Log in

Relevant bibliographies by topics / Deep learning as well as machine learning. Type of XAI method / Journal articles

To see the other types of publications on this topic, follow the link: Deep learning as well as machine learning. Type of XAI method.

Journal articles on the topic 'Deep learning as well as machine learning. Type of XAI method'

Author: Grafiati

Published: 7 June 2025

Last updated: 17 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Deep learning as well as machine learning. Type of XAI method.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Ashwini, Pujari* Manasi Rajput Shruti Patil. "A Overview Of Current Approaches And Applications For Explainable AI In Alzheimer's Detection." International Journal of Pharmaceutical Sciences 3, no. 1 (2025): 2527–47. https://doi.org/10.5281/zenodo.14774029.

Full text

Abstract:

A common kind of dementia, Alzheimer’s disease (AD), is characterized via decline in memory and mental decline. Alzheimer’s The, a progressive neurological disease, which poses difficulties in early diagnosis, which is essential to an effective intervention. Traditional AI methods like deep learning and machine learning have demonstrated encouraging outcomes in identifying AD biomarkers and predicting disease progression. However, their “black-box” nature restricts their ability to utility. This review emphasizes how crucial accessibility is to Models of AI, allowing cl

APA, Harvard, Vancouver, ISO, and other styles

2

Choi, Hoseok, Seokbeen Lim, Kyeongran Min, Kyoung-ha Ahn, Kyoung-Min Lee, and Dong Pyo Jang. "Non–human primate epidural ECoG analysis using explainable deep learning technology." Journal of Neural Engineering 18, no. 6 (2021): 066022. http://dx.doi.org/10.1088/1741-2552/ac3314.

Full text

Abstract:

Abstract Objective. With the development in the field of neural networks, explainable AI (XAI), is being studied to ensure that artificial intelligence models can be explained. There are some attempts to apply neural networks to neuroscientific studies to explain neurophysiological information with high machine learning performances. However, most of those studies have simply visualized features extracted from XAI and seem to lack an active neuroscientific interpretation of those features. In this study, we have tried to actively explain the high-dimensional learning features contained in the

APA, Harvard, Vancouver, ISO, and other styles

3

Saxena, Ms Kavita, Rishabh Sharma, Rishav Kumar, and Roshan Kumar. "Disease Prediction Using Machine Learning and Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 2655–63. http://dx.doi.org/10.22214/ijraset.2022.42871.

Full text

Abstract:

Abstract: Health being the state of complete physical and mental wellbeing is an imperative part of humankind .Healthcare sector been a capital incentive sector having complicated entry barrier for investors like acquiring land for making hospital, stamp duties on it, human resource crunch which further act as roadblock for the government in providing universal good healthcare services to its citizenry . In this regard artificial intelligence is leading to disruption in the healthcare sector which is helping poor in safeguarding them from been exploited by extravagant out of pocket expenditure

APA, Harvard, Vancouver, ISO, and other styles

4

Bhagwandas, Kamra Komal. "Machine Learning and Deep Learning applications-a vision using the SPSS Method." 3 1, no. 3 (2020): 16–24. http://dx.doi.org/10.46632/rmc/1/3/3.

Full text

Abstract:

AI can be categorised as either machine learning or deep learning. Machine learning, in essence, is AI that can adjust automatically with little human involvement. Artificial neural networks are used in deep learning, a subclass of machine learning, to simulate the educational process of the human brain. Deep learning is more effective with vast amounts of data than other methods. Traditional machine learning methods, however, do better with smaller amounts of data. In order to train deep learning techniques in a timely manner, a highquality infrastructure is needed. The lengthy training proce

APA, Harvard, Vancouver, ISO, and other styles

5

Ali, Syed Saqib, Mazhar Ali, Dost Muhammad Saqib Bhatti, and Bong Jun Choi. "Explainable Clustered Federated Learning for Solar Energy Forecasting." Energies 18, no. 9 (2025): 2380. https://doi.org/10.3390/en18092380.

Full text

Abstract:

Explainable Artificial Intelligence (XAI) is a well-established and dynamic field defined by an active research community that has developed numerous effective methods for explaining and interpreting the predictions of advanced machine learning models, including deep neural networks. Clustered Federated Learning (CFL) mitigates the difficulties posed by heterogeneous clients in traditional federated learning by categorizing related clients according to data characteristics, facilitating more tailored model updates, and improving overall learning efficiency. This paper introduces Explainable Cl

APA, Harvard, Vancouver, ISO, and other styles

6

Mohanthi Kakarla and Dr. K. Padma Raju. "Review of Machine learning: Views, Architectures or Techniques, Challenges and Future guidance and Real-world applications." International Research Journal on Advanced Engineering and Management (IRJAEM) 2, no. 03 (2024): 545–51. http://dx.doi.org/10.47392/irjaem.2024.0075.

Full text

Abstract:

In this digital world, the data is wealth, this data is analyzed, developed and applied to specific applications using some well-developed algorithms known as Machine learning (ML). Machine learning algorithms are supervised, unsupervised, semi-supervised and reinforcement types. Deep learning (DL) is also a method of analyzing the data on a large scale. Deep learning is a further subdivision of The subsection of ML is deep learning and measures a particular type of learning that involves the use of artificial neural networks (ANN). This paper provides group of different machine learning termi

APA, Harvard, Vancouver, ISO, and other styles

7

Lo, Shaw-Hwa, and Yiqiao Yin. "An Interaction-Based Convolutional Neural Network (ICNN) Toward a Better Understanding of COVID-19 X-ray Images." Algorithms 14, no. 11 (2021): 337. http://dx.doi.org/10.3390/a14110337.

