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Artificial intelligence based machine learning algorithm for prediction of cancer in female anatomy

Multimedia Tools and Applications 2025年第16期84卷 15735-15761页

作者： Ghantasala, G S Pradeep Hung, Bui Thanh Chakrabarti, Prasun R, Sathiyaraj Pellakuri, Vidyullatha Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Karnataka Bangalore India Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh city Ho Chi Minh City Viet Nam Deputy Provost ITM SLS Baroda University Gujarat Vadodara India Department of CSE GITAM School of Technology GITAM University Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur India

Breast and cervical cancers account for more than 85 percent of all cancer-related fatalities in developing nations, according to the World Cancer Research Fund. As a result, breast and cervical cancer have become one of the leading causes of mortality among women worldwide. This field is still in its infancy, with only a few studies in gynaecology and computer science looking into the detection of breast and cervical cancer. According to the researchers, medical records and early testing from individuals with breast and cervical cancer will be used in this study to determine the prognosis of those suffering from the diseases. To assess our cervical cancer predictions, we employed machine learning models such as Optimized Hybrid Ensemble Classifier (OHEC), which were trained on patient behavior and variables revealed to be associated with patient behavior. The datasets in this study have a substantial number of missing values, and the distribution of those values has been altered as a function of the missing values. OHEC classifier performance has been shown to improve when the number of features is reduced and the problem of high-class imbalance is resolved, because the accuracy, sensitivity, and specificity of the classifier, as well as the number of false positives, were used to demonstrate the success of feature selection in the suggested model's predictive analysis. This has been demonstrated through the use of numerous tests involving categorization challenges. The study underscores the critical significance of early detection and prognosis in combating breast and cervical cancers, which remain leading causes of mortality worldwide. Through the utilization of machine learning models like the OHEC, the authors have demonstrated the potential for improved predictive accuracy and clinical outcomes. The findings highlight the importance of addressing challenges such as missing data and class imbalance in enhancing the performance of predictive models for effective

关键词： Diseases

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Combination of Local Outlier Factor and Winsorization for Clustering Outlier in Medical Records 11

Combination of Local Outlier Factor and Winsorization for Cl...

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11th International Conference on Information and Communication Technology, ICoICT 2023

作者： Pinio Sinaga, Gohan Bonar Budhiarti Nababan, Erna Mawengkang, Herman Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia Universitas Sumatera Utara Department of Data Science and Artificial Intelligence Medan Indonesia Medan Indonesia

ISBN: (纸本)9798350321982

In medical records, the doctors need to stay updated about the condition of their patients, but it would make a problem for the medical records. The medical records would be inconsistent due to changes in information regarding the result of examinations and leads to data that contains noises and outliers. But in medical records, the data sometimes can be mistaken as noise and it cannot be deleted as it is, because the medical records might contain one or more important information. In this research, the medical records are taken from one of the Regional Public Hospital in North Sumatera with limited work of ethics, and efforts will be made to solve the outlier problem in medical records by clustering the outliers into normal cluster by combining the Local Outlier Factor and Winsorization technique. The dataset consists 2 medical record data which contains data on outpatients and inpatients with various types of diagnoses in the last 3 years, and the variable that will be used are Gender, Date of Birth, and the Diagnosis, but it will be reduced into 2 variables, which is Diagnosis and Age (Years Old). In the end, the results are evaluated using a Confusion Matrix to see the percentage of performance for the proposed combination between the Local Outlier Factor method and the Winsorization technique, and the results are: Precision 96.8%, Recall 99.8%, Accuracy 96.9%, F1 Score 98.3%. The results have achieved the best percentage between the K-values. © 2023 IEEE.

关键词： Diagnosis

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Provenance for Lattice QCD workflows

Provenance for Lattice QCD workflows

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32nd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Auge, Tanja Bali, Gunnar Klettke, Meike Ludäscher, Bertram Söldner, Wolfgang Weishäupl, Simon Wettig, Tilo Faculty of Computer Science and Data Science University of Regensburg Germany Department of Physics University of Regensburg Germany School of Information Sciences University of Illinois at Urbana-Champaign United States

ISBN: (纸本)9781450394161

We present a provenance model for the generic workflow of numerical Lattice Quantum Chromodynamics (QCD) calculations, which constitute an important component of particle physics research. These calculations are carried out on the largest supercomputers worldwide with data in the multi-PetaByte range being generated and analyzed. In the Lattice QCD community, a custom metadata standard (QCDml) that includes certain provenance information already exists for one part of the workflow, the so-called generation of configurations. In this paper, we follow the W3C PROV standard and formulate a provenance model that includes both the generation part and the so-called measurement part of the Lattice QCD workflow. We demonstrate the applicability of this model and show how the model can be used to answer some provenance-related research questions. However, many important provenance questions in the Lattice QCD community require extensions of this provenance model. To this end, we propose a multi-layered provenance approach that combines prospective and retrospective elements. © 2023 Owner/Author.

