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information technology (ACIT)

作者： Rahma Abu Salma Ahmad al-Qerem Abedalhakeam Iessa Mohammed Rajab Mohammad Alauthman Mohammad Jeadi Ali Elrashidi Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan University Headquarter University of Anbar Ramadi Iraq Department of Information Security University of Petra Amman Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Stroke, a leading global disability cause, affects 12 million people annually, with 6.5 million dying. Preventing at least half of strokes requires public policy action and increased awareness. Early diagnosis is crucial and enhances patients' opportunities to survive a stroke. This research looks into how well machine learning (ML) models can predict when strokes will happen and how data augmentation affects those predictions.. Five ML models (Logistic Regression, Decision Tree, K-Nearest Neighbors, XGBoost, and Random Forest) are assessed and their performance is compared using an imbalanced and augmented dataset. Data augmentation techniques, including Random Oversampling, SMOTE, and Cluster Centroids, are employed to address class imbalance. The results demonstrate that some models achieve relatively high accuracy without data augmentation (XGBoost: 0.95). However, data augmentation techniques further improve performance across all models. Random Oversampling leads to the highest Accuracy for Random Forest (0.99), while SMOTE achieves the best Precision for Logistic Regression (1.00). The results found that age, occupation type, BMI, blood glucose level, and history of cardiovascular disease are risk factors for stroke. This study highlights the effectiveness of ML models, particularly when combined with data augmentation, for stroke prediction and emphasizes the importance of specific features in identifying stroke risk.

关键词： Logistic regression Accuracy Predictive models Stroke (medical condition) Nearest neighbor methods Data augmentation Data models Public policy information technology Random forests

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BioBERT Based Efficient Clustering Framework for Biomedical Document Analysis 14th

BioBERT Based Efficient Clustering Framework for Biomedical ...

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14th International Conference on Genetic and Evolutionary Computing, ICGEC 2021

作者： Davagdorj, Khishigsuren Park, Kwang Ho Amarbayasgalan, Tsatsral Munkhdalai, Lkhagvadorj Wang, Ling Li, Meijing Ryu, Keun Ho College of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of Department of Computer Science and Technology School of Computer Science Northeast Electric Power University Jilin City China College of Information Engineering Shanghai Maritime University 1550 Haigang Avenue Pudong New Area Shanghai213 China Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City700000 Viet Nam Biomedical Engineering Institute Chiang Mai University Chiang Mai50200 Thailand Department of Computer Science College of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of

ISBN: (纸本)9789811684296

The large volumes of biomedical documents have been generating exponentially in modern applications. Document clustering methods play an important role in gathering textual content documents into a few meaningful coherent groups. However, clustering unstructured and unlabeled text is challenging to extract informative representations and find the relevant articles from the rapid growth biomedical literature. Therefore, traditional text document clustering methods often represent unsatisfactory results due to general non-contextualized vector space representations, which neglect the semantic relation between bio medical texts. Pre-trained language models have been gaining attention recently in variety of natural language processing tasks. In this paper, we propose a heavily pre-trained language representation BioBERT based clustering framework for biomedical document analysis in order to improve the clustering accuracy. In experimental architecture, we provide benchmarks of the pre-trained transformer model, statistical technique and word-embedding methods while incorporating with clustering algorithms. In order to distinguish the efficiency of the models, Fowlkes mallows score (FM), silhouette coefficient (SC), adjusted rand index (ARI), Davies-Bouldin score (DB) metrics are used. The comprehensive experimental results show that the BioBERT based K-means model achieves better clustering accuracies than other models. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Visible Light Positioning Method for Indoor Line-of-Sight Scene Based on Time Series Coding 9

Visible Light Positioning Method for Indoor Line-of-Sight Sc...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Yu, Yonghao Guo, Yongde Zhao, Dawei Fu, Kexue Tang, Yongwei Wang, Qinyuan City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong University School of Mechanical Engineering Key Laboratory of High Efficiency and Clean Mechanical Manufacture Ministry of Education Jinan China

ISBN: (纸本)9798350391367

Existing indoor visible light positioning methods, such as those based on fingerprint database algorithms, are complex to construct and consume significant resources. In response to this issue, the text proposes an end-to-end indoor visible light positioning method that samples continuous signals at a small number of random coordinate positions in indoor environments. Through a deep learning method based on attention mechanism, a nonlinear mapping between the received signal and spatial position coordinates is constructed, and the time series properties in the received signal are encoded as a feature in the data. The method is further improved by combining residual networks to improve accuracy. Through simulation experiments, it has been verified that the deep learning based localization method described in this article can achieve sub meter level accuracy on small sample datasets, and it has been proven that time series features have a significant impact on localization accuracy in indoor visible light positioning algorithms based on deep learning. © 2024 IEEE.