Full text

Abstract:

The field of explainable artificial intelligence (XAI) aims to build explainable and interpretable machine learning (or deep learning) methods without sacrificing prediction performance. Convolutional neural networks (CNNs) have been successful in making predictions, especially in image classification. These popular and well-documented successes use extremely deep CNNs such as VGG16, DenseNet121, and Xception. However, these well-known deep learning models use tens of millions of parameters based on a large number of pretrained filters that have been repurposed from previous data sets. Among t

APA, Harvard, Vancouver, ISO, and other styles

8

Vilone, Giulia, and Luca Longo. "Classification of Explainable Artificial Intelligence Methods through Their Output Formats." Machine Learning and Knowledge Extraction 3, no. 3 (2021): 615–61. http://dx.doi.org/10.3390/make3030032.

Full text

Abstract:

Machine and deep learning have proven their utility to generate data-driven models with high accuracy and precision. However, their non-linear, complex structures are often difficult to interpret. Consequently, many scholars have developed a plethora of methods to explain their functioning and the logic of their inferences. This systematic review aimed to organise these methods into a hierarchical classification system that builds upon and extends existing taxonomies by adding a significant dimension—the output formats. The reviewed scientific papers were retrieved by conducting an initial sea

APA, Harvard, Vancouver, ISO, and other styles

9

Norinder, Ulf. "Traditional Machine and Deep Learning for Predicting Toxicity Endpoints." Molecules 28, no. 1 (2022): 217. http://dx.doi.org/10.3390/molecules28010217.

Full text

Abstract:

Molecular structure property modeling is an increasingly important tool for predicting compounds with desired properties due to the expensive and resource-intensive nature and the problem of toxicity-related attrition in late phases during drug discovery and development. Lately, the interest for applying deep learning techniques has increased considerably. This investigation compares the traditional physico-chemical descriptor and machine learning-based approaches through autoencoder generated descriptors to two different descriptor-free, Simplified Molecular Input Line Entry System (SMILES) b

APA, Harvard, Vancouver, ISO, and other styles

10

Chaudhari, Palash. "Skin Cancer Classification Application Using Machine Learning." International Journal of Data Science 2, no. 1 (2021): 47–55. http://dx.doi.org/10.18517/ijods.2.1.47-55.2021.

Full text

Abstract:

Melanoma is one of the predominant types of skin cancer. The affected number has been increasing year after year. Although the deaths can be minimized by early detection and there is where the problem exists and consulting a dermatologist may not always guarantee the success of early detection and diagnoses. At first, the dermatologist examines the skin visually and decides whether it’s a type of skin cancer or a skin allergy. The accuracy of the diagnosis directly corresponds to the experience of the dermatologist. Even a small error in the inspection of the skin might end a life of a person

APA, Harvard, Vancouver, ISO, and other styles

11

Chen, Chen. "Research on Intelligent Bodybuilding System Based on Machine Learning." Journal of Sensors 2022 (May 5, 2022): 1–8. http://dx.doi.org/10.1155/2022/6293856.

Full text

Abstract:

In recent years, people’s health is facing many challenges as their workload is increasing and their lives are becoming more and more stressful. In this context, healthy living has become a topic of concern and more and more people are choosing to promote their bodies through fitness. To address these existing problems in action recognition research, this paper designs and implements a machine learning-based intelligent fitness system to monitor three important parameters in physical activity: the type of action, the number of actions, and the period of action. Through the action recognition a

APA, Harvard, Vancouver, ISO, and other styles

12

Muhammad Awais, Muhammad Bilal Qureshi, Naila Hamid, and Asad Hussain Shah. "Segmentation For Object-Based Image Analysis (OBIA) Using Tensorflow Framework." Annual Methodological Archive Research Review 3, no. 2 (2025): 54–71. https://doi.org/10.63075/s4gfe370.

Full text

Abstract:

Deep learning is the ultimate breakthrough of artificial intelligence and it will change the world dramatically in this century. Various type of deep neural networks has been used to resolve challenging computer vision problems such as detection, localization, recognition and segmentation of objects in the wild. Semantic segmentation to separate a portrait from the video background. Semantical Segmentation. This process essentially closes the image bits based on an object class to vacuum them together. In this paper we differentiate four separate deep learning models that we trained to provide

APA, Harvard, Vancouver, ISO, and other styles

13

Kim, Taewan, and Seungchul Lee. "Deep Learning-based Health Indicator for Better Bearing RUL Prediction." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 263, no. 6 (2021): 493–98. http://dx.doi.org/10.3397/in-2021-1492.