关键词： Supercomputers

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Classifying Psychiatric Patients Using Machine Learning 25

Classifying Psychiatric Patients Using Machine Learning

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alrjoob, Hmmam M. Alazaidah, Raed Batyha, Radwan Khafajeh, Hayel Elsoud, Esraa Abu Saeb Al-Sherideh, Ala'a Elrashidi, Ali Alqaraleh, Muhyeeddin Amer, Waleed Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Applied Science Private University Faculty of Information Technology Department of Computer Science Amman Jordan Zarqa University Faculty of Information Technology Department Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Department of Cyper Security Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Zarqa University Faculty of Information Technology Department of Software Engineering Zarqa Jordan Zarqa University Faculty on Information Technology Department of Cyber Security Zarqa Jordan

ISBN: (纸本)9798331540012

Psychological disorders are considered chronic illnesses that affect a wide range of populations. Some studies in the United States indicate that one in every eight individuals is affected by a psychological disorder. Additionally, individuals with a psychological disorder often have an impact on the nature of the lives of those around them. It is beneficial to detect the illness in its early stages to increase the likelihood of recovery. In this research, thirteen classification algorithms are used. Each algorithm has an interesting approach. It can be used to analyze several datasets where three datasets specific to Psychiatric patients were selected. The datasets have been chosen from UCI Machine Learning Repository. The datasets were re-analyzed after only 50% of the characteristics were approved after being evaluated by the several feature selection techniques. The main objective of this research is to find the best classifier and feature selection strategy in analyzing datasets of Psychiatric patients. After conducting numerous comparisons, it became evident that, the best classifier for the Autism-Adult-data dataset are Bagging and Random Forest. As for the best learning strategy, it was found to be Functions and Trees, the best feature selection was obtained using Symmetrical Uncourt Attribute Evaluation. Both IBK and KStar classifier proved to be the best classifier for the Parkinson's dataset, Lazy demonstrated to be the most effective learning strategy, Correlation Attribute Evaluation technique yielded the best feature selection. Logistic emerged as the best classifier for planning-relax dataset, the best learning strategy was Bayes, the best feature selection was found to be Symmetrical Uncert Feature Selection. © 2024 IEEE.

关键词： Contrastive Learning

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Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition

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植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

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Comparative Analysis of CNN and SVM Machine Learning Techniques for Plant Disease Detection 4th

Comparative Analysis of CNN and SVM Machine Learning Techniq...

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4th International Conference on data, Engineering, and Applications, IDEA 2022

作者： Adeniyi, Abidemi Emmanuel Madamidola, Olugbenga Ayomide Awotunde, Joseph Bamidele Misra, Sanjay Agrawal, Akshat Department of Computer Science College of Computing and Communication Studies Bowen University Iwo Nigeria Department of Information Technology Federal University of Technology Akure Nigeria Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria Department of Applied Data Science Institute for Energy Technology Halden Norway Department of Computer Science & Engineering Amity University Haryana Gurugram122412 India

ISBN: (纸本)9789819700363

The growing advancement of different jobs, skills and businesses has stimulated the spread of diseases. Most of the time, these diseases can cross countries’ borders and spread at a very rapid rate. Avoiding and detecting these infections have proved to be a major problem for our public health and one that must be treated with great importance. Crop disease has haunted, farmers have been victimized for several decades, and a remedy must be offered to halt this damage. As a result, a system should be developed to detect plant disease. Untreated plant diseases ultimately result in a loss to the farmer. Traditional methods of detecting plant disease have become less efficient and inaccurate;thus there is need to improve on the techniques of plant disease detection, as a reliable detection of plant diseases will greatly improve the quality of food crops produced and decrease the threat plant diseases pose to food security. Therefore, this study is aimed at analyzing comparatively, two machine learning language techniques convolutional neural network (CNN) and support vector machine (SVM) in terms of their respective accuracy in detecting plant diseases through their leaves in which both algorithms are used for the automatic diagnosis of plant disease. From the experimental test conducted on various plants using CNN and SVM algorithms, CNN had an accuracy of 98%, while SVM had an accuracy of 62% on various plant leaves used. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Hyperbolic Variational Graph Auto-Encoder for Next POI Recommendation 25

Hyperbolic Variational Graph Auto-Encoder for Next POI Recom...

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34th ACM Web Conference, WWW 2025

作者： Liu, Yuwen Qi, Lianyong Mao, Xingyuan Liu, Weiming Wang, Fan Xu, Xiaolong Zhang, Xuyun Dou, Wanchun Zhou, Xiaokang Beheshti, Amin Qingdao China College of Computer Science and Technology Zhejiang University Hangzhou China School of Software Nanjing University of Information Science and Technology Nanjing China School of Computing Macquarie University Sydney Australia Department of Computer Science and Technology Nanjing University Nanjing China Faculty of Business and Data Science Kansai University Osaka Japan

