the proceedings contain 38 papers. the topics discussed include: leveraging topic modeling and sentiment analysis to improve digital bank applications;a hybrid ant colony optimization for Parkinson’s disease classifi...
ISBN:
(纸本)9798350368710
the proceedings contain 38 papers. the topics discussed include: leveraging topic modeling and sentiment analysis to improve digital bank applications;a hybrid ant colony optimization for Parkinson’s disease classification based on synthetic minority oversampling and adaptive synthetic techniques;building system to recognize deaf language using BlazePalm and LSTM network model;crosschecking verification over the Ethereum blockchain using QR scannable products;cloud-based application to facilitate employee voice in IT companies;developing a strategic framework for cloud adoption in higher education;the trend of published research for predicting bankruptcy via machinelearning: a bibliometric analysis;and automated emotion recognition from facial expressions using convolutional neural network.
the proceedings contain 41 papers. the special focus in this conference is on Recent Trends in machinelearning. the topics include: Video Enhancement by Progressive Use of Denoising and Super-Resolution;Integrating G...
ISBN:
(纸本)9789819788606
the proceedings contain 41 papers. the special focus in this conference is on Recent Trends in machinelearning. the topics include: Video Enhancement by Progressive Use of Denoising and Super-Resolution;Integrating GPS Technology and Smart Payment Systems for Enhanced Public Transportation Efficiency in India;a machinelearning-Driven Approach for Early Leaf Disease Detection and Classification: A Survey;software Defect Code Analyzer Using Cosine Similarity;unleashing Agricultural Precision: A Deep learning Paradigm for Papaya (Carica Papaya L.) Variety Discrimination and Yield Optimization;Fish Farm Monitoring and Controlled System Using LoRaWAN Network;Deep Neuro-Fuzzy Imaging: Enhanced 3D Brain Reconstruction from MRI Scans Using ANFIS and Deep learning;analysis of Different machinelearning Techniques in Troll data Detection;comparative Analysis of machinelearning Algorithms for Prostate Cancer;breaking Barriers in Communication Using Long Short-Term Memory Networks for Sign Language recognition;data Transmission and Reception with Error Detection Circuitry;An Amphibious Surface Cleaning Robot with GVM Wireless Control and Automatic Waste Segregation;Automatic Speech recognition (ASR) Elevator;integrated Smart Water Management System for Urban Sustainability;a Simplified Deep learning Approach for Diabetic Macular Edema Detection;innovative Rumor Detection on Social Media Text: A Comprehensive Study of Dual Co-Attention Ensemble Based Voting Approach;artificial Intelligence for Glaucoma and Diabetic Retinopathy: A Review;query-Based Text Summarization: A Comparative Investigation and TextRank Implementation;An Acoustic Method for Inspection of CNC Cutting Operations;Enhancing Question Answer Generation from PDFs: A Fusion of BERT, RAKE, T5 and DistilBERT with RQUGE Evaluation;Detecting Fundus Lesions for Diabetic Retinopathy (DR) Analysis.
Withthe increased frequency and intensity of disasters resulting from a changed climate, however, the need for the timely detection of forest fires has grown imperative. In countries like China, deep learning techniq...
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Ayurvedic plants are rich sources of therapeutic compounds, yet accurately identifying the plants posses significant challenges. the introduction of a deep learning approach by researchers had already taken place. A d...
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ISBN:
(纸本)9798350386813;9798350386820
Ayurvedic plants are rich sources of therapeutic compounds, yet accurately identifying the plants posses significant challenges. the introduction of a deep learning approach by researchers had already taken place. A deep learning approach for the automated recognition of Ayurvedic plant species. Our approach strategically combines specialized datasets to create a comprehensive collection covering one or three distinct Ayurvedic leaves. Comprising over 9000 displaying images under diverse and realistic conditions this data set Sequentially supports the testing of CNN and Mobile NetV2 architectures. Convolution neural network for spectral feature extraction and Mobile NetV2 architecture optimized through depthwise separable convolutions and transferred learning enhancing efficiency and accuracy. Comparative analysis reveals that Mobile NetV2 achieves over 81% testing accuracy by surpassing the CNN model by 13% which means CNN model achieves 68%. this underscores the value of transferable representatives in addressing data set complexities. these findings the potential of our approach to reliability distinguish visually similar ayurvedic species, a crucial capability of practical ayurvedic applications while significant progress has been made, further optimizations in architecture tuning and data search augmentation can enhance generalization performance. In somebody hopes can provide so precise and robust platform for unlocking the Ayurvedic treasures of plant species through the integration of machinelearning and botanical heritage.