Full text

Abstract:

The prognostic performance of data-driven approaches closely depends on the features extracted from the measurement. For a high level of prognostic performance, features must be carefully designed to represent the machine's health state well and are generally obtained by signal processing techniques. These features are themselves used as health indicators (HI) or used to construct HIs. However, many conventional HIs are heavily relying on the type of machine components and expert domain knowledge. To solve these drawbacks, we propose a fully data-driven method, that is, the adversarial autoenc

APA, Harvard, Vancouver, ISO, and other styles

14

Oku, K., B. Caffo, Y. Arinuma, and K. Yamaoka. "AB1848 ARTIFICIAL INTELLIGENCE IN THE CLINICAL SETTING OF THE RHEUMATIC DISEASES; A SYSTEMATIC REVIEW OF THE LITERATURE." Annals of the Rheumatic Diseases 82, Suppl 1 (2023): 2152.1–2152. http://dx.doi.org/10.1136/annrheumdis-2023-eular.1037.

Full text

Abstract:

BackgroundAutoimmune Rheumatic Diseases (AIRD) are chronic multifactorial diseases that present with a variety of pathologic findings due to immune abnormalities. The pathogenesis is a complex interplay of genetic and environmental factors, and there are usually large individual differences in phenotype, imaging and blood findings, treatment response, and prognosis. As a result, clear algorithms for diagnosis and treatment are scarce, and clinicians must interpret complex disease images from a variety of data to make analogies between diagnosis, prognosis prediction, and treatment effects. Art

APA, Harvard, Vancouver, ISO, and other styles

15

Soares, Hélcio de Abreu, Raimundo Santos Moura, Vinícius Ponte Machado, Anselmo Paiva, Weslley Lima, and Rodrigo Veras. "The Detection of Spurious Correlations in Public Bidding and Contract Descriptions Using Explainable Artificial Intelligence and Unsupervised Learning." Electronics 14, no. 7 (2025): 1251. https://doi.org/10.3390/electronics14071251.

Full text

Abstract:

Artificial Intelligence (AI) models, including deep learning and rule-based approaches, often function as black boxes, limiting transparency and increasing uncertainty in decisions. This study addresses spurious correlations, defined as associations between patterns and classes that do not reflect causal relationships, affecting AI models’ reliability and applicability. In Natural Language Processing (NLP), these correlations lead to inaccurate predictions, biases, and challenges in model generalization. We propose a method that employs Explainable Artificial Intelligence (XAI) techniques to d

APA, Harvard, Vancouver, ISO, and other styles

16

Jun, Wang, Chu Huiqin, Rashid Abbasi, Muhammad Shahid Iqbal, and Md Bilel Bin Heyat. "Transforming physical healthcare training through integration of machine learning and advanced artificial intelligent methods." Journal of Human Sport and Exercise 20, no. 4 (2025): 1133–50. https://doi.org/10.55860/5v5d5p73.

Full text

Abstract:

Mental healthcare and heart disease continues to be a major cause of death worldwide, making it essential to find effective ways to prevent it. Physical activity has long been known to be important for preventing and treating mental healthcare disease, but it is not always clear how much and what type of activity an individual should do. Artificial intelligence (AI) models that can predict a person's risk of mental healthcare disease based on their individual characteristics have become increasingly popular in recent years. In this study, we used AI models to explore the relationship between p

APA, Harvard, Vancouver, ISO, and other styles

17

Zeng, Shan, Ali Omar, Mark Vaughan, et al. "Identifying Aerosol Subtypes from CALIPSO Lidar Profiles Using Deep Machine Learning." Atmosphere 12, no. 1 (2020): 10. http://dx.doi.org/10.3390/atmos12010010.

Full text

Abstract:

The Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP), on-board the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) platform, is an elastic backscatter lidar that has been providing vertical profiles of the spatial, optical, and microphysical properties of clouds and aerosols since June 2006. Distinguishing between feature types (i.e., clouds vs. aerosol) and subtypes (e.g., ice clouds vs. water clouds and dust aerosols from smoke) in the CALIOP measurements is currently accomplished using layer-integrated measurements acquired by co-polarized (parallel) an

APA, Harvard, Vancouver, ISO, and other styles

18

Li, Ming, and Yueguan Yan. "Comparative Analysis of Machine-Learning Models for Soil Moisture Estimation Using High-Resolution Remote-Sensing Data." Land 13, no. 8 (2024): 1331. http://dx.doi.org/10.3390/land13081331.

Full text

Abstract:

Soil moisture is an important component of the hydrologic cycle and ecosystem functioning, and it has a significant impact on agricultural production, climate change and natural disasters. Despite the availability of machine-learning techniques for estimating soil moisture from high-resolution remote-sensing imagery, including synthetic aperture radar (SAR) data and optical remote sensing, comprehensive comparative studies of these techniques remain limited. This paper addresses this gap by systematically comparing the performance of four tree-based ensemble-learning models (random forest (RF)

APA, Harvard, Vancouver, ISO, and other styles

19

Curia, Francesco. "Cervical cancer risk prediction with robust ensemble and explainable black boxes method." Health and Technology 11, no. 4 (2021): 875–85. http://dx.doi.org/10.1007/s12553-021-00554-6.

Full text

Abstract:

AbstractClinical decision support systems (CDSS) that make use of algorithms based on intelligent systems, such as machine learning or deep learning, they suffer from the fact that often the methods used are hard to interpret and difficult to understand on how some decisions are made; the opacity of some methods, sometimes voluntary due to problems such as data privacy or the techniques used to protect intellectual property, makes these systems very complicated. Besides this series of problems, the results obtained also suffer from the poor possibility of being interpreted; in the clinical con

APA, Harvard, Vancouver, ISO, and other styles

20

Alsaade, Fawaz Waselallah, and Mosleh Hmoud Al-Adhaileh. "Cyber Attack Detection for Self-Driving Vehicle Networks Using Deep Autoencoder Algorithms." Sensors 23, no. 8 (2023): 4086. http://dx.doi.org/10.3390/s23084086.

