检索结果-内蒙古大学图书馆

An Innovative Bispectral Deep Learning Method for Protein Family Classification

Computers, Materials & Continua 2023年第5期75卷 3971-3991页

作者： Isam Abu-Qasmieh Amjed Al Fahoum Hiam Alquran Ala’a Zyout Biomedical Systems and Informatics Engineering Department Hijjawi Faculty for Engineering TechnologyYarmouk UniversityIrbid21163Jordan

Proteins are essential for many biological *** example,folding amino acid chains reveals their functionalities by maintaining tissue structure,physiology,and *** that quantifiable protein characteristics are vital for improving therapies and precision *** automatic inference of a protein’s properties from its amino acid sequence is called“basic structure”.Nevertheless,it remains a critical unsolved challenge in bioinformatics,although with recent technological advances and the investigation of protein sequence *** protein function from amino acid sequences is crucial in *** study considers using raw sequencing to explain biological facts using a large corpus of protein sequences and the Globin-like superfamily to generate a vector *** power of two representations was used to identify each amino acid,and a coding technique was established for each sequence ***,the encoded protein numerical sequences are transformed into an image using bispectral analysis to identify essential characteristics for discriminating between protein sequences and their families.A deep Convolutional Neural Network(CNN)classifies the resulting images and developed non-normalized and normalized encoding ***,the dataset was split 70/30 for training and ***,the dataset was utilized for 70%training,15%validation,and 15%*** suggested methods are evaluated using accuracy,precision,and *** non-normalized method had 70%accuracy,72%precision,and 71%recall.68%accuracy,67%precision,and 67%recall after ***,the normalized approach without validation had 92.4%accuracy,94.3%precision,and 91.1%*** showed 90%accuracy,91.2%precision,and 89.7%*** that both algorithms outperform the *** paper presents that bispectrum-based nonlinear analysis using deep learning models outperforms standard machine learning methods and other deep learning

关键词： Globin-like superfamily numerical encoding bispectral analysis classification model deep convolutional neural network(CNN)

来源：评论

学校读者我要写书评

暂无评论

A Novel Multi-Stage Bispectral Deep Learning Method for Protein Family Classification

引用

Computers, Materials & Continua 2023年第7期76卷 1173-1193页

作者： Amjed Al Fahoum Ala’a Zyout Hiam Alquran Isam Abu-Qasmieh Biomedical Systems and Informatics Engineering Department Hijjawi Faculty for Engineering TechnologyYarmouk UniversityIrbid21163Jordan

Complex proteins are needed for many biological *** amino acid chains reveals their properties and *** support healthy tissue structure,physiology,and *** medicine and treatments require quantitative protein identification and *** technical advances and protein sequence data exploration,bioinformatics’“basic structure”problem—the automatic deduction of a protein’s properties from its amino acid sequence—remains *** function inference from amino acid sequences is the main biological data *** study analyzes whether raw sequencing can characterize biological facts.A massive corpus of protein sequences and the Globin-like superfamily’s related protein families generate a solid vector representation.A coding technique for each sequence in each family was devised using two representations to identify each amino acid precisely.A bispectral analysis converts encoded protein numerical sequences into images for better protein sequence and family *** and validation employed 70%of the dataset,while 30%was used for *** paper examined the performance of multistage deep learning models for differentiating between sixteen protein families after encoding and representing each encoded sequence by a higher spectral representation image(Bispectrum).Cascading minimized false positive and negative cases in all *** initial stage focused on two classes(six groups and ten groups).The subsequent stages focused on the few classes almost accurately separated in the first stage and decreased the overlapping cases between families that appeared in single-stage deep learning *** single-stage technique had 64.2%+/-22.8%accuracy,63.3%+/-17.1%precision,and a 63.2%+/19.4%*** two-stage technique yielded 92.2%+/-4.9%accuracy,92.7%+/-7.0%precision,and a 92.3%+/-5.0%*** work provides balanced,reliable,and precise forecasts for all families in all measures.

关键词： Globin-like superfamily numerical encoding bispectral analysis classification model deep convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

Gamification Model for the Affective Commitment of the Technology Team in a Financial Institution 16

Gamification Model for the Affective Commitment of the Techn...