关键词： Time series

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Colour Image Cryptography using Chaotic Pixel Shuffling and 3D Logistic Diffusion 22

Colour Image Cryptography using Chaotic Pixel Shuffling and ...

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Proceedings of the 4th International Conference on information Management & Machine Intelligence

作者： Renjith V Ravi S.B. Goyal Sanjay Singla Electronics and Communication Engineering M.E.A Engineering College India Faculty of Information Technology City University India Department of Computer Science and Engineering UIE Chandigarh University India

ISBN: (纸本)9781450399937

All types of multimedia data, including digital images, audio, text, and video, are now easily accessible on the internet due to the phenomenal growth of internet services and wireless telecommunication systems, including mobile and computer systems, in order to achieve a specific degree of security, integrity, and secrecy as well as to prevent malicious access to the information. In contrast, for data storage and transmission, cryptographic procedures are necessary. This study presents brand-new, very effective colour image encryption and decryption algorithms that use diffusion and pixel scrambling. At first Zig-Zag permutation was used to scramble the data. Then, by concealing the statistical makeup of the pixels, the two-dimensional Lozi map is used to distort further the connection between neighbouring pixels in the original image. Furthermore, the diffusion in three colour channels is accomplished using a 3D logistic map. During the decryption process, the image pixels are again moved back to their original positions. The chaotic mapping technique encrypts images to reduce the correlation between encrypted and unencrypted versions. As described in this study, the tested and examined encryption algorithm on certain colour photos showed substantial security and authenticity to resist statistical and differential cyber attacks.

关键词： 3D Logistic Diffusion

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Weighted Mask Recurrent-Convolutional Neural Network based Plant Disease Detection using Leaf Images 7

Weighted Mask Recurrent-Convolutional Neural Network based P...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Sharmila, R. Kamalitta, R. Moorthy Singh, Devesh Pratap Chauhan, Amit Acharjee, Purnendu Bikash Dhanalakshmi Srinivasan Engineering College Department of MCA Tamilnadu Perambalur India Information Technology K.Ramakrishnan College of Engineering Tamilnadu Trichy India Electronics Engineer Production Dhaksha Unmanned System Pvt Ltd Tamilnadu Chennai India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttrakhand Dehradun India Department of Life Sciences Karnataka Bengaluru India CHRIST University Faculty Member Computer Science Maharashtra Pune India

ISBN: (纸本)9798350397253

Large losses in output, money, and quality/quantity of agricultural goods are incurred due to plant diseases. Seventy percent of India's GDP is tied to the agricultural sector, thus protecting plants from diseases is crucial. For this reason, it is important to keep an eye on plants from the moment they sprout. The usual approach for this omission is naked eye inspection, which is more time-consuming, costly, and requires significant skill. Thus, automating the method for detecting diseases is necessary to speed up this process. It is imperative that image processing methods be used in the creation of the illness detection system. Disease detection involves a number of processes, including Weighted Mask R-CNN, GLCM feature extraction, Multi-thresholding image pre-processing, and K means image segmentation classification. The weighted Mask R-CNN outperforms the standard RNN, the Mask R-CNN, and the CNN in terms of accuracy and recall in analytical trials by a significant margin. © 2023 IEEE.