Full text

Abstract:

Connected and autonomous vehicles (CAVs) present exciting opportunities for the improvement of both the mobility of people and the efficiency of transportation systems. The small computers in autonomous vehicles (CAVs) are referred to as electronic control units (ECUs) and are often perceived as being a component of a broader cyber–physical system. Subsystems of ECUs are often networked together via a variety of in-vehicle networks (IVNs) so that data may be exchanged, and the vehicle can operate more efficiently. The purpose of this work is to explore the use of machine learning and deep lear

APA, Harvard, Vancouver, ISO, and other styles

21

Pratiwi, Renny Amalia, Siti Nurmaini, Dian Palupi Rini, Muhammad Naufal Rachmatullah, and Annisa Darmawahyuni. "Deep ensemble learning for skin lesions classification with convolutional neural network." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 3 (2021): 563. http://dx.doi.org/10.11591/ijai.v10.i3.pp563-570.

Full text

Abstract:

<span lang="EN-US">One type of skin cancer that is considered a malignant tumor is melanoma. Such a dangerous disease can cause a lot of death in the world. The early detection of skin lesions becomes an important task in the diagnosis of skin cancer. Recently, a machine learning paradigm emerged known as deep learning (DL) utilized for skin lesions classification. However, in some previous studies by using seven class images diagnostic of skin lesions classification based on a single DL approach with CNNs architecture does not produce a satisfying performance. The DL approach allows the

APA, Harvard, Vancouver, ISO, and other styles

22

Renny, Amalia Pratiwi, Nurmaini Siti, Palupi Rini Dian, Naufal Rachmatullah Muhammad, and Darmawahyuni Annisa. "Deep ensemble learning for skin lesions classification with convolutional neural network." International Journal of Artificial Intelligence (IJ-AI) 10, no. 3 (2021): 563–70. https://doi.org/10.11591/ijai.v10.i3.pp563-570.

Full text

Abstract:

One type of skin cancer that is considered a malignant tumor is melanoma. Such a dangerous disease can cause a lot of death in the world. The early detection of skin lesions becomes an important task in the diagnosis of skin cancer. Recently, a machine learning paradigm emerged known as deep learning (DL) utilized for skin lesions classification. However, in some previous studies by using seven class images diagnostic of skin lesions classification based on a single DL approach with CNNs architecture does not produce a satisfying performance. The DL approach allows the development of a medical

APA, Harvard, Vancouver, ISO, and other styles

23

Oftadeh, Hadis, and Mohammad Manthouri. "APPLICATION OF DEEP BOLTZMANN MACHINE IN DIAGNOSIS PROCESSES OF HEPATITIS TYPES B & C." Azerbaijan Journal of High Performance Computing 5, no. 2 (2022): 112–30. http://dx.doi.org/10.32010/26166127.2022.5.1.112.130.

Full text

Abstract:

Correct diagnosis of diseases is the main problem in medicine. Artificial intelligence and learning methods have been developed to solve problems in many fields, such as biology and medical sciences. Correct diagnosis before treatment is the most challenging and the first step in achieving proper cures. The primary objective of this paper is to introduce an intelligent system that develops beyond the deep neural network. It can diagnose and distinguish between Hepatitis types B and C by using a set of general tests for liver health. The deep network used in this research is the Deep Boltzmann

APA, Harvard, Vancouver, ISO, and other styles

24

V., K. Gahal V. A. Chavan* S. S. Deokar. "Brain Tumors: Signs, Detection, and Treatment." International Journal in Pharmaceutical Sciences 1, no. 11 (2023): 140–44. https://doi.org/10.5281/zenodo.10073048.

Full text

Abstract:

Among all types of cancer, brain tumor is a rare type of cancer but it is deadliest cancer. These tumors are difficult to treat, due to protected in the hard skull. All abnormal cell are not tumors but some of which can lead cancer. Around 50-60% patients of brain tumor are getting well due to advanced technology. Radiation therapy and chemotherapy play important roll to treat brain tumors, with help of MRI images tumors can be detected and treated. Right method of segmentation must use to divide patient to give them proper treatment. Region growing and clustering algorithm are commonly used r

APA, Harvard, Vancouver, ISO, and other styles

25

Ding, Junsheng, and Zhongyong Zhao. "Diagnosis of Stator Inter-Turn Short Circuit Faults in Synchronous Machines Based on SFRA and MTST." Energies 18, no. 8 (2025): 2142. https://doi.org/10.3390/en18082142.

