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检索条件"任意字段=2023 International Conference on Computer Engineering and Distance Learning, CEDL 2023"

共 40714 条记录，以下是4781-4790 订阅

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Lack of Efficiency on Robotic Hectoring Recognition over Online Social Media Networks using Novel Support Vector Machine Algorithm Comparing Lasso Algorithm

Lack of Efficiency on Robotic Hectoring Recognition over Onl...

引用

2023 international conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Reddy, P. Sai Vindhya, A. Shri Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350334357

This study points to enhance the efficiency of detection to alleviate the impact of cyberbullying using machine learning classifiers by comparing the Novel SVMalgorithm and the Lasso algorithm. Materials and Methods: The Novel SVMalgorithm sample size of 184 and the Lasso algorithm test size of 184 with G power (0.6) in order to anticipate the efficiency %, were examined several times. The Novel SVM algorithm maps the prediction by using its labels. Results: The Novel SVM algorithm has better accuracy (88.90%) in comparison to the Lasso algorithm efficiency (80.00%). The outcomes that had a significant value p=0.001 (p © 2023 IEEE.

关键词： Support vector machines

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Diagnosis of Alzheimer's Disease Using Boosting Classification Algorithms

Diagnosis of Alzheimer's Disease Using Boosting Classificati...

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2023 IEEE international conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Önder, Mithat Şentürk, Ümit Polat, Kemal Paulraj, D. Department of Electrical and Electronics Engineering Bolu Abant İzzet Baysal University Bolu Turkey Department of Computer Engineering Bolu Abant İzzet Baysal University Bolu Turkey Department of Computer Science and Engineering R.M.K. Engineering College Chennai Chennai India

ISBN: (纸本)9798350305708

Alzheimer's Disease (AD) is a progressive degenerative disorder of the brain that impacts memory, cognition, and, ultimately, the ability to carry out daily activities. There is presently no cure for Alzheimer's Disease. However, there are available treatments to manage symptoms and slow their advancement. This research conducted a comprehensive study to diagnose AD using four different categorization methods. These methods included XGBoost, GradientBoost, AdaBoost, and voting classification algorithms. To carry out the examination, a high-quality dataset was obtained from the collection of machine learning data of the prestigious University of California. This dataset was carefully selected to ensure accurate and reliable results. The analysis of the collected data revealed some interesting findings. XGBoost exhibited an accuracy rate of 85% in diagnosing Alzheimer's Disease. ADABoost also performed, achieving an accuracy rate of 75%. GradientBoost, similarly, obtained an accuracy rate of 85%. Additionally, the voting classification algorithms showed promise, attaining an accuracy rate of 80%. All these accuracy rates were obtained by implementing a 5-fold cross-validation methodology, which ensured robust and unbiased results. This research contributes to the field of AD diagnosis by providing insights into the effectiveness of different categorization methods. © 2023 IEEE.

关键词： ADABoost Alzheimer's Disease GradientBoost XGBoost

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Heart Disease Prediction based on Machine learning Technique 3

Heart Disease Prediction based on Machine learning Technique

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3rd IEEE international conference on Technology, engineering, Management for Societal Impact using Marketing, Entrepreneurship and Talent, TEMSMET 2023

作者： Hafiz, Abdul Kaur, Narinder Chandigarh University Computer Science & Engineering Mohali Punjab Gharuan India

ISBN: (纸本)9798350397819

Any condition that poses a significant risk to one's life and affects the cardiovascular system is referred to as ''cardiovascular disease Because of the potentially lethal nature of heart conditions, scientists are working to develop intelligent systems that can accurately diagnose these conditions using electronic health data and machine learning algorithms. These systems will be able to draw their information from electronic health records. In this article, to provide a number of different machine learning algorithms for the purpose of predicting cardiovascular disease based on an examination of the demographic and clinical data of patients. In the research article, a total of four distinct categorization approaches were discussed for the purpose of creating predictions. These included the Multilayer Perceptron (MLP), Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes models (NB). The data were cleaned up, and then features were decided upon, before the models were constructed. When ranking the performance of the models, numerous other criteria, such as the F1-score, accuracy, precision, and recall, were utilized. The Logistic Regression Classifier was successful in achieving an overall score of 91.8%. © 2023 IEEE.

关键词： Support vector machines

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Efficient Diagnosis of Coronary Artery Disease: A Hybrid Convolutional-XGBoost Approach 2

Efficient Diagnosis of Coronary Artery Disease: A Hybrid Con...

