregates multi‐scale features into a unified feature representation for tracking *** tracker achieves AUC score of 69.5 on LaSOT and precision score of 82.8 on Track-ingNet while maintaining a shorter sequence length of attention tokens,fewer parameters and FLOPs compared to existing state‐of‐the‐art(SOTA)Transformer tracking *** experiments demonstrate that DPT tracker yields a strong real‐time tracking baseline with a good trade‐off between tracking performance and inference efficiency.

关键词： human‐computer interfacing image motion analysis pattern recognition signal processing tracking

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Modeling User Quality of Experience in Adaptive Point Cloud Video Streaming 26

Modeling User Quality of Experience in Adaptive Point Cloud ...

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26th International Symposium on Multimedia, ISM 2024

作者： Nguyen, Duc V. Quang, Nguyen Long Hien, Tran Thuy Huyen, Nguyen Ngoc Huong, Truong Thu Nam, Pham Ngoc Tohoku Insitute of Technology Faculty of Engineering Sendai Japan Hanoi University of Science and Technology School of Electrical and Electronic Engineering Hanoi Viet Nam VinUniversity College of Engineering and Computer Science Hanoi Viet Nam

ISBN: (纸本)9798331511111

Point cloud video streaming over networks is challenging because of the high data rate of uncompressed point cloud data. Adaptive point cloud video streaming has been proposed to deal with this challenge. However, temporal quality variation and stalling might occur under unstable network conditions, potentially degrading users' Quality of Experience (QoE). This paper aims to evaluate and model the impacts of temporal quality variations and stalling on users' QoE in adaptive point cloud video streaming. We first conduct a large-scale subjective study to construct a QoE database. Then, based on the constructed database, the effects of individual factors are analyzed, and two novel QoE prediction models are presented. Experiment results show that the proposed QoE models achieve high prediction performance in PLCC, SROCC, and RMSE across various point cloud videos. © 2024 IEEE.

关键词： Video streaming

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Design of a Secured and Trusted E-Voting System Using Blockchain technology

Design of a Secured and Trusted E-Voting System Using Blockc...

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2024 IEEE SmartBlock4Africa, SmartBlock4Africa 2024

作者： Elegba Jeremiah, O. Agbelusi, O. Olutayo Victor, A. Akintola Kolawole, G. School of Computing The Federal University of Technology Department of Software Engineering Akure Nigeria School of Computing The Federal University of Technology Department of Computer Science Akure Nigeria

ISBN: (纸本)9798350376838

The state of election in Nigeria is worrisome. Electoral malpractices have been a major challenge to the Nigerian government in recent times. Government has made frantic efforts to tackle these electoral challenges but the recent event in year 2023 presidential election is worrisome suggests that the solution is not near. Recently, the percentage of eligible voters who vote on election days is declining. Voters turnout went up from 52.3% in 1999 - the first general election since 1993 - to 69% in 2003. But, it has been on the decline nearly ever since - 57.5% in 2007, 53.7% in 2011, 43.7% in 2015 and 34.8% in 2019. In 2023, it is 28.63%. With the way things are going, if urgent action is not taken, this could signal the end of democracy in Nigeria. This research presents the design and implementation of a trusted and secured e-voting based on blockchain technology that will increase voters confidence and trust. The system utilizes *** for the frontend and Solidity on the Polygon blockchain for the backend, ensuring transparency, tamper-resistance, and heightened security. This innovative approach addresses trust and reliability concerns in traditional voting systems, offering a modernized and trustworthy electoral process. © 2024 IEEE.

关键词： Blockchain

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Erratum:Corrigendum to"Effect of dark energy models on the energy content of charged and rotating black holes"[Chinese Phys.C,46(2022)015101]

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Chinese Physics C 2023年第12期47卷 195-196页

作者： Asia Liaqat Ibrar Hussain School of Natural Sciences National University of Sciences and TechnologyH-12IslamabadPakistan School of Electrical Engineering and Computer Science National University of Sciences and TechnologyH-12IslamabadPakistan

In Liaqat and Hussain(2022)it was proved that the effect of dark energy with w_(n)=1/3 causes a reduction in the energy content of the quintessential charged-Kerr *** result needs correction as it is observed that the contribution of dark energy first decreases the total energy of the underlying spacetime for smaller values of radial coordinate r and then increases the energy for comparatively larger values of r.

