Illegal logging detection based on acoustic surveillance of forest

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. In this article, we present a framework for automatic detection of logging activity in forests using audio recordings. The framework was evaluated in terms of logging detection classification performance and various widely used classification methods and algorithms were tested. Experimental setups, using different ratios of sound-to-noise values, were followed and the best classification accuracy was reported by the support vector machine algorithm. In addition, a postprocessing scheme on decision level was applied that provided an improvement in the performance of more than 1%, mainly in cases of low ratios of sound-to-noise. Finally, we evaluated a late-stage fusion method, combining the postprocessed recognition results of the three top-performing classifiers, and the experimental results showed a further improvement of approximately 2%, in terms of absolute improvement, with logging sound recognition accuracy reaching 94.42% when the ratio of sound-to-noise was equal to 20 dB

Facial Expression Recognition Using Adaptive Neuro-fuzzy Inference Systems

Facial expressions form a universal language of emotions, which can instantly express a wide range of emotional states and feelings. The analysis of facial expressions and the accurate recognition of their emotional content are highly desired and assistive in a wide spectrum of domains. In this paper, we present a work on the analysis of facial expressions and the recognition of emotions using an approach that is based on adaptive neuro fuzzy inference systems. Initially, given a new image, human faces are detected using the Viola-Jones algorithm. Then, facial expressions are analyzed and facial deformations of specific regions such as eyes, eyebrows and mouth are located and then characteristics such as locations, length, width and shape are extracted. The feature vectors represent the deformations of the facial expression and based on them the emotional content of the facial expressions is recognized using an approach that is based on adaptive neuro fuzzy inference systems. An evaluation study was conducted on the JAFFE database and the results collected were very encouraging indicating that the approach is efficient and accurate in analyzing facial expressions and recognizing their emotional content. © 2018 IEEE

Genotoxic and Antigenotoxic Assessment of Chios Mastic Oil by the In Vitro Micronucleus Test on Human Lymphocytes and the In Vivo Wing Somatic Test on Drosophila

International audienceChios mastic oil (CMO), the essential oil derived from Pistacia lentiscus (L.) var. chia (Duham), has generated considerable interest because of its antimicrobial, anticancer, antioxidant and other beneficial properties. In the present study, the potential genotoxic activity of CMO as well as its antigenotoxic properties against the mutagenic agent mitomycin-C (MMC) were evaluated by employing the in vitro Cytokinesis Block MicroNucleus (CBMN) assay and the in vivo Somatic Mutation And Recombination Test (SMART). In the in vitro experiments, lymphocytes were treated with 0.01, 0.05 and 0.10% (v/v) of CMO with or without 0.05 μg/ml MMC, while in the in vivo assay Drosophila larvae were fed with 0.05, 0.10, 0.50 and 1.00% (v/v) of CMO with or without 2.50 μg/ml MMC. CMO did not significantly increase the frequency of micronuclei (MN) or total wing spots, indicating lack of mutagenic or recombinogenic activity. However, the in vitro analysis suggested cytotoxic activity of CMO. The simultaneous administration of MMC with CMO did not alter considerably the frequencies of MMC-induced MN and wing spots showing that CMO doesn't exert antigenotoxic or antirecombinogenic action. Therefore, CMO could be considered as a safe product in terms of genotoxic potential. Even though it could not afford any protection against DNA damage, at least under our experimental conditions, its cytotoxic potential could be of interest