引用

16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Farias-Guich, Daniel-Antonio Rodriguez, Ciro National University Faculty of Systems Engineering and Informatics San Marcos Lima Peru

ISBN: (纸本)9798331505264

This research explores the application of a gamification model as a tool to reduce the deficit in affective commitment within an IT Squad from the Technology Tribe of a financial entity in Lima, Peru. Issues negatively impacting the team's performance were identified, such as lack of communication, interpersonal conflicts, and lack of recognition, among others. The gamification model proposed in this research was designed with the aim of increasing motivation, improving interpersonal relationships, and fostering more effective collaboration, based on theories about gamification models and organizational commitment, as well as using role-playing games as a tool to develop social skills. The results of the research showed a 25% increase in the affective commitment of the IT Squad, concluding that the application of a gamification model is a viable strategy to improve the performance of IT workers. © 2024 IEEE.

关键词： Decentralized finance

来源：评论

学校读者我要写书评

暂无评论

ELCD-NSC2: a novel early lung cancer detection and non-small cell classification framework

引用

Neural Computing and Applications 2024年第24期36卷 15149-15164页

作者： Helaly, Hadeer A. Badawy, Mahmoud El-Gendy, Eman M. Haikal, Amira Y. Electrical Engineering Department Faculty of Engineering Damietta University Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Computer Science and Informatics Taibah University Medina Saudi Arabia

The survival rate of lung cancer relies significantly on how far the disease has spread when it is detected, how it reacts to the treatment, the patient’s overall health, and other factors. Therefore, the earlier the lung cancer diagnosis, the higher the survival rate. For radiologists, recognizing malignant lung nodules from computed tomography (CT) scans is a challenging and time-consuming process. As a result, computer-aided diagnosis (CAD) systems have been suggested to alleviate these burdens. Deep-learning approaches have demonstrated remarkable results in recent years, surpassing traditional methods in different fields. Researchers are currently experimenting with several deep-learning strategies to increase the effectiveness of CAD systems in lung cancer detection with CT. This work proposes a deep-learning framework for detecting and diagnosing lung cancer. The proposed framework used recent deep-learning techniques in all its layers. The autoencoder technique structure is tuned and used in the preprocessing stage to denoise and reconstruct the medical lung cancer dataset. Besides, it depends on the transfer learning pre-trained models to make multi-classification among different lung cancer cases such as benign, adenocarcinoma, and squamous cell carcinoma. The proposed model provides high performance while recognizing and differentiating between two types of datasets, including biopsy and CT scans. The Cancer Imaging Archive and Kaggle datasets are utilized to train and test the proposed model. The empirical results show that the proposed framework performs well according to various performance metrics. According to accuracy, precision, recall, F1-score, and AUC metrics, it achieves 99.60, 99.61, 99.62, 99.70, and 99.75%, respectively. Also, it depicts 0.0028, 0.0026, and 0.0507 in mean absolute error, mean squared error, and root mean square error metrics. Furthermore, it helps physicians effectively diagnose lung cancer in its early stages and allows spe

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Unveiling the Truth: Detecting Fake News Using SVM and TF-IDF

Unveiling the Truth: Detecting Fake News Using SVM and TF-ID...

引用

2025 International Conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Rizal, Randi Faturahman, Ade Impron, Ali Darmawan, Irfan Haerani, Erna Rahmatulloh, Alam Faculty of Engineering Siliwangi University Department of Informatics Tasikmalaya Indonesia Universitas Muhammadiyah Sampit Faculty of Engineering and Agriculture Departement of Informatics Central Kalimantan Indonesia Telkom University Faculty of Industrial Engineering Department of Information Systems Bandung Indonesia Faculty of Engineering Siliwangi University Department of Information Systems Tasikmalaya Indonesia