关键词： Recurrent neural networks

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Hybrid Water Quality Prediction with Graph Attention and Spatio-Temporal Fusion

Hybrid Water Quality Prediction with Graph Attention and Spa...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Lin, Yongze Qiao, Junfei Bi, Jing Yuan, Haitao Gao, Han Zhou, MengChu Beijing University of Technology Faculty of Information Technology Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Chinese Academy of Environmental Planning Beijing100012 China New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Spatio-temporal prediction has a wide range of applications in many fields, e.g., air pollution, weather forecasting, and traffic forecasting. Water quality prediction is also one of spatio-temporal prediction tasks. However, it faces the following challenges: 1) Water quality in river networks has complex spatial dependencies;2) There are complex nonlinear relations in water quality time series;and 3) It is difficult to realize long-term forecasting. To address these challenges, this work proposes a spatio-temporal prediction model called a Graph Attention-based Spatio-Temporal (GAST) neural network. GAST investigates spatial and temporal dependencies of water quality time series. First, we introduce a temporal attention mechanism to capture time series dependencies, which can effectively handle nonlinear relationships in time series. Second, we adopt a spatial attention mechanism to extract spatial dependencies of river networks and fuse temporal features of spatial nodes. Third, we adopt a temporal convolution residual mechanism based on the spatio-temporal fusion, which improves the accuracy of long-term series prediction. This work adopts two real-world datasets to evaluate the proposed GAST and experiments demonstrate that GAST outperforms several state-of-the-art methods in terms of prediction accuracy. © 2022 IEEE.

关键词： Time series

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Friend or foe? Using eye-tracking technology to investigate the visual discrimination ability of giant pandas

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Current Zoology 2024年第4期70卷 430-439页

作者： Xinrui Huang Guo Li Guiquan Zhang Zixiang Li Lin Zhao Mengdie Zhu Qinghua Xiang Xuefeng Liu Mei Tian Hemin Zhang Christina D.Buesching Dingzhen Liu Ministry of Education Key Laboratory of Biodiversity Science and Ecological EngineeringDepartment of EcologyCollege of Life SciencesBeijing Normal UniversityBeijing 100875China Key Laboratory of State Forestry and Grassland Administration on Conservation Biology of Rare Animals in the Giant Panda National Park China Conservation and Research Center for the Giant PandaSichuan 623004China School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China Beijing Key Laboratoryof CaptiveWildlife Technology Beijing ZooBeijing100044China Department of Biology Irving K.Barber Faculty of ScienceThe University of British ColumbiaOkanagan CampusKelownaBCCanada

The role that visual discriminative ability plays among giant pandas in social communication and individual discrimination has received less attention than olfactory and auditory ***,we used an eye-tracker technology to investigate pupil fixation patterns for 8 captive male giant pandas Ailuropoda *** paired images(N=26)of conspecifics against:1)sympatric predators(gray wolves and tigers),and non-threatening sympatric species(golden pheasant,golden snub-nosed monkey,takin,and red panda),2)conspecifics with atypical fur colora-tion(albino and brown),and 3)zookeepers/non-zookeepers wearing either work uniform or plain *** each session,we tracked the pan-da's pupil movements and measured pupil first fixation point(FFP),fixation latency,total fixation count(TFC),and duration(TFD)of attention to each ***,pandas exhibited similar attention(FFPs and TFCs)to images of predators and non-threatening sympatric *** of golden pheasant,snub-nosed monkey,and tiger received less attention(TFD)than images of conspecifics,whereas images of takin and red panda received more attention,suggesting a greater alertness to habitat or food competitors than to potential ***'TFCs were greater for images of black-white conspecifics than for albino or brown phenotypes,implying that familiar color elicited more *** reacted differently to images of men versus *** images of women only,pandas gave more attention(TFC)to familiar combinations(uniformed zookeepers and plain-clothed non-zookeepers),consistent with the familiarity *** pandas can use visual perception to discriminate intra-specifically and inter-specifically,including details of human appearance,has applications for panda conservation and captive husbandry.

关键词： eye movement tracker giant panda images total fixation duration total pupil fixation count visual discrimination.

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Success Probability and Spectral Efficiency Estimation Using Successive Interference Cancellation for B5G Network

Success Probability and Spectral Efficiency Estimation Using...

