检索结果-内蒙古大学图书馆

19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Chauhan, Ritu Mishra, Aarushi Jena, Mehak Yafi, Eiad Zuhairi, Megat F. Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University UP India Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur50250 Malaysia

ISBN: (纸本)9798331507817

The application of machine learning has rapidly advanced as a transformative approach in healthcare, offering significant potential in the analysis of huge amounts of acquired data on Mental health across the world enhancing the treatment of mental health issues. Machine learning algorithms are being utilized to analyse patterns and trends in mental health datasets. This study investigates various machine learning methodologies and descriptive analytics techniques to detect patterns and trends in mental health-related factors, focusing on predicting the likelihood of individuals seeking mental health treatment. We utilized clustering, association rule mining, feature selection, and machine learning classifiers such as Random Forest, Decision Tree, XGBoost, and Logistic Regression. By applying these techniques to a dataset of mental health factors, we identified key predictors influencing mental health outcomes and developed models to enhance predictive accuracy. Our analysis highlighted the effectiveness of machine learning algorithms in extracting meaningful patterns from the dataset, with Random Forest and XGBoost emerging as particularly strong performers with accuracies of 81% and 84% . Other applied algorithms that are Decision Tree, Gradient Boosting and logistic regression have the accuracy of 81%, 77%, and 73% respectively. © 2025 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

FDA-approved Levophed as an alternative multitargeted therapeutic against cervical cancer transferase, cell cycle, and regulatory proteins

引用

Computers in Biology and Medicine 2025年 191卷 110163-110163页

作者： Bano, Nagmi Ahmad, Shaban Gupta, Dinesh Raza, Khalid Translational Bioinformatics Group International Centre for Genetic Engineering and Biotechnology New Delhi 110067 India Computational Intelligence and Bioinformatics Lab Department of Computer Science Jamia Millia Islamia New Delhi 110025 India

Despite the availability of Pap tests and HPV vaccines, Cervical Cancer continues to be a significant factor contributing to women's deaths. It poses severe consequences to women's health. The disease's severity lies in its potential to progress silently in its early stages, mainly detected in its advanced stage, and clinical treatment is challenging due to drug resistance. This study aims to identify multitargeted lead molecules based on the interactome of Cervical Cancer-related crucial genes, which can help develop drug-resistant therapies. We have considered 9 crucial Cervical Cancer genes, namely BUBR1, CCNB1, FEN1, MAD2, MCM10, MCM6, ITGB8, POLE, and TPX2, to perform gene network analysis and Gene Ontology enrichment studies to identify the potential hub genes and their role. Further, we performed multitarget screening using multisampling algorithms HTVS, SP, and XP to screen the protein products of the 9 genes for their binding affinity for the FDA-approved drugs library. The binding affinities of the compounds were evaluated using MM\GBSA that identified multitargeted potential inhibitor as a Levophed for Cervical Cancer, and the docking results showed a range of MM/GBSA scores, varying from −8.35 to −5.38 kcal/mol for docking, and −43.41 to −19.37 kcal/mol for MM/GBSA scoring. The protein residues that interact the most with Levophed are ALA, THR, ILE, ASN, GLY, ASP, LEU, LYS, VAL, GLN, PRO, CYS, GLU, and TYR. The pharmacokinetic properties and WaterMap computations also support the idea that the compound can potentially become a drug candidate. Furthermore, all 9 complexes were simulated for 100ns, resulting in cumulative deviation and fluctuation of © 2025 Elsevier Ltd

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

Assessing the Impact of Vulnerabilities on Confidentiality, Integrity, and Availability in Smart Systems 2

Assessing the Impact of Vulnerabilities on Confidentiality, ...

引用

2nd International Conference on Informatics, ICI 2023

作者： Minhass, Shubham Chauhan, Ritu Kaur, Harleen Amity University Amity Institute of Information Technology UP India Amity University IoT and Artificial Intelligence Lab Center for Computational Biology and Bioinformatics UP India Jamia Hamdard Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350343830

As Internet of Things (IoT) technologies proliferate and smart devices become more and more integrated into our daily lives and workplaces, it is becoming more and more important to address the complex issues of data security and privacy within smart homes and buildings. Even though the Internet of Things (IoT) has many benefits, such as remote control, real-time monitoring, and sophisticated security features, it also raises serious concerns about how sensitive data is protected. By examining the particular problems associated with protecting sensitive data in the IoT era and guaranteeing the continuous provision of dependable services within smart environments, this research paper seeks to close a significant knowledge gap. In order to do this, the research focuses on the complex data-centric features of smart ecosystems, including data processing, generation, and storage. This paper thoroughly examines the range of current IoT technologies, data management practices, and privacy challenges by using a qualitative research approach. It also offers a thorough examination of the privacy protections and security measures that are now in place in smart buildings and homes. This research focuses primarily on the interface between technology, security, and privacy of data. This work effectively fills in the significant research vacuum by clarifying the specific issues it seeks to solve. It provides useful advice and insights that can help individuals and organizations navigate the complex world of smart technologies, securely store their data, and realize the full potential of these game-changing advancements. © 2023 IEEE.