ISBN: (纸本)9798331513320

The spread of fake news on social media platforms such as Facebook, Instagram, and X has become a significant issue in the digital era due to its negative societal and informational impacts. The rapid dissemination of unverified information often leads to misinformation and confusion, highlighting the critical need for reliable detection methods. This study aims to classify and detect fake news by optimizing the Support Vector Machine (SVM) machine learning algorithm using the Term Frequency-Inverse Document Frequency (TFIDF) technique. Two publicly available Kaggle datasets, containing labeled fake and real news, were utilized as the foundation for this research. The research results demonstrate that the SVM algorithm, when optimized with TF-IDF, achieved the highest accuracy of 99.6% compared to other commonly used machine learning algorithms such as Random Forest and Logistic Regression. This highlights the effectiveness of TF-IDF in improving the feature extraction process, enabling the model to better differentiate between fake and real news. These findings underscore the potential of SVM as a reliable tool for combating misinformation. To further enhance fake news detection, future research should explore the use of more comprehensive, multilingual, and complex datasets to better reflect real-world scenarios. Additionally, employing transformer-based approaches, such as BERT, GPT, or similar models, could provide a deeper understanding of linguistic context and semantic relationships, ultimately improving the robustness and accuracy of fake news detection systems. © 2025 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Observer-based Implementation of Discrete Gabor Transform

Periodica Polytechnica Electrical Engineering and Computer S...

引用

Periodica Polytechnica Electrical engineering and Computer Science 2025年第1期69卷 79-88页

作者： Ország, Bence Sujbert, László Department of Artificial Intelligence and Systems Engineering Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3. BudapestH-1111 Hungary

The most commonly used technique in time-frequency analysis is the short-time Fourier transform. It can be used to determine the spectral components as they change over time by computing the Fourier transform of a windowed segment of the signal. A fundamental constraint of this method is that the frequency resolution of the representation in the time-frequency domain will be linear by design. The frequency adaptive nonstationary discrete Gabor transform offers an alternative that does not have this limitation. A Luenberger observer is capable of the implementation of the short-time Fourier transform, and its numerical advantages are already established based on the work of Hostetter and Péceli. Here, we introduce the family of discrete Gabor transforms and its properties along with a constructive method to define such transforms. Furthermore, we show that they are realizable by Luenberger observers which are capable of the error-free reconstruction of the observed signal in finite steps. The dead-beat property is derived for the state variables as well which estimate the transform of the windowed signal. © 2025 Budapest University of Technology and Economics. All rights reserved.

关键词： Frequency domain analysis

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of PPG Signal Capturing Using Piezoelectric, Photosensor, and Earlobe Devices 2

Comparative Analysis of PPG Signal Capturing Using Piezoelec...

引用

2nd Jordanian International Biomedical engineering Conference, JIBEC 2024

作者： Al-Fahoum, Amjed Al Omari, Ghadeer Yarmouk University Hijjawi Faculty for Engineering Technology Biomedical Systems and Informatics Engineering Dept Irbid Jordan

ISBN: (纸本)9798331539542

This paper provides an in-depth analysis of the use of three different photoplethysmography (PPG) sensors;piezoelectric, photosensor, and earlobe device to capture PPG signals from normal subjects. This study uses bispectrum analysis to provide insight into the time characteristics, frequency characteristics, time-frequency distributions, and frequency-frequency distributions at Yarmouk University Labs. The results provide information on how well each type of sensor performs and whether it is appropriate for use in clinical and research settings. While the photosensor and earlobe device offered useful advantages in terms of setup and usability, the piezoelectric sensor impressed with its improved signal quality and analytical clarity. © 2024 IEEE.

关键词： Piezoelectricity

来源：评论

学校读者我要写书评

暂无评论

An Ensemble Approach Combining LSTM and Random Forest for Forecasting Product Stock Expenditure in Retail

An Ensemble Approach Combining LSTM and Random Forest for Fo...

引用

2024 International Conference on Information Technology systems and Innovation, ICITSI 2024

作者： Jessica Suryasari Universitas Multimedia Nusantara Faculty of Engineering and Informatics Information Systems Department Tangerang Indonesia

ISBN: (数字)9798331511470

ISBN: (纸本)9798331511470

Demand forecasting is essential for optimizing inventory management in retail, ensuring product availability while minimizing the risks of overstocking and stockouts. This study explores the application of deep learning and machine learning techniques to improve demand forecasting accuracy, focusing on the integration of Long Short-Term Memory (LSTM) networks and Random Forest models. Following the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology, the research adopts a structured approach from business understanding to model development. Ensemble methods, incorporating Stacking, Voting, and Bagging, are employed to leverage the strengths of both LSTM and Random Forest models. The results demonstrate that the Random Forest achieving the lowest MSE (74.6), MAE (6.98), and RMSE (8.63). The Bagging model excels in reducing percentage-based errors, yielding the lowest MAPE (80.99%) and SMAPE (20.58%). In contrast, the LSTM model exhibits the weakest performance across all metrics, particularly with a MAPE of 155.1%. Following the modeling phase, the Bagging model was deployed using Streamlit, enabling real-time access to predictions. These findings highlight the effectiveness of ensemble methods, particularly Bagging, in enhancing demand forecasting accuracy. Furthermore, the study underscores the potential for future research to explore hybrid approaches and incorporate additional factors to refine predictive capabilities in retail inventory management. © 2024 IEEE.