关键词： dark energy quintessential charged-Kerr black hole Energy content

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Knowledge‐based deep learning system for classifying Alzheimer's disease for multi‐task learning

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CAAI Transactions on Intelligence technology 2024年第4期9卷 805-820页

作者： Amol Dattatray Dhaygude Gaurav Kumar Ameta Ihtiram Raza Khan Pavitar Parkash Singh Renato R.Maaliw III Natrayan Lakshmaiya Mohammad Shabaz Muhammad Attique Khan Hany S.Hussein Hammam Alshazly Department of Data Science University of WashingtonSeattleWashingtonUSA Department of Computer Science&Engineering Parul Institute of TechnologyParul UniversityVadodaraGujaratIndia Department of Computer Science Jamia HamdardDelhiIndia Department of Management Lovely Professional UniversityPhagwaraIndia 5 College of Engineering Southern Luzon State UniversityLucbanQuezonPhilippines Department of Mechanical Engineering Saveetha School of EngineeringSIMATSChennaiTamil NaduIndia Model Institute of Engineering and Technology JammuJ&KIndia Department of Computer Science HITEC UniversityTaxilaPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Electrical Engineering Department College of EngineeringKing Khalid UniversityAbhaSaudi Arabia Electrical Engineering Department Aswan UniversityAswanEgypt Faculty of Computers and Information South Valley UniversityQenaEgypt

Deep learning has recently become a viable approach for classifying Alzheimer's disease(AD)in medical ***,existing models struggle to efficiently extract features from medical images and may squander additional information resources for illness *** address these issues,a deep three‐dimensional convolutional neural network incorporating multi‐task learning and attention mechanisms is *** upgraded primary C3D network is utilised to create rougher low‐level feature *** introduces a new convolution block that focuses on the structural aspects of the magnetORCID:ic resonance imaging image and another block that extracts attention weights unique to certain pixel positions in the feature map and multiplies them with the feature map ***,several fully connected layers are used to achieve multi‐task learning,generating three outputs,including the primary classification *** other two outputs employ backpropagation during training to improve the primary classification *** findings show that the authors’proposed method outperforms current approaches for classifying AD,achieving enhanced classification accuracy and other in-dicators on the Alzheimer's disease Neuroimaging Initiative *** authors demonstrate promise for future disease classification studies.

关键词： classification deep learning

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Machine Learning Using Radial Basis Function with K Means Clustering for Predicting Cardiovascular Diseases

Machine Learning Using Radial Basis Function with K Means Cl...

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International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2022

作者： Akoosh, Lamiaa Mohammed Salem Siddiqui, Farheen Naaz, Sameena Alam, M. Afshar Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India

ISBN: (纸本)9789819959730

Heart disease is a condition that affects the heart and has been responsible for most deaths globally in recent decades. Getting a proper diagnosis is an essential step in treating heart disease. The challenge is timely diagnosis and treatment of heart disease in order to save lives. Machine learning algorithms can help healthcare practitioners to predict heart problems using massive medical data. The purpose of this work is to critically discuss and evaluate machine learning algorithms for the diagnosis of several heart disease characteristics using supervised, unsupervised, and deep learning techniques. Further, a neural network model using radial basis function with K means clustering has been implemented using TensorFlow and Keras. The model was run for 10 and 100 epochs for batch sizes 8, 16, and 32 and the results have been derived in terms of loss function and accuracy. Although the model performs very well in terms of loss which is around 0.32, it gives an accuracy of close to 72% only which needs to improve. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： K-means clustering

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IoT data cleaning techniques: A survey

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Intelligent and Converged Networks 2022年第4期3卷 325-339页

作者： Xiaoou Ding Hongzhi Wang Genglong Li Haoxuan Li Yingze Li Yida Liu School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China School of Mechatronics Engineering Harbin Institute of TechnologyHarbin 150001China

Data cleaning is considered as an effective approach of improving data quality in order to help practitioners and researchers be devoted to downstream analysis and decision-making without worrying about data *** paper provides a systematic summary of the two main stages of data cleaning for Internet of Things(IoT)data with time series characteristics,including error data detection and data *** respect to error data detection techniques,it categorizes an overview of quantitative data error detection methods for detecting single-point errors,continuous errors,and multidimensional time series data errors and qualitative data error detection methods for detecting rule-violating ***,it provides a detailed description of error data repairing techniques,involving statistics-based repairing,rule-based repairing,and human-involved *** review the strengths and the limitations of the current data cleaning techniques under IoT data applications and conclude with an outlook on the future of IoT data cleaning.

关键词： Internet of Things(IoT) data quality data cleaning error detection data repairing

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