the proceedings contain 40 papers. the special focus in this conference is on Innovations in Computational Intelligence and Computer Vision. the topics include: dataset Balancing Techniques and Supervised Learnin...
ISBN:
(纸本)9789819769940
the proceedings contain 40 papers. the special focus in this conference is on Innovations in Computational Intelligence and Computer Vision. the topics include: dataset Balancing Techniques and Supervised learning Algorithms for Predictive Analysis of Rice and Corn Yields;fish Blood Cell as Biological Dosimeter: In Between Measurements, Radiomics, Preprocessing, and Artificial Intelligence;predictive Analysis for Early Detection of Breast Cancer through Artificial Intelligence Algorithms;boosting Security: An Effective Approach to Intrusion Detection in Wireless Sensor Networks with AdaBoost Classifiers;Chat2Fluency: Enhancing Language learningthrough Conversational AI;REED-NET: Residual Enhanced Encoder-Decoder Network for Low-Dose CT Reconstruction;comparative Analysis of Large Language Models;Visualizing Insights to Empower HR Decision-Making: A data-Driven Approach;handling Uncertainty in Parkinson’s Disease Voice data Using Intuitionistic Fuzzy Entropy Measure;Exploring Shopping Opportunities and Elevating Customer Experiences through AI-Powered E-Commerce Strategies;ioT-Based Vehicle Class Detection for Smart Traffic Control;Domain Adaptation for NER Using mBERT;SQL Query Recommendation Based on Matrix Factorization;retenSure: Ensemble learning for Managing Employee Attrition;Subject–Verb Agreement Error Handling Using RNN Architectures;ad-Spend Analytics;an Efficient Real-Time Word-Level recognition of Indian Sign Language;innovating Drug Design for Alzheimer’s Disease via Reinforcement learning for Enhanced Molecular Generation;an Efficient Real-Time recognition of Static Kannada Sign Language;unveiling Diagnostic Clarity: A machinelearning Approach to Distinguish Borderline Personality Disorder and Bipolar Disorder for Enhanced Mental Health Diagnostics;DLSTM with Adam Waterwheel Optimization for Groundwater Level Prediction in India;PhishGuard: machinelearning Model for Real-Time URL Detection;Improved Grasshopper Optimization with Squeezenet (IGO-SNet)
this paper proposes a frequent patterndatamining algorithm based on support vector machine (SVM), aiming to solve the performance bottleneck of traditional frequent patternmining algorithms in high-dimensional and ...
详细信息
ISBN:
(数字)9798331534622
ISBN:
(纸本)9798331534639
this paper proposes a frequent patterndatamining algorithm based on support vector machine (SVM), aiming to solve the performance bottleneck of traditional frequent patternmining algorithms in high-dimensional and sparse data environments. By converting the frequent patternmining task into a classification problem, the SVM model is introduced to improve the accuracy and robustness of pattern extraction. In terms of method design, the kernel function is used to map the data to a high-dimensional feature space, so as to construct the optimal classification hyperplane, realize the nonlinear separation of patterns and the accurate mining of frequent items. In the experiment, two public datasets, Retail and Mushroom, were selected to compare and analyze the proposed algorithm with traditional FP-Growth, FP-Tree, decision tree and random forest models. the experimental results show that the algorithm in this paper is significantly better than the traditional model in terms of three key indicators: support, confidence and lift, showing strong patternrecognition ability and rule extraction effect. the study shows that the SVM model has excellent performance advantages in an environment with high data sparsity and a large number of transactions, and can effectively cope with complex patternmining tasks. At the same time, this paper also points out the potential direction of future research, including the introduction of deep learning and ensemble learning frameworks to further improve the scalability and adaptability of the algorithm. this research not only provides a new idea for frequent patternmining, but also provides important technical support for solving pattern discovery and association rule mining problems in practical applications.
Quantum machinelearning is an emerging sub-field in machinelearning where one of the goals is to perform patternrecognition tasks by encoding data into quantum states. this extension from classical to quantum domai...
详细信息
Attitude-assisted correction framework for general cheerleading based on visual tracking technology is presented in this paper. In the proposed model, the contributions are focused on the three aspects. (1) A tracking...
详细信息
Withthe advancement of computer vision technology, fish recognition plays a crucial role in various fields such as aquaculture processing and disseminating fish knowledge. In this study, we propose an enhanced and li...
详细信息
Citrus canker is a bacterial disease caused by Xanthomonas citri pv citri. Mainly the research in the field of Citrus canker has been done on fruits;early detection of disease on leaves can help in adopting preventive...
详细信息
Human behavior recognition is one of the most important research directions in the field of computer vision, and it plays an important role in the fields of rehabilitative medicine, auxiliary security, and scene enter...
详细信息
the use of data driven automation is not new, but it has gain a lot of attention recently withthe wide-spread understanding that it is the solution to all problems in terms of 'fair' and 'non-bias' cl...
详细信息
ISBN:
(数字)9781665466943
ISBN:
(纸本)9781665466943
the use of data driven automation is not new, but it has gain a lot of attention recently withthe wide-spread understanding that it is the solution to all problems in terms of 'fair' and 'non-bias' classification. this is not different in the law area, where 'artificial intelligence' became a 'magic word'. However, using historic data is a very tricky job which can quite easily propagate discrimination in a very efficient way. thus, this work is aimed to analyse data from legal proceedings looking for evidence related to the occurrence of bias in the judges' decision-making process, considering mainly the gender or social condition of the convicts. Supervised and unsupervised machinelearning techniques, preceded by data analysis and processing procedures, were used to explain and find explicit data behaviour. Our results pointed to the fragility of the techniques to identify biases but suggest the need to improve data pre-processing and the search for more robust classification techniques.
the agriculture sector is still working to mitigate the detrimental effects of plant diseases on crop production and food security. Accurate and prompt disease diagnosis is essential to preventing extensive crop damag...
详细信息
the same herbs from various origins were scanned with mid-infrared under standard lighting conditions, and the spectral readings at various wavelengths were gathered, sorted, examined, and downscaled. the downscaled d...
详细信息
暂无评论