The proceedings contain 22 papers. The special focus in this conference is on Artificial Intelligence and Green Computing. The topics include: Maximization of Lifetime in Wireless Sensor Networks Using pattern Search ...
ISBN:
(纸本)9783031465833
The proceedings contain 22 papers. The special focus in this conference is on Artificial Intelligence and Green Computing. The topics include: Maximization of Lifetime in Wireless Sensor Networks Using pattern Search Algorithm;task Deadline-Based Computation Offloading Algorithm for Service Time Minimization in Mobile Edge Computing;detection of Web-Based Attacks using Tree-Based learning Models: An Evaluation study;energy Minimization in Wireless Sensor Networks Based Bio-Inspired Algorithms;Heuristic Optimization of the LEACH Routing Protocol in Wireless Sensor Networks;toward Understanding the Impact of Demographic Factors on Cybersecurity Awareness in the Moroccan Context;health Data Security in a Big Data Environment;Handling the ED Problem Using a DTSA in Smart Grids;Multivariate Time Series Data Prediction Based on Social Sentiments Community and LstM Method (S-S-LstM);longitudinal study of the Thyroid Surgery Effect Based on Computer Vision;improving Wheat Yield Estimating by Using Satellites Data and machinelearning—Deep learning Algorithm-In Morocco;online Process Mining: A Systematic Literature Review;machinelearning with Nighttime Lights to Predict Morocco’s Gross Domestic Product;pectoral Muscle Segmentation Using Mammogram images in Medio Lateral Oblique View;a Nearest Neighbor-Based Hamiltonian Clustering Algorithm;an Acoustic Analysis of Voice Before and After Thyroidectomy;estimation of Water Turbidity by image-Based learning Approaches;an image Compression Approach Based on Convolutional AutoEncoder;offline Writer Identification Based on Diagonal Gradient Angle of Small Fragments;Enhanced Aircraft Time Delay Prediction Using Weighted Hybrid ML and Dimensionality Reduction.
Chronic Kidney diseases are increasing at an alarming rate in present days. The prediction of kidney disease consume more time as it involves a lot of laboratory tests. Traditionally, the health care domain relies on ...
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Scene-Text Visual Question Answering (Scene-Text VQA) is an emerging research area that combines computer vision, natural language processing, and scene understanding. The goal of Scene-Text VQA is to develop models a...
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Multifarious approaches exist for data preprocessing in machinelearning (ML), patternrecognition, and data mining, and feature selection (FS) is regarded as the most efficacious technique for dimensionality reductio...
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ISBN:
(纸本)9798331529246
Multifarious approaches exist for data preprocessing in machinelearning (ML), patternrecognition, and data mining, and feature selection (FS) is regarded as the most efficacious technique for dimensionality reduction in a structured way. The existence of irrelevant, redundant, and disproportionate features directly diminishes the model's performance and dramatically expands its complexity. Distinguishable FS algorithms can outperform others, and the most influential aspect of a well-performing FS algorithm is discovering momentous features that help optimize model performance by processing data more efficiently. Therefore, this study introduces a novel FS technique based on the joint mutual information (FSJMI) technique for life science data. This technique declines the number of features and enhances the model's performance, or at least maintains it in most cases, making it a unique and promising approach. It also enriches predictive accuracy while minimizing computational overhead by substituting the correlation between the features and the correlation substitution of the class values with the features. Comprehensive experiments utilizing ten diverse life science datasets have been conducted to validate the suggested model's performance. The experimental results demonstrate a substantial improvement in the proposed techniques compared to other well-known FS techniques and no selection (NoSel) in the primary dataset. The results conveyed using widely used ML algorithms such as Decision Tree (DT), K Nearest Neighbour (KNN), Naive Bayes (NB) Classifier, and Logistic Regression (LR) reveal that the presented algorithm demonstrated superior performance and outperformed NoSel and other FS algorithms in mean accuracy by at least 2.7%, 3.466%, 2.038%, and 1.792%, respectively. Additionally, regarding the LR model, the proposed algorithm outperforms one of the FS models by at most 11.631%. These findings of the proposed algorithm regarding outcomes highlight its capabili
In the rapidly evolving field of medical imaging, machinelearning algorithms have become indispensable for enhancing diagnostic accuracy. However, the effectiveness of these algorithms is contingent upon the availabi...
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ISBN:
(数字)9783031449925
ISBN:
(纸本)9783031449918;9783031449925
In the rapidly evolving field of medical imaging, machinelearning algorithms have become indispensable for enhancing diagnostic accuracy. However, the effectiveness of these algorithms is contingent upon the availability and organization of high-quality medical imaging datasets. Traditional Digital Imaging and Communications in Medicine (DICOM) data management systems are inadequate for handling the scale and complexity of data required to be facilitated in machinelearning algorithms. This paper introduces an innovative data curation tool, developed as part of the Kaapana (https://***/kaapana/ kaapana) open-source toolkit, aimed at streamlining the organization, management, and processing of large-scale medical imaging datasets. The tool is specifically tailored to meet the needs of radiologists and machinelearning researchers. It incorporates advanced search, auto-annotation and efficient tagging functionalities for improved data curation. Additionally, the tool facilitates quality control and review, enabling researchers to validate image and segmentation quality in large datasets. It also plays a critical role in uncovering potential biases in datasets by aggregating and visualizing metadata, which is essential for developing robustmachinelearning models. Furthermore, Kaapana is integrated within the Radiological Cooperative Network (RACOON), a pioneering initiative aimed at creating a comprehensive national infrastructure for the aggregation, transmission, and consolidation of radiological data across all university clinics throughout Germany. A supplementary video showcasing the tool's functionalities can be accessed at https://***/MICCAI- DEMI2023.
Skin diseases are very common and the diagnosis is tricky and challenging. Latest research in the field of medicine along with the help of advanced technology has proved to be quite useful not only in diagnosis but al...
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Microbes are tiny living organisms beyond the scope to be seen by the naked eye that are coexisting all around the biosphere along with other animals. Significant identification of the microbes from the elementary for...
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Meteorological satellites are widely used for collecting information about the atmosphere. Due to the indirect relation between satellite data and measurements of meteorological parameters, mathematical models, especi...
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ISBN:
(纸本)9789811965814;9789811965807
Meteorological satellites are widely used for collecting information about the atmosphere. Due to the indirect relation between satellite data and measurements of meteorological parameters, mathematical models, especially those based on artificial intelligence, have been developed for meteorological modeling. Indeed, in recent years, machinelearning has enabled fundamental advances in the modeling of random systems. In this context, we will show the contribution of techniques based on artificial intelligence in the estimation of precipitation. Based on the expertise of our research laboratories in this field, the objective of this paper is to present our recent results and developments using machinelearning, such as ANN, SVM, and RF. For the classification and estimation of rainfall intensities, satellite images were used for the implementation of these techniques. The training and validation was carried out by comparing the satellite images to the corresponding radar images. The results of these artificial intelligence-based techniques indicate very interesting performance.
With the ever-rising risk of phishing attacks, which capitalize on vulnerable human behavior in the contemporary digital space, requires new cybersecurity methods. This literary work contributes to the solution by nov...
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The field-programmable gate array (FPGA) offers an effective solution to meet the high-performance requirements of real-time digital signal processors. IP cores developed on FPGAs benefit from the programmable logic&#...
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