关键词： Sensitive data

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence as Automated Technology for Prediction of Breast Cancer 3rd

Artificial Intelligence as Automated Technology for Predicti...

引用

3rd International Conference on computational Electronics for Wireless Communications, ICCWC 2023

作者： Chauhan, Ritu Kaur, Harleen Choudhary, Tisya Alankar, Bhavya Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India Amity Institute of Biotechnology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819719426

Artificial intelligence techniques are utilized in cancer research and oncology which involves medical data in the detection of cancer, subtype classification, and optimization of cancer treatment. This paper discusses artificial intelligence techniques, such as K-means cluster analysis and its data interpretation based on breast cancer diagnostic dataset. This study focuses on the database of digital images of fine needle aspirate (FNA) of breast mass which shows abnormal lump, and a clustering technique was applied on this dataset. This study represents various descriptive statistics techniques of the breast cancer diagnostic dataset. Furthermore, we have also tried to show a variety of techniques of descriptive statistics like KMO and Bartlett’s test, total variance representing initial eigenvalues and extraction sum of squared loadings, scree plots, and clustering techniques like ANOVA table, initial and final cluster centers, and the distance between initial and final cluster centers of K-means cluster analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

A Role of Wearable Health Technology in Smart Cities 3rd

A Role of Wearable Health Technology in Smart Cities

引用

3rd International Conference on computational Electronics for Wireless Communications, ICCWC 2023

作者： Chauhan, Ritu Kaur, Harleen Mehta, Khushi Alankar, Bhavya Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India Amity Institute of Biotechnology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819719426

In this rapidly changing era of the world, healthcare devices are revolutionizing, which enables the continuous measurement of crucial biological markers for diagnostic purposes, physiological health monitoring, and evaluation. Hence, wearable devices have transformed into accessories, embedded clothing, body adaptors, and body implants. Additionally, the emergence of wearable healthcare devices that facilitates the diagnosis and prognosis by using tiny sensing devices and biomedical devices has greatly enhanced the efficiency as well as the quality of medical care facilities results in the tremendous advancements in semiconductor technology, biomedical technologies, and nano-sized materials over the past few decades. Meanwhile, the devices could provide real-time feedback, allowing users to analyze their health and monitor in a specific period that has been assigned to the smart wearable devices, even though the industries and manufacturers are intensely interested in interpreting the factors that influence the adoption of new smart technologies, which helps to improve the performance and viability of wearable devices to fascinate consumers. Hence, the advancements in technology are regarded as trustworthy equipment for everlasting monitoring of health that are frequently used to monitor and control a diverse of health tracking predictors in the surroundings, including monitoring health and wellness of the body. In recent years, it has been focused on the study of a wide range of parameters closer to advancements in smart technology. As a result, these smart devices are now utilized in a variety of healthcare monitoring applications, one of the most crucial aspects of data collection is the various factors based on bodily activities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

An Analytical Paradigm for Exploration of Diabetes Using Machine Learning 19

An Analytical Paradigm for Exploration of Diabetes Using Mac...

引用

19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Chauhan, Ritu Mishra, Aarushi Mani, Ruchi Jakhmola Yafi, Eiad Zuhairi, Megat F. Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University UP India Proteomics and Translational Research Lab Center for Medical Biotechnology Amity University UP India Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Malaysian Institute of Information Technology University of Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (纸本)9798331507817

Machine Learning is an approach in AI which has swiftly emerged as a transformative and powerful tool in the healthcare industry, changing the methods of diagnosing and predicting diseases. In the present situation, the application of different machine learning methods, including deep learning, ensemble techniques, and natural language processing, across various healthcare activities, such as disease prediction, medical imaging analysis, drug discovery, and tailored treatment suggestions, has offered encouraging prospects for improved diagnostics, treatment, and patient results. By utilizing a diverse range of data sources, including electronic health records, medical imaging, and wearable devices, machine learning algorithms have facilitated early detection, precise prognostication, and customized interventions. The application of machine learning algorithms into health databases is the ongoing area of research concerning future modelling predictions. The current study revolves around the utilization of descriptive analytic methods and machine learning algorithms to predict if an individual is diabetic. The effectiveness of four distinct models of machine learning, specifically Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM) and K Nearest Neighbor (KNN), is assessed and contrasted. The respective accuracies of these classifiers are noted as 89.83%, 77.16%, 84.84%, 81.16%. © 2025 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Prediction of Autism Spectrum Disorder Using AI and Machine Learning 18

Prediction of Autism Spectrum Disorder Using AI and Machine ...