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2nd international conference on Ambient Intelligence in Health Care, ICAIHC 2023

作者： Mishra, Arkashree P. Panigrahi, Suvasini Veer Surendra Sai University of Technology Dept. of Computer Science & Engineering Burla India

ISBN: (纸本)9798350344882

Cardio Vascular Diseases (CVDs) are the prime reason of death globally. Particularly, the cases of coronary artery disease (CAD) have been rising in the post Covid era, especially amongst middle-aged people. Thus, the before time detection of the disease might evade the growing number of fatalities and morbidities. Two most common and accurate methods used to detect CAD are angiography and electrocardiography (ECG). While the former is invasive and a risky procedure, the latter one although non-invasive lacks correctness. Over a decade, non-invasive Machine learning (ML) as well as Deep learning (DL) methods have shaped the healthcare scenario by facilitating early diagnosis of the diseases through the application of various computer based algorithmic models over clinical data and images. A hybrid Convolutional- Extreme Gradient Boosting (Convolutional XGBoost) model has been proposed to accurately detect CAD by outperforming some of the existing methods. © 2023 IEEE.

关键词： Diseases

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Deep learning-based Segmentation and computer Vision-based Ultrasound Imagery Techniques 5

Deep Learning-based Segmentation and Computer Vision-based U...

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5th IEEE international conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Girinath, S. Kavitha, T. Chandra, Pamulapati Satish Kumar, Nellore Manoj Gattim, Naveen Kishore Challa, Ratna Kumari Sree Vidyanikethan Engineering College Department of MCA Tirupati India Periyar Maniammai Institute of Science & Technology Department of CSE Thanjavur India SRM Institute of Science & Technology Department of ECE Chennai India SCSVMV Deemed University Department of Physics Kanchipuram India Sasi Institute of Technology and Engineering Department of ECE Tadepalligudem India RGUKT Department of CSE AP-IIIT YSR Kadapa India

ISBN: (纸本)9781665493604

Biomedical imaging has been a game-changer in the medical field because of how it facilitates the analysis of human body issues. The best possible diagnosis may now be made thanks to the development of computer science and its integration with medical imaging. The state-of-the-art in biological image processing has been demonstrated by methods based on deep learning. Thanks to these approaches' inherent capacity for self-learning, manually constructed features are no longer necessary. It has given a world-class solution with trustworthy outcomes for medical image processing in this way. This effort is being done to help with better diagnosis by addressing the key challenges mentioned above. These approaches' inherent capacity for self-learning has rendered custom enhancements superfluous. This has supplied an excellent solution with trustworthy outcomes in the field of medical image processing. In order to better diagnose health problems, the current effort is conducted to solve the aforementioned key challenges. Therefore, the study aims to create novel segmentation and classification methods for identifying the different types of breast masses from US pictures. The elastic characteristics of tissues can now be imaged thanks to a novel method called elastography. In our studies, we use ultrasound elastography as well as ultrasound B-mode to categorise breast tumours. © 2023 IEEE.

关键词： Diagnosis

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Domain Adaptation based on Attention-Weighted Optimal Transport and Cluster Alignment 4

Domain Adaptation based on Attention-Weighted Optimal Transp...

引用

4th international conference on computer Vision, Image and Deep learning, CVIDL 2023

作者： Zhu, Yanan Qi, Yangyang College of Computer Engineering and Science Shanghai University Shanghai China

ISBN: (纸本)9798350326444

Optimal transport (OT) as an efficient way to measure differences in distribution. provide new ideas for solving the unsupervised domain adaptation (UDA) problem. However, existing work based on OT ignores the multi-modal structure of the domain and does not adequately reflect the true data distribution. Therefore, this paper proposes a new deep UDA framework, namely attention-weighted optimal transport and cluster alignment (AWOT). Specifically, the model first proposes a weighting strategy based on a self-attentive mechanism to reduce the bias caused by mini-batch selection in training. The strategy calculates weights by correlating the iterative prediction results of the source and target domains to weight the optimal in-transmission cost function. A confidence thresholding technique is also proposed to filter out low confidence target samples to enhance the robustness of pseudo-label. Moreover, AWOT is also equipped with a clustering centroid alignment loss to learn discriminative features. Comprehensive experiments have shown that AWOT outperforms existing state-of-the-art methods on several domain adaptation benchmarks. © 2023 IEEE.

关键词： Alignment

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Multi-Agent Reinforcement learning with Epistemic Priors 9

Multi-Agent Reinforcement Learning with Epistemic Priors

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9th international conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Walker, Thayne T. Ide, Jaime S. Choi, Minkyu Guarino, Michael Alcedo, Kevin Applied Ai Team University of Denver Department of Computer Science United States Yale University Department of Psychiatry United States University of Texas at Austin Department of Electrical and Computer Engineering AustinTX United States

ISBN: (纸本)9798350311402

It is important for autonomous multi-agent teams to coordinate actions so that collaborative goals can be achieved efficiently without conflicts. Practical issues in multi-agent, real-time systems are limited sensing and communication capabilities. A significant number of multi-agent algorithms rely on accurate state information for all agents in order to effectively coordinate. In this paper, we propose an approach called Reinforcement learning with Epistemic Priors (MARL-EP). MARL-EP incorporates a shared mental model between agents by leveraging epistemic estimation to infer portions of the obser-vation space which are unobservable. We show that MARL-EP allows a very high level of coordination to be achieved with severely impaired sensing and zero communication between agents. © 2023 IEEE.

