Submission Deadline: Friday 28 Apr 2023
Conference Dates: Sep 17, 2023 - Sep 20, 2023
Impact Score 3.00
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International Workshop on Machine Learning for Signal Processing primarily tackles Artificial intelligence, Pattern recognition, Algorithm, Machine learning and Speech recognition. The work tackled in International Workshop on Machine Learning for Signal Processing goes beyond the discipline of Artificial intelligence as it also encompasses Computer vision. Issues in Pattern recognition were discussed, taking into consideration concepts from other disciplines like Contextual image classification and Cluster analysis.
The conference focuses on Algorithm but the discussions also offer insight into other areas such as Mathematical optimization, Gaussian process and Blind signal separation. The in-depth study on Blind signal separation also explores topics in the intersecting field of Source separation. Many of the studies tackled connect Machine learning with a similar field of study like Data mining.
It dives deep in exploring the relationship between the study of Speech recognition and Electroencephalography. The study on Feature extraction presented in the event intersects with subjects under the field of Feature (computer vision). The Deep learning study featured in it draws connections with the study of Convolutional neural network.
The published articles mainly tackle studies in Artificial intelligence, Pattern recognition, Speech recognition, Machine learning and Algorithm. While Pattern recognition is the focus of the most cited articles, it also provides insights into the studies of Noise measurement, Non-negative matrix factorization, Blind signal separation and Electroencephalography. The published articles hold forums on Speech recognition that merge themes from other disciplines such as Audio signal processing, Mel-frequency cepstrum and Time–frequency analysis.
The conference investigates areas of study like Artificial intelligence, Pattern recognition, Algorithm, Artificial neural network and Feature extraction. The Artificial intelligence study tackled is a key component of adjacent topics in the area of Machine learning. While Pattern recognition is the focus of it, it also provided insights into the studies of Time–frequency analysis, MNIST database, Robustness (computer science) and Spectrogram.
The studies on Algorithm discussed can also contribute to research in the domains of Sparse matrix, Signal processing, Blind signal separation and Time series. International Workshop on Machine Learning for Signal Processing tackles studies in Speech enhancement and the interrelated subject of Signal-to-noise ratio to gain insights into Artificial neural network. Feature extraction research featured in the event incorporates concerns from various other topics such as Network architecture, Feature (machine learning), Feature (computer vision), Contextual image classification and Scatternet.
A key indicator for each conference is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing at International Workshop on Machine Learning for Signal Processing (based on the number of publications) are:
The overall trend for top authors publishing at this conference is outlined below. The chart shows the number of publications at each edition of the conference for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing at International Workshop on Machine Learning for Signal Processing (based on the number of publications) are:
The overall trend for top affiliations publishing at this conference is outlined below. The chart shows the number of publications at each edition of the conference for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions at the conference edition to all articles published within that conference. The best research institutions were selected based on the largest number of articles published during all editions of the conference.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2017 edition, 3.26% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 32.58% were posted by at least one author from the top 10 institutions publishing at the conference. Another 22.47% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 24.72% of all publications and 20.22% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of conferences they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same conference from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the conference in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing at a conference. The index includes the authors publishing at the last edition of a conference, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
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