Full text

Abstract:

As a key component of the power system, the good or bad conditions of synchronous machines will directly affect the stable supply of electric energy. The inter-turn short fault of the stator is one of the main dangers to the synchronous machine and is difficult to diagnose. Frequency response analysis has recently been introduced and used for detecting this type of fault; however, the fault degrees and locations cannot be directly recognized by traditional frequency response analysis. Therefore, this study improves the frequency response analysis by combining it with a deep learning model of a

APA, Harvard, Vancouver, ISO, and other styles

26

Saldanha, Juli Sergine Tavares Teixeira, Jaime Andres Collazos Gonzalez, Paulo Douglas Santos de Lima, Hugo Alexandre Dantas do Nascimento, and João Medeiros de Araújo. "A Comparison of WaveNet and XGBoost for traditional direct wave propagation and seismic inversion using horizontal layer models." Research, Society and Development 13, no. 5 (2024): e7213545797. http://dx.doi.org/10.33448/rsd-v13i5.45797.

Full text

Abstract:

The application of machine learning in geophysics has steeply increased in the last decade, with the quality of its results varying according to the type of seismic problem in focus and the employed computational method. Deep Learning methods are achieving impressive results in this area, but we note that there is still a lack of certainty on whether classical machine learning methods can provide similar results. In the present paper, the objective was to attempt to fill part of that gap, by comparing a well-known non-DL machine-learning method with a DL method for the direct wave propagation

APA, Harvard, Vancouver, ISO, and other styles

27

Zhang, Shengao, Mengze Li, and Chunxiao Yan. "The Empirical Analysis of Bitcoin Price Prediction Based on Deep Learning Integration Method." Computational Intelligence and Neuroscience 2022 (June 10, 2022): 1–9. http://dx.doi.org/10.1155/2022/1265837.

Full text

Abstract:

As a new type of electronic currency, bitcoin is more and more recognized and sought after by people, but its price fluctuation is more intense, the market has certain risks, and the price is difficult to be accurately predicted. The main purpose of this study is to use a deep learning integration method (SDAE-B) to predict the price of bitcoin. This method combines two technologies: one is an advanced deep neural network model, which is called stacking denoising autoencoders (SDAE). The SDAE method is used to simulate the nonlinear complex relationship between the bitcoin price and its influe

APA, Harvard, Vancouver, ISO, and other styles

28

Zhang, Shaoxuan, Senxiang Lu, and Xu Dong. "Stress and Corrosion Defect Identification in Weak Magnetic Leakage Signals Using Multi-Graph Splitting and Fusion Graph Convolution Networks." Machines 11, no. 1 (2023): 70. http://dx.doi.org/10.3390/machines11010070.

Full text

Abstract:

Weak magnetic flux leak detection is one of the most important non-destructive testing and measurement methods for pipelines. Since different defects cause different damage, it is necessary to classify the different types of defects. Traditional machine learning methods of defect type identification mainly use feature analysis methods and rely on expert a priori knowledge and the ability of designers. These methods have the following weaknesses: a priori knowledge needs to be designed iteratively, and a priori knowledge design relies on expert experience. In recent years, the rapid development

APA, Harvard, Vancouver, ISO, and other styles

29

Bahai, Akash, Chee Keong Kwoh, Yuguang Mu, and Yinghui Li. "Systematic benchmarking of deep-learning methods for tertiary RNA structure prediction." PLOS Computational Biology 20, no. 12 (2024): e1012715. https://doi.org/10.1371/journal.pcbi.1012715.

Full text

Abstract:

The 3D structure of RNA critically influences its functionality, and understanding this structure is vital for deciphering RNA biology. Experimental methods for determining RNA structures are labour-intensive, expensive, and time-consuming. Computational approaches have emerged as valuable tools, leveraging physics-based-principles and machine learning to predict RNA structures rapidly. Despite advancements, the accuracy of computational methods remains modest, especially when compared to protein structure prediction. Deep learning methods, while successful in protein structure prediction, hav

APA, Harvard, Vancouver, ISO, and other styles

30

Hernandez, Jérôme, Mathieu Muratet, Matthis Pierotti, and Thibault Carron. "Can We Detect Non-playable Characters’ Personalities Using Machine And Deep Learning Approaches?" European Conference on Games Based Learning 16, no. 1 (2022): 271–79. http://dx.doi.org/10.34190/ecgbl.16.1.627.

Full text

Abstract:

Personality recognition and computational psychometrics data have become prevalent in personnel selection processes. Such assessment tools are adequate for human resources seeking tools to assess a large volume of diverse player personalities in the current "war of talents." Recently, studies about using Gamified situational judgment test approaches have shown positive results in assessing players' behavior and personality. Gamified situational judgment tests combine the advantages of gamification, such as enhancing players' reactions and flow state, with the acknowledged traditional sit

APA, Harvard, Vancouver, ISO, and other styles

31

Chua, Tuan-Hong, and Iftekhar Salam. "Evaluation of Machine Learning Algorithms in Network-Based Intrusion Detection Using Progressive Dataset." Symmetry 15, no. 6 (2023): 1251. http://dx.doi.org/10.3390/sym15061251.

Full text

Abstract:

Cybersecurity has become one of the focuses of organisations. The number of cyberattacks keeps increasing as Internet usage continues to grow. As new types of cyberattacks continue to emerge, researchers focus on developing machine learning (ML)-based intrusion detection systems (IDS) to detect zero-day attacks. They usually remove some or all attack samples from the training dataset and only include them in the testing dataset when evaluating the performance. This method may detect unknown attacks; however, it does not reflect the long-term performance of the IDS as it only shows the changes

APA, Harvard, Vancouver, ISO, and other styles

32

Zhang, Shuhui, Changdong Hu, Lianhai Wang, Miodrag J. Mihaljevic, Shujiang Xu, and Tian Lan. "A Malware Detection Approach Based on Deep Learning and Memory Forensics." Symmetry 15, no. 3 (2023): 758. http://dx.doi.org/10.3390/sym15030758.