关键词： Sales

来源：评论

学校读者我要写书评

暂无评论

Hands-On Gaming: Real-Time Gesture Detection in Rock-Paper-Scissors Using Mediapipe and CNN

Hands-On Gaming: Real-Time Gesture Detection in Rock-Paper-S...

引用

2025 International Conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Darmawan, Irfan Meldiawati, Aulia Melda Rizal, Randi Rahmatulloh, Alam Haerani, Erna Gunawan, Rohmat Telkom University Department of Information Systems Faculty of Industrial Engineering Bandung Indonesia Siliwangi University Faculty of Engineering Departement of Informatics Tasikmalaya Indonesia Siliwangi University Faculty of Engineering Department of Information Systems Tasikmalaya Indonesia

ISBN: (纸本)9798331513320

Real-time hand gesture detection enables intuitive non-verbal communication between humans and devices. In gaming, using hand gestures as a control mechanism enhances user engagement and provides a more immersive experience. This research implements a real-time hand gesture detection system for the Rock-Paper-Scissors game by combining Mediapipe for feature extraction and a Convolutional Neural Network (CNN) for gesture classification. Mediapipe detects and extracts key hand landmarks in real-time, while the CNN classifies the gestures into three categories: rock, paper, and scissors. The proposed system demonstrates high performance, achieving an impressive classification accuracy of 98.12% after 500 training epochs. Furthermore, it ensures adequate detection speed for seamless real-time interaction in gameplay. Future improvements to this system include utilizing a larger and more diverse dataset to enhance the model's ability to recognize a broader range of hand gesture patterns. This approach could improve robustness across different lighting conditions, hand sizes, and variations in gesture execution. Additionally, refining the CNN model architecture could lead to further performance gains in both accuracy and speed. The findings of this research highlight the potential for hand gesture detection to enhance interactivity in gaming and beyond. Applications such as augmented reality (AR), virtual reality (VR), and other interactive systems could benefit from this technology. This study demonstrates that integrating Mediapipe and CNNs can provide an accurate, responsive, and engaging user experience, paving the way for further advancements in gesture-based interaction technologies. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

In-Depth Examination of Student Attrition in Higher Education Through Decision Tree, Random Forest, and XGBoost Algorithms 11

In-Depth Examination of Student Attrition in Higher Educatio...

引用

11th International Conference on Electrical engineering, Computer Science and informatics, EECSI 2024

作者： Aurelio, Clement Setiawan, Johan Universitas Multimedia Nusantara Faculty of Engineering and Informatics Information Systems Study Tangerang Indonesia

ISBN: (纸本)9798350355314

Customer Customer churn, a critical metric for businesses reliant on customer loyalty, involves the loss of customers from a company. This study examines churn among college and university students, leading to financial losses, decreased accreditation, and diminished reputations for educational institutions, ultimately reducing their appeal to prospective students. To identify factors contributing to student churn and classify students as likely to churn or not, this research utilizes Decision Tree, Random Forest, and XGBoost algorithms. These classification algorithms, with XGBoost and Random Forest enhancing Decision Trees through boosting and bagging techniques, respectively, are well-suited for the task. The data was sourced from the Academic Information Bureau and processed before model development. The model with the best performance will predict student churn on new, unseen data. Findings indicate varying performance metrics for each model, including accuracy, precision, recall, and F1-score. The XGBoost model excelled, achieving an accuracy of 98%, precision of 99%, recall of 96%, and an F1-score of 97%, significantly improving over the base Decision Tree model across all metrics. Notably, all models showed better performance in predicting the majority class (not churn) compared to the minority class (churn). © 2024 IEEE.

关键词： Sales

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：