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Computing (ICOCO), IEEE International Conference on

作者： Faizan Qamar Anand Gachhadar Syed Hussain Ali Kazmi Rosilah Hassan Center for Cyber Security Faculty of Information Science and Technology (FTSM) Universiti Kebangsaan Malaysia (UKM) UKM Bangi Selangor Malaysia Department of Electronics and Computer Engineering Institute of Engineering Tribhuvan University Kathmandu Nepal Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Selangor Malaysia

This research paper investigates the examination of Successive Interference Cancellation (SIC) implementation within ultra-dense 5G networks. The study focuses on the theoretical foundations of SIC, encompassing its system and channel models modified for Ultra-Dense Networks (UDNs). A mathematical model is formulated to assess critical network performance metrics such as Signal-to-Interference-plus-Noise Ratio (SINR), Success Probability and Spectral Efficiency after the application of SIC in the network. The effectiveness of SIC is evaluated through simulations and compared against existing interference mitigation techniques like Zero Forcing. The simulations results show that the proposed model has potential to subdue the interference-related issues, such as improved success and probability. By knowing the capabilities and limitations of SIC, network operators can make informed decisions to optimize network performance and provide enhanced user experiences. Through an exploration of SIC's principles and advantages, this study aims to shed light on its potential for effectively mitigating interference and improving the Success Probability and Spectral Efficiency of B5G networks in real-world scenarios.

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A high available and strong generalization capability data-driven-based wireless fingerprint positioning model

A high available and strong generalization capability data-d...

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Electrical engineering, Big Data and Algorithms (EEBDA), IEEE International Conference on

作者： Xiaochong Wu Minxun Hu Ganrong Dong School of Information Science and Engineering Harbin Institute of Technology Weihai Shandong China Faculty of Computer and Communication Engineering North East University of Qinhuangdao Hebei China School of Information Science and Engineering Jiaxing University Jiaxing China

The demand for indoor location-based services is far greater than supply, and many novel indoor localization technologies are emerging. Wi-Fi fingerprint-based method has drawn much attention by virtue of its user-friendliness and no extra device deployment. However, the traditional methods based on signal similarity or received-signal-strength-based (RSS-based) machine learning face their bottlenecks, such as the performance bottleneck or the generalization capability. Thus, a binary-classification model based on sample differences is proposed, which divides the original fingerprint data into positive and negative samples. Then, new relative features are proposed to replace the traditional media-access-control(MAC)-RSS features. Finally, the classification models are trained using decision tree (DT), support vector machine (SVM), and random forest (RF) algorithms, and several sets of controlled experiments are performed to evaluate the proposed method. It is noticeable that the proposed method has better performance in floor detection accuracy and point matching accuracy, and the mean error can reach 2.9m (DT), 3.09m (SVM), and 2.96 m(RF). Besides, the proposed method shows a very excellent model generalization capability, and the model trained by one building's data can still work well in another building. Those advantages have enormous potential in many large indoor localization scenes, like mall, theme park, airports, museums and exhibition halls.

关键词： Location awareness Training Support vector machines Machine learning algorithms Databases Atmospheric modeling Fingerprint recognition

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Explainable Artificial Intelligence Solution for Online Retail

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computers, Materials & Continua 2022年第6期71卷 4425-4442页

作者： Kumail Javaid Ayesha Siddiqa Syed Abbas Zilqurnain Naqvi Allah Ditta Muhammad Ahsan M.A.Khan Tariq Mahmood Muhammad Adnan Khan Department of Computer Science University of South AsiaLahore54000Pakistan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Division of Science and Technology University of EducationLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea

Artificial intelligence(AI)and machine learning(ML)help in making predictions and businesses to make key decisions that are beneficial for *** the case of the online shopping business,it’s very important to find trends in the data and get knowledge of features that helps drive the success of the *** this research,a dataset of 12,330 records of customers has been analyzedwho visited an online shoppingwebsite over a period of one *** main objective of this research is to find features that are relevant in terms of correctly predicting the purchasing decisions made by visiting customers and build ML models which could make correct predictions on unseen data in the *** permutation feature importance approach has been used to get the importance of features according to the output variable(Revenue).Five ML models i.e.,decision tree(DT),random forest(RF),extra tree(ET)classifier,Neural networks(NN),and Logistic regression(LR)have been used to make predictions on the unseen data in the *** performance of each model has been discussed in detail using performance measurement techniques such as accuracy score,precision,recall,F1 score,and ROC-AUC *** model is the bestmodel among all five chosen based on accuracy score of 90%and F1 score of 79%followed by extra tree ***,our study indicates that RF model can be used by online retailing businesses for predicting consumer buying *** research also reveals the importance of page value as a key feature for capturing online purchasing *** may give a clue to future businesses who can focus on this specific feature and can find key factors behind page value success which in turn will help the online shopping business.

关键词： Explainable artificial intelligence online retail neural network random forest regression

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