Full text

Abstract:

As cyber attacks grow more complex and sophisticated, new types of malware become more dangerous and challenging to detect. In particular, fileless malware injects malicious code into the physical memory directly without leaving attack traces on disk files. This type of attack is well concealed, and it is difficult to find the malicious code in the static files. For malicious processes in memory, signature-based detection methods are becoming increasingly ineffective. Facing these challenges, this paper proposes a malware detection approach based on convolutional neural network and memory fore

APA, Harvard, Vancouver, ISO, and other styles

33

Vaghashiya, Rajkumar, Sanghoon Shin, Varun Chauhan, et al. "Machine Learning Based Lens-Free Shadow Imaging Technique for Field-Portable Cytometry." Biosensors 12, no. 3 (2022): 144. http://dx.doi.org/10.3390/bios12030144.

Full text

Abstract:

The lens-free shadow imaging technique (LSIT) is a well-established technique for the characterization of microparticles and biological cells. Due to its simplicity and cost-effectiveness, various low-cost solutions have been developed, such as automatic analysis of complete blood count (CBC), cell viability, 2D cell morphology, 3D cell tomography, etc. The developed auto characterization algorithm so far for this custom-developed LSIT cytometer was based on the handcrafted features of the cell diffraction patterns from the LSIT cytometer, that were determined from our empirical findings on th

APA, Harvard, Vancouver, ISO, and other styles

34

Skondras, Panagiotis, Nikos Zotos, Dimitris Lagios, Panagiotis Zervas, Konstantinos C. Giotopoulos, and Giannis Tzimas. "Deep Learning Approaches for Big Data-Driven Metadata Extraction in Online Job Postings." Information 14, no. 11 (2023): 585. http://dx.doi.org/10.3390/info14110585.

Full text

Abstract:

This article presents a study on the multi-class classification of job postings using machine learning algorithms. With the growth of online job platforms, there has been an influx of labor market data. Machine learning, particularly NLP, is increasingly used to analyze and classify job postings. However, the effectiveness of these algorithms largely hinges on the quality and volume of the training data. In our study, we propose a multi-class classification methodology for job postings, drawing on AI models such as text-davinci-003 and the quantized versions of Falcon 7b (Falcon), Wizardlm 7B

APA, Harvard, Vancouver, ISO, and other styles

35

Jia, Jianhua, Lulu Qin, and Rufeng Lei. "DGA-5mC: A 5-methylcytosine site prediction model based on an improved DenseNet and bidirectional GRU method." Mathematical Biosciences and Engineering 20, no. 6 (2023): 9759–80. http://dx.doi.org/10.3934/mbe.2023428.

Full text

Abstract:

<abstract> <p>The 5-methylcytosine (5mC) in the promoter region plays a significant role in biological processes and diseases. A few high-throughput sequencing technologies and traditional machine learning algorithms are often used by researchers to detect 5mC modification sites. However, high-throughput identification is laborious, time-consuming and expensive; moreover, the machine learning algorithms are not so advanced. Therefore, there is an urgent need to develop a more efficient computational approach to replace those traditional methods. Since deep learning algorithms are m

APA, Harvard, Vancouver, ISO, and other styles

36

An, Feng-Ping. "Medical Image Classification Algorithm Based on Weight Initialization-Sliding Window Fusion Convolutional Neural Network." Complexity 2019 (October 20, 2019): 1–15. http://dx.doi.org/10.1155/2019/9151670.

Full text

Abstract:

Due to the complexity of medical images, traditional medical image classification methods have been unable to meet actual application needs. In recent years, the rapid development of deep learning theory has provided a technical approach for solving medical image classification tasks. However, deep learning has the following problems in medical image classification. First, it is impossible to construct a deep learning model hierarchy for medical image properties; second, the network initialization weights of deep learning models are not well optimized. Therefore, this paper starts from the per

APA, Harvard, Vancouver, ISO, and other styles

37

Wang, Zhao, Hanjun Yin, Haoxuan Tang, et al. "Damage Evaluation of Unconsolidated Sandstone Particle Migration Reservoir Based on Well–Seismic Combination." Processes 12, no. 9 (2024): 2009. http://dx.doi.org/10.3390/pr12092009.

Full text

Abstract:

The primary factor constraining the performance of unconsolidated sandstone reservoirs is blockage from particle migration, which reduces the capacity of liquid production. By utilizing logging, seismic, core–testing, and oil–well production data, the reservoir damage induced by particle migration in the Bohai A oilfield was characterized and predicted through combined well–seismic methods. This research highlights the porosity, permeability, median grain diameter, and pore structure as the primary parameters influencing reservoir characteristics. Based on their permeability differences, reser

APA, Harvard, Vancouver, ISO, and other styles

38

Afolabi, Hassan A., and Abdurazzag A. Aburas. "Statistical performance assessment of supervised machine learning algorithms for intrusion detection system." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 266–77. https://doi.org/10.11591/ijai.v13.i1.pp266-277.