关键词： Real time systems

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Energy Based Stable Multi-Hop Leader Selection for Effective Communication in Vehicular Ad-Hoc Networks 6

Energy Based Stable Multi-Hop Leader Selection for Effective...

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6th Iraqi international conference on engineering Technology and its Applications, IICETA 2023

作者： Malalah Al-Sharhanee, Kareem Ali Najim, Ali Hamzah Alkhazraji, Nujod Abbas, Fatima H. Alkhayyat, Ahmed H. Al-Farahidi University College of Engineering Techniques Department of Communication Technical Engineering Baghdad Iraq Department of Computer Technical Engineering Al Diwaniyah Iraq Imam Ja'afar Al-Sadiq University College of Information Technology Department of Computer Technical Engineering Al-Muthanna Iraq Al-Mustaqbal University College Al-Mustaqbal University Medical Laboratories Techniques Department Hillah Iraq Islamic University College of Technical Engineering Department of Computer Technical Engineering Najaf Iraq

ISBN: (纸本)9798350303339

Vehicular Adhoc Network (VANETs) have high speed mobility with huge data communication which affects the the traditional VANET communication. As the reason in the dynamically varying topology the consumption of energy and delay is maximized at the time of vehicle communication. To overcome this drawback in this article Energy based Stable Multi-hop Leader Selection (SMLS) is introduced in VANETs. Selection of stable CH is the main motive where certain effectual parameters are concentrated to achieve high efficiency and the chosen parameters are vehicle average velocity, stabilization factor, neighbor distance, inter-nodal distance and the vehicle trust value with the combination of direct trust and indirect trust calculation. Through this calculation the stable clusters are formed that's leads to achieve high quality in data communication which is carried out using inter cluster and intra cluster network model. The extensive simulation is done in NS3 whereas it gets compared with the earlier researches such as HGCM-VANETs and ICAH-VANETs. The simulation result proves that the proposed stable multi-hop leader selection for VANETs achieves high CH stability, CM lifetime, and throughput when compared with the earlier methods. © 2023 IEEE.

关键词： Vehicular ad hoc networks

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Potato leaf disease prediction using RMSProp, Adam and SGD optimizers

Potato leaf disease prediction using RMSProp, Adam and SGD o...

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2023 international conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Prasher, Shikha Nelson, Leema Jagdish, Mukta Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Vardhaman College of Engineering Dept. of Information Technology Telangana Hyderabad India

ISBN: (纸本)9798350396485

The advances in agricultural equipment and the utilization of artificial intelligence to the treatment of plant diseases is critical to undertake meaningful research for the development of organic agriculture. Numerous diseases, including such late blight and early blight, have a major effect on the quality and yields of potatoes, and subjective analysis of The several leaf diseases require a lot of time and labour. Despite the high level of knowledge required, automated and efficient recognition of these infections during the blossoming phase can help to boost the output of the potato crop. This research provides a sequential model that identifies the significant features from the dataset utilising pre-Trained deep learning model with RMSProp, Adam and SGD optimizer. The convolutional neural network (CNN) model is developed using potato leaf diseases dataset obtained from Kaggle respository. The RMSProp, Adam and SGD optimizers achieve accuracy of 97.6%, 96% and 94.3%. © 2023 IEEE.

关键词： Convolutional neural networks

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Intracranial Haemorrhage Detection Based on Deep learning Using CT Images 2023

Intracranial Haemorrhage Detection Based on Deep Learning Us...

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15th international conference on Contemporary Computing, IC3 2023

作者： Mishra, Pratyush Raj Ilavarasi, A.K. School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Science and Engineering Centre for Healthcare Advancement Innovation and Research Vellore Institute of Technology Chennai India

ISBN: (纸本)9798400700224

Intracranial haemorrhage (ICH) is a medical condition that can have life-threatening consequences if it is not promptly diagnosed and treated. Diagnostic tools for medical imaging, such computed tomography (CT) scans, can help identify ICH. In this study, we investigated the application of deep learning methods to CT scan images used for ICH diagnosis. In particular, the authors made use of the Block Matching 3D (BM3D) algorithm for image pre-processing, the Densenet121 neural network design, and the Hough Transformation for image transformation. The objective was to create a model that could correctly identify and categorize ICH in CT scan pictures. The region of interest (ROI) in the CT scan pictures was then located and extracted using the Hough Transformation approach. Overall, the findings of this study show the potential of deep learning methods to support ICH diagnosis from CT scan pictures. The suggested approach was highly accurate and has the potential to help radiologists identify ICH. © 2023 ACM.

关键词： Diagnosis

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