引用

18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Chauhan, Ritu Mehta, Khushi Eiad, Yafi Zuhairi, Megat Farez Center for Computational Biology and Bioinformatics Amity University Artificial Intelligence and IoT lab UP India Amity Institute of Biotechnology Amity University UP India University of Technology Sydney Faculty of Engineering & Information Technology SydneyNSW Australia Malaysian Institute of Information Technology University Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (纸本)9798350331011

Knowing the likelihood for a generational shift in the scientific exploration of digital data which has extensively outgrown society with tons of data. Moreover, the scientific community has evolved and outgrown themselves with Artificial intelligence based technology to compute power and detect hidden features and information from outgrown digital databases. These advanced systems have the capability to evaluate enormous amounts of data generated with unprecedent speed, which can detect valuable insights from the data. In addition, the current study of approach focuses on the application of AI with healthcare databases to detect the hidden information from autism spectrum disorder (ASD). In this research we have various algorithmic predictions for Autism Spectrum Disorder (ASD) by combining Machine Learning with the dataset provided. The proposed approach is based on the predictive method where the data preprocessing and data evaluation models are used. The study of approach focuses on ASD with several machine Learning algorithms such as, SVM, KNN, Random Forest, Decision Tree to measure the accuracy and precision among the data and determine the hidden information and patterns. The current research scenario investigated concurrent parameters which are dependable features such as Class\ASD Traits, Sex, Jaundice scores and Q chat- 10 questions by which we have come to a conclusion which corroborated the current analysis with an accuracy Score of 74% for Random Forest, KNN: 71%, SVM: 70.6% and Decision Tree: 69.1% stating whether the individual has the Autism characteristics visible or not using various parametric analysis through data visualization and analyzing results by this approach. © 2024 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Machine Learning in Fraud Detection for Financial Sustainability in Credit Card 2nd

Machine Learning in Fraud Detection for Financial Sustainabi...

引用

2nd International Conference on Sustainable Development Using Machine Learning, Artificial intelligence, and IoT, ICSD 2024

作者： Kaur, Harleen Chauhan, Ritu Shamsi, Yezdanul Haque Alankar, Bhavya Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University Noida India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India

ISBN: (纸本)9783031717284

In today’s financial world, various technologies have been emerged and with the evolution in the technologies it has led to the increase in the fraud cases. The sustainable growth of businesses and the financial markets are severely harmed by financial statement fraud. Consequently, it is crucial and vital to build an efficient methodology to identify financial statement fraud in businesses. Usage of credit card are very frequent these days and due to loopholes in the system fraud cases in credit card is rising which causes huge financial losses. In this paper the focus is on credit card fraud detection in real world with the help of machine learning. In this case Logistic Regression, Decision tree method and SVM (support vector machine) are applied on credit card fraud detection for knowledge discovery among the data. The applied methods can detect the fraudulent transactions with highest accuracy. However, the results are predicted using evaluation metrics such as accuracy, error rate, specificity, sensitivity, and ROC curves are calculated to assess each model's performance. Hence, the predicted results suggested that logistic regression is the most accurate model for prediction of fraud datasets in financial sustainability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Decentralized finance

来源：评论

学校读者我要写书评

暂无评论

Mental Health Diagnostics Using Machine Learning: A Clustering and Predictive Modeling Approach

Mental Health Diagnostics Using Machine Learning: A Clusteri...

引用

International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Ritu Chauhan Aarushi Mishra Mehak Jena Eiad Yafi Megat F. Zuhairi Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University UP India Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

The application of machine learning has rapidly advanced as a transformative approach in healthcare, offering significant potential in the analysis of huge amounts of acquired data on Mental health across the world enhancing the treatment of mental health issues. Machine learning algorithms are being utilized to analyse patterns and trends in mental health datasets. This study investigates various machine learning methodologies and descriptive analytics techniques to detect patterns and trends in mental health-related factors, focusing on predicting the likelihood of individuals seeking mental health treatment. We utilized clustering, association rule mining, feature selection, and machine learning classifiers such as Random Forest, Decision Tree, XGBoost, and Logistic Regression. By applying these techniques to a dataset of mental health factors, we identified key predictors influencing mental health outcomes and developed models to enhance predictive accuracy. Our analysis highlighted the effectiveness of machine learning algorithms in extracting meaningful patterns from the dataset, with Random Forest and XGBoost emerging as particularly strong performers with accuracies of 81% and 84%. Other applied algorithms that are Decision Tree, Gradient Boosting and logistic regression have the accuracy of 81%, 77%, and 73% respectively.

关键词： Logistic regression Machine learning algorithms Accuracy Data analysis Mental health Predictive models Market research Decision trees Public healthcare Random forests

来源：评论

学校读者我要写书评

暂无评论

Assessing the Impact of Vulnerabilities on Confidentiality, Integrity, and Availability in Smart Systems

Assessing the Impact of Vulnerabilities on Confidentiality, ...

引用

Informatics (ICI), International Conference on

作者： Shubham Minhass Ritu Chauhan Harleen Kaur Amity Institute of Information Technology Amity University UP India Center for Computational Biology and Bioinformatics IoT and Artificial Intelligence Lab Amity University UP India Department of Computer Science and Engineering Jamia Hamdard Delhi India

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：