Withthe advent of the era of big data, the demand for datamining and machinelearning in various fields is increasing. Withthe continuous increase of data scale, the same problem can also be studied through multipl...
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the increasing threat of malware concealed within images through steganography underscores the need for advanced detection methods. Traditional signature-based techniques often fall short as malware evolves rapidly, e...
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In the realm of machinelearning, integrating state-of-the-art technologies is crucial for enhancing the efficacy of applications, particularly in Speech Emotion recognition (SER), where cutting-edge technologies can ...
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A key element of smart manufacturing is condition monitoring and heath controlling of production machines. In today's rapidly evolving landscape of industrial machinery and equipment, optimizing the operation of p...
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A key element of smart manufacturing is condition monitoring and heath controlling of production machines. In today's rapidly evolving landscape of industrial machinery and equipment, optimizing the operation of production lines is critical to ensure high productivity and product quality. Timely detection and prevention of faults in the production process plays a crucial role in minimizing downtime, reducing costs, and ensuring optimal performance. the scientific challenge here is that the increasing number of sensors and actuators with digital input and output signals in the production machines creates different patterns, which are difficult to evaluate using conventional statistical methods. Another difficulty is identifying the cause of the failure to be able to intervene rapidly and in a focused manner in the event of irregularities. For this reason, this research study presented a comprehensive analysis of anomaly detection in binary time series data using various machinelearning models. the study included preprocessing of the dataset, normalizing the data, and evaluation of the anomaly detection performance of the different models. the accuracy, detection rate, and F1-score are used as evaluation measures. the execution time of each model is also analyzed. In addition, the identification of sensors that cause anomalies is investigated and the impact of false detections is discussed. Experimental results show the strengths and weaknesses of each model and provide valuable insights for selecting the appropriate anomaly detection approach. the Isolation Forest, Local Outlier Factor, DBSCAN, and kMeans models show high precision and detection, while the Autoencoder and Variational Autoencoder models show high precision but lower detection. the one-class Support Vector machine model achieves balanced performance. AutoML shows excellent results in recognition rate but is not real-time capable. the results highlight the trade-offs between performance and computation
In today's world of globalized commerce, cross-market recommendation systems (CMRs) are crucial for providing personalized user experiences across diverse market segments. However, traditional recommendation algor...
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ISBN:
(纸本)9798400716379
In today's world of globalized commerce, cross-market recommendation systems (CMRs) are crucial for providing personalized user experiences across diverse market segments. However, traditional recommendation algorithms have difficulties dealing with market specificity and data sparsity, especially in new or emerging markets. In this paper, we propose the CrossGR model, which utilizes Graph Isomorphism Networks (GINs) to improve CMR systems. It outperforms existing benchmarks in NDCG@10 and HR@10 metrics, demonstrating its adaptability and accuracy in handling diverse market segments. the CrossGR model is adaptable and accurate, making it well-suited for handling the complexities of cross-market recommendation tasks. Its robustness is demonstrated by consistent performance across different evaluation timeframes, indicating its potential to cater to evolving market trends and user preferences. Our findings suggest that GINs represent a promising direction for CMRs, paving the way for more sophisticated, personalized, and context-aware recommendation systems in the dynamic landscape of global e-commerce.
Biometric authentication systems have gained popularity as the necessity for secure individual identification has expanded. Iris, hand geometry, fingerprints, retina, vein patterns on the fingers and palms, and voice ...
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