@article{abdollahiPredictionVarroaMite2026,

title = {On the Prediction of Varroa Mite Infestations in Honey Bee Colonies via Acoustic Monitoring},

author = {M. Abdollahi and Y. Zhu and H. R. Guimãraes and N. Coallier and S. Maucourt and P. Giovenazzo and T. H. Falk},

url = {https://www.scopus.com/pages/publications/105031477347?origin=resultslist},

doi = {10.1109/JSEN.2026.3666127},

issn = {1530437X (ISSN)},

year  = {2026},

date = {2026-01-01},

journal = {IEEE Sensors Journal},

volume = {26},

number = {8},

pages = {12157–12167},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Honey bees play a vital role in global ecosystems and agriculture through their pollination services. However, the Varroa destructor mite remains one of the most damaging parasites affecting honey bee health, contributing significantly to colony decline. Early detection of Varroa infestation is essential for effective beekeeping management. In this study, we propose a machine learning approach for predicting Varroa mite infestation levels based on the acoustic analysis of hive recordings. We introduce a set of nine spectral shape descriptors (SSDs) computed from conventional and modulation spectrograms to characterize the acoustic environment within hives. We evaluate the predictive power of these features against traditional cepstral features across multiple classifiers, including support vector machines (SVMs), random forests (RFs), and k-nearest neighbors (KNNs). Our results demonstrate that the proposed SSDs significantly improve the classification performance relative to conventional features across numerous figures of merit, particularly in hive-independent test settings. This work highlights the potential of spectral acoustic monitoring combined with the supervised learning as a scalable and noninvasive tool for precision beekeeping and early detection of parasitic threats. © 2001-2012 IEEE.},

keywords = {Acoustic measuring instruments, Acoustic monitoring, Acoustic variables measurement, Acoustics, Beehive acoustic, Beehive acoustics, Ecosystems, Food products, Honey bee, honey bees, Honeybee colonies, Learning systems, Machine learning approaches, Modulation spectrogram, Motion compensation, Nearest neighbor search, Parasite-, Random forests, Sanitary sewers, Spectrograms, Spectrographs, Support vector machines, Varroa destructor, Varroa mite infestation},

pubstate = {published},

tppubtype = {article}

}

@article{filali_multi-scale_2016,

title = {Multi-scale salient object detection using graph ranking and global–local saliency refinement},

author = {I. Filali and M. S. Allili and N. Benblidia},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84982091007&doi=10.1016%2fj.image.2016.07.007&partnerID=40&md5=60dabe68b5cff4b5d00216d6a632e1cd},

doi = {10.1016/j.image.2016.07.007},

issn = {09235965},

year  = {2016},

date = {2016-01-01},

journal = {Signal Processing: Image Communication},

volume = {47},

pages = {380–401},

publisher = {Elsevier B.V.},

abstract = {We propose an algorithm for salient object detection (SOD) based on multi-scale graph ranking and iterative local–global object refinement. Starting from a set of multi-scale image decompositions using superpixels, we propose an objective function which is optimized on a multi-layer graph structure to diffuse saliency from image borders to salient objects. This step aims at roughly estimating the location and extent of salient objects in the image. We then enhance the object saliency through an iterative process employing random forests and local boundary refinement using color, texture and edge information. We also use a feature weighting scheme to ensure optimal object/background discrimination. Our algorithm yields very accurate saliency maps for SOD while maintaining a reasonable computational time. Experiments on several standard datasets have shown that our approach outperforms several recent methods dealing with SOD. © 2016 Elsevier B.V.},

note = {Publisher: Elsevier B.V.},

keywords = {Algorithms, Boundary information, Decision trees, Feature relevance, Iterative methods, Multi-layer graphs, Object detection, Object recognition, Random forests, Salient object detection},

pubstate = {published},

tppubtype = {article}

}

@article{filaliMultiscaleSalientObject2016,

title = {Multi-scale salient object detection using graph ranking and global–local saliency refinement},

author = {I. Filali and M. S. Allili and N. Benblidia},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84982091007&doi=10.1016%2fj.image.2016.07.007&partnerID=40&md5=60dabe68b5cff4b5d00216d6a632e1cd},

doi = {10.1016/j.image.2016.07.007},

issn = {09235965},

year  = {2016},

date = {2016-01-01},

journal = {Signal Processing: Image Communication},

volume = {47},

pages = {380–401},

publisher = {Elsevier B.V.},

abstract = {We propose an algorithm for salient object detection (SOD) based on multi-scale graph ranking and iterative local–global object refinement. Starting from a set of multi-scale image decompositions using superpixels, we propose an objective function which is optimized on a multi-layer graph structure to diffuse saliency from image borders to salient objects. This step aims at roughly estimating the location and extent of salient objects in the image. We then enhance the object saliency through an iterative process employing random forests and local boundary refinement using color, texture and edge information. We also use a feature weighting scheme to ensure optimal object/background discrimination. Our algorithm yields very accurate saliency maps for SOD while maintaining a reasonable computational time. Experiments on several standard datasets have shown that our approach outperforms several recent methods dealing with SOD. © 2016 Elsevier B.V.},

keywords = {Algorithms, Boundary information, Decision trees, Feature relevance, Iterative methods, Multi-layer graphs, Object detection, Object recognition, Random forests, Salient object detection},

pubstate = {published},

tppubtype = {article}

}