Full text

Abstract:

Several studies have shown that an ensemble classifier's effectiveness is directly correlated with the diversity of its members. However, the algorithms used to build the base learners are one of the issues encountered when using a stacking ensemble. Given the number of options, choosing the best ones might be challenging. In this study, we selected some of the most extensively applied supervised machine learning algorithms and performed a performance evaluation in terms of well-known metrics and validation methods using two internet of things (IoT) intrusion detection datasets, namely network

APA, Harvard, Vancouver, ISO, and other styles

39

Puchkov, Andrey Yu, Maxim I. Dli, Maria A. Vasilkova, and Nikolay N. Prokimnov. "A method for predicting bank customer churn based on an ensemble machine learning model." Journal Of Applied Informatics 19, no. 1 (2024): 5–27. http://dx.doi.org/10.37791/2687-0649-2024-19-1-5-27.

Full text

Abstract:

The results of research are presented, the purpose of which was to develop a method for predicting the outflow of clients of a commercial bank based on the use of machine learning models (including deep artificial neural networks) for processing client data, as well as the creation of software tools that implement this method. The object of the study is a commercial bank, and the subject of the study is its activities in the B2C segment, which includes commercial interaction between businesses and individuals. The relevance of the chosen area of research is determined by the increased activity

APA, Harvard, Vancouver, ISO, and other styles

40

Afolabi, Hassan A., and Aburas A. Abdurazzag. "Statistical performance assessment of supervised machine learning algorithms for intrusion detection system." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 266. http://dx.doi.org/10.11591/ijai.v13.i1.pp266-277.

Full text

Abstract:

<span lang="EN-US">Several studies have shown that an ensemble classifier's effectiveness is directly correlated with the diversity of its members. However, the algorithms used to build the base learners are one of the issues encountered when using a stacking ensemble. Given the number of options, choosing the best ones might be challenging. In this study, we selected some of the most extensively applied supervised machine learning algorithms and performed a performance evaluation in terms of well-known metrics and validation methods using two internet of things (IoT) intrusion detection

APA, Harvard, Vancouver, ISO, and other styles

41

Naqvi, Sardar Shan Ali, Yuancheng Li, and Muhammad Uzair. "DDoS attack detection in smart grid network using reconstructive machine learning models." PeerJ Computer Science 10 (January 9, 2024): e1784. http://dx.doi.org/10.7717/peerj-cs.1784.

Full text

Abstract:

Network attacks pose a significant challenge for smart grid networks, mainly due to the existence of several multi-directional communication devices coupling consumers to the grid. One of the network attacks that can affect the smart grid is the distributed denial of service (DDoS), where numerous compromised communication devices/nodes of the grid flood the smart grid network with false data and requests, leading to disruptions in smart meters, data servers, and the state estimator, ultimately effecting the services for end-users. Machine learning-based strategies show distinctive benefits in

APA, Harvard, Vancouver, ISO, and other styles

42

Horbenko, Vladyslav, Kostiantyn Onyshchenko, Iryna Afanasieva, and Roman Kameniev. "Analysis of existing methods and models of in-depth learning in the problems of natural language processing." Bionics of Intelligence 2, no. 97 (2021): 33–38. https://doi.org/10.30837/bi.2021.2(97).05.

Full text

Abstract:

Approaches and methods of deep learning of natural language are considered. Deep neural networks are being created as direct propagation networks, but research has very successfully applied recurrent neural networks to tasks such as language modeling. Convolutional deep neural networks are used in computer vision and natural language. Convolutional deep neural networks have also been applied to acoustic modeling for automatic speech recognition. Flexible neural network, these functions are used to describe rectangular objects in the form of a binary mask in natural language, as well as to calc

APA, Harvard, Vancouver, ISO, and other styles

43

Yildirim, Isa Eren, Tariq Alkhalifah, and Ertugrul Umut Yildirim. "Machine learning-enabled traveltime inversion based on the horizontal source-location perturbation." GEOPHYSICS 87, no. 1 (2021): U1—U8. http://dx.doi.org/10.1190/geo2020-0735.1.

Full text

Abstract:

Gradient-based traveltime tomography, which aims to minimize the difference between modeled and observed first-arrival times, is a highly nonlinear optimization problem. Stabilization of this inverse problem often requires using regularization. Although regularization helps avoid local minima solutions, it might cause low-resolution tomograms because of its inherent smoothing property. However, although conventional ray-based tomography can be robust in terms of the uniqueness of the solution, it suffers from the limitations inherent in ray tracing, which limits its use in complex media. To mi

APA, Harvard, Vancouver, ISO, and other styles

44

Lagerquist, Ryan, Amy McGovern, Cameron R. Homeyer, David John Gagne II, and Travis Smith. "Deep Learning on Three-Dimensional Multiscale Data for Next-Hour Tornado Prediction." Monthly Weather Review 148, no. 7 (2020): 2837–61. http://dx.doi.org/10.1175/mwr-d-19-0372.1.