ISBN: (纸本)9798400712746

Next Point-of-Interest (POI) recommendation has become a crucial task in Location-Based Social Networks (LBSNs), which provide personalized recommendations by predicting the user’s next check-in locations. Commonly used models including Recurrent Neural Networks (RNNs) and Graph Convolutional Networks (GCNs) have been widely explored. However, these models face significant challenges, including the difficulty of capturing the hierarchical and tree-like structure of POIs in Euclidean space and the sparsity problem inherent in POI recommendations. To address these challenges, we propose a Hyperbolic Variational Graph Auto-Encoder (HVGAE) for next POI recommendation. Specifically, we utilize a Hyperbolic Graph Convolutional Network (Hyperbolic GCN) to model hierarchical structures and tree-like relationships by converting node embeddings from euclidean space to hyperbolic space. Then we use Variational Graph Auto-Encoder (VGAE) to convert node embeddings to probabilistic distributions, enhancing the capture of deeper latent features and providing a more robust model structure. Furthermore, we combine the Mamba4Rec recommender and Rotary Position Embedding (RoPE) and propose Rotary Position Mamba (RPMamba) to effectively utilize POI embeddings rich in sequential information, which improves the accuracy of the next POI recommendation. Extensive experiments on three public datasets demonstrate the superior performance of the HVGAE model. © 2025 Copyright held by the owner/author(s).

关键词： Recurrent neural networks

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Towards A Knowledge Graph-based Exploratory Search for Privacy Engineering 8

Towards A Knowledge Graph-based Exploratory Search for Priva...

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8th International Workshop on the Visualization and Interaction for Ontologies, Linked data and Knowledge Graphs, VOILA! 2023

作者： Herwanto, Guntur Budi Ekaputra, Fajar J. Piroi, Florina Sabou, Marta Faculty of Computer Science University of Vienna Austria Department of Computer Science and Electronics Universitas Gadjah Mada Indonesia Institute of Information Systems Engineering Faculty of Informatics TU Wien Austria Institute of Data Process and Knowledge Management WU Wien Austria

The concept of privacy-by-design has gained considerable attention in the wake of legal requirements that software systems should fulfill. The interconnected knowledge on privacy concepts, legal requirements, and software development artifacts required to implement this concept can be a major burden for organizations. To address this challenge, we propose a knowledge graph-based Exploratory Search system to help organizations complete privacy engineering tasks. We identify major requirements of such systems and develop an Exploratory Search prototype built on privacy engineering knowledge that integrates different information sources. While still preliminary, this work serves as a foundation for future research on integrating privacy knowledge into software development and demonstrates the potential of Exploratory Search Systems to support the cognitive process in privacy tasks such as privacy threat identification. © 2023 Copyright for this paper by its authors.

关键词： Knowledge graph

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A Review of Machine Learning Algorithms for Detection of Breast Cancer 4

A Review of Machine Learning Algorithms for Detection of Bre...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Tale, Abhay Barhate, Aditya Verma, Prateek Gourshettiwar, Palash Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331529635

Breast cancer is still a global health concern, chiefly affecting women, as it is one of the major causes of cancer mortality. For the therapy to be effective, early diagnosis of cancer is critical to raising the survival level and increasing the effectiveness of the procedure. The advancement in the applied technique of Machine Learning (ML) has brought further opportunities for early and effective diagnosis of breast cancer. This review paper provides insights into how different ML algorithms including the Support Vector Machine (SVM), Convolutional Neural Network (CNN), Random Forests, and many more have contributed to the detection of breast cancer. These algorithms have proved effective in providing solutions to various medical data and imaging including early diagnosis and treatment. The paper also looks at the use of ML with precision medicine to show that these technologies will help minimize false diagnoses and offer better treatment options. This review consolidates recent works and advancements to emphasize the prospect of ML in breast cancer management and calls for more studies to enhance its implementation in practice © 2024 IEEE.

关键词： Convolutional neural networks

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Attentional Sentiment and Confidence Aware Neural Recommender Model 15

Attentional Sentiment and Confidence Aware Neural Recommende...

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15th International Conference on Knowledge Discovery and Information Retrieval, KDIR 2023 as part of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2023

作者： Berkani, Lamia Ighilaza, Lina Dib, Fella Department of Artificial Intelligence & Data Sciences Faculty of Informatics USTHB University Algiers Algeria Department of Computer Science Faculty of Informatics USTHB University Algiers Algeria

ISBN: (纸本)9789897586712

One of the major problems of recommendation systems is the rating data sparseness and information overload. To address these issues, some studies are leveraging review information to construct an accurate user/item latent factor. We propose in this article a neural hybrid recommender model based on attentional hybrid sentiment analysis, using BERT word embedding and deep learning models. An attention mechanism is used to capture the most relevant information. As reviews may contain misleading information (" fake good reviews/fake bad reviews "), a confidence matrix has been used to measure the relationship between rating outliers and misleading reviews. Then, the sentiment analysis module with fake reviews detection is used to update the user-item rating matrix. Finally, a hybrid recommendation is processed by combining the generalized matrix factorization (GMF) and the multilayer perceptron (MLP). The results of experiments on two datasets from the Amazon database show that our approach significantly outperforms state-of-the-art baselines and related work. Copyright © 2023 by SCITEPRESS - science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Recommender systems

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