Full text

Abstract:

Abstract This paper describes the development of convolutional neural networks (CNN), a type of deep-learning method, to predict next-hour tornado occurrence. Predictors are a storm-centered radar image and a proximity sounding from the Rapid Refresh model. Radar images come from the Multiyear Reanalysis of Remotely Sensed Storms (MYRORSS) and Gridded NEXRAD WSR-88D Radar dataset (GridRad), both of which are multiradar composites. We train separate CNNs on MYRORSS and GridRad data, present an experiment to optimize the CNN settings, and evaluate the chosen CNNs on independent testing data. Bot

APA, Harvard, Vancouver, ISO, and other styles

45

Mohammadpour, Leila, T. C. Ling, C. S. Liew, and Alihossein Aryanfar. "A Mean Convolutional Layer for Intrusion Detection System." Security and Communication Networks 2020 (October 24, 2020): 1–16. http://dx.doi.org/10.1155/2020/8891185.

Full text

Abstract:

The significant development of Internet applications over the past 10 years has resulted in the rising necessity for the information network to be secured. An intrusion detection system is a fundamental network infrastructure defense that must be able to adapt to the ever-evolving threat landscape and identify new attacks that have low false alarm. Researchers have developed several supervised as well as unsupervised methods from the data mining and machine learning disciplines so that anomalies can be detected reliably. As an aspect of machine learning, deep learning uses a neuron-like struct

APA, Harvard, Vancouver, ISO, and other styles

46

Tirumalapudi, Raviteja, Nanda Kumar M, and Sirisha J. "Onward and Autonomously: Expanding the Horizon of Image Segmentation for Self-Driving Cars through Machine Learning." Scalable Computing: Practice and Experience 25, no. 4 (2024): 3163–71. http://dx.doi.org/10.12694/scpe.v25i4.2869.

Full text

Abstract:

Autonomous navigation is the leading technology in current era, in this intelligent traffic light, sign detection, ADAS and obstacle detections were playing major role. Image segmentation is the process of dividing an image into different regions, or semantic classes. This is a challenging problem in autonomous vehicle technology because it requires the vehicle to be able to understand its surroundings to safely navigate. The major challenges in this platform are the accuracy and efficiency of model performance. The proposed method in the abstract uses a convolutional neural network (CNN) to p

APA, Harvard, Vancouver, ISO, and other styles

47

Chen, Wei, Liuqing Yang, Bei Zha, Mi Zhang, and Yangkang Chen. "Deep learning reservoir porosity prediction based on multilayer long short-term memory network." GEOPHYSICS 85, no. 4 (2020): WA213—WA225. http://dx.doi.org/10.1190/geo2019-0261.1.

Full text

Abstract:

The cost of obtaining a complete porosity value using traditional coring methods is relatively high, and as the drilling depth increases, the difficulty of obtaining the porosity value also increases. Nowadays, the prediction of fine reservoir parameters for oil and gas exploration is becoming more and more important. Therefore, high-efficiency and low-cost prediction of porosity based on logging data is necessary. We have developed a machine-learning method based on the traditional long short-term memory (LSTM) model, called multilayer LSTM (MLSTM), to perform the porosity prediction task. We

APA, Harvard, Vancouver, ISO, and other styles

48

Raj, Veena, Sam-Quarcoo Dotse, Mathew Sathyajith, M. I. Petra, and Hayati Yassin. "Ensemble Machine Learning for Predicting the Power Output from Different Solar Photovoltaic Systems." Energies 16, no. 2 (2023): 671. http://dx.doi.org/10.3390/en16020671.

Full text

Abstract:

In this paper, ensemble-based machine learning models with gradient boosting machine and random forest are proposed for predicting the power production from six different solar PV systems. The models are based on three year’s performance of a 1.2 MW grid-integrated solar photo-voltaic (PV) power plant. After cleaning the data for errors and outliers, the model features were chosen on the basis of principal component analysis. Accuracies of the developed models were tested and compared with the performance of models based on other supervised learning algorithms, such as k-nearest neighbour and

APA, Harvard, Vancouver, ISO, and other styles

49

Mantha, Simon, Andrew Dunbar, Kelly L. Bolton, et al. "Machine Learning for Prediction of Cancer-Associated Venous Thromboembolism." Blood 136, Supplement 1 (2020): 37. http://dx.doi.org/10.1182/blood-2020-138579.

Full text

Abstract:

Background: Several clinical prediction scores have been designed to assess the risk of cancer-associated thrombosis (CAT). The most commonly used in current clinical practice is the Khorana score, however it is applicable only to patients prior to initiation of chemotherapy. We now apply machine learning with clinical, demographic, and genomics parameters to predict CAT events. Methods: The random survival forest (RSF) ensemble learning method was selected to illustrate a machine approach to CAT prediction. The cohort consisted of 14,223 individuals with a solid tumor malignancy and MSK IMPAC

APA, Harvard, Vancouver, ISO, and other styles

50

Guo, Zhengwei, Wenwen Qi, Yabo Huang, et al. "Identification of Crop Type Based on C-AENN Using Time Series Sentinel-1A SAR Data." Remote Sensing 14, no. 6 (2022): 1379. http://dx.doi.org/10.3390/rs14061379.

Full text

Abstract:

Crop type identification is the initial stage and an important part of the agricultural monitoring system. It is well known that synthetic aperture radar (SAR) Sentinel-1A imagery provides a reliable data source for crop type identification. However, a single-temporal SAR image does not contain enough features, and the unique physical characteristics of radar images are relatively lacking, which limits its potential in crop mapping. In addition, current methods may not be applicable for time-series SAR data. To address the above issues, a new crop type identification method was proposed. Speci

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!