Browsing by Author "Lounnas, Bilal"

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    Crowdsensing monitoring
    (University of Mohamed Boudiaf, M’sila, 2024-06) Makri, Leyla; Khelifi, Zeyneb; Lounnas, Bilal
    The collaborative of crowdsensing and advanced sensing technologies can vastly improve environmental and infrastructure monitoring in cities through creative applications. Novel use cases tracked important metrics like air quality, noise levels and traffic patterns to provide communities with valuable data. In this work, propose a Kinect sensor that leverage computer vision for detecte obstacles in the roads.with adopt YOLO-v8 as obstacle detector,added a series of methods proposed for road obstacle detection, like a self-driving car system and Kinect approach that effectively mapped hazards in 3D for navigation safety. Based on those methodes we are going to integrated to generate system in the futures. Results indicated addressing limitations of traditional solutions by enabling timely, comprehensive data collection, bringing significant benefits in safety, costs, transportation resilience and urban management. Overall,this work is helping advance the potential of collaborative digital tools to revolutionize smarter, safer mobility for all through continuous learning.
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    Discovery and extraction of motifs and/or profiles in biological sequences.
    (Université de M'sila, 2016-01-21) Lounnas, Bilal
    Since the discovery of DNA sequencing by Frederick Sanger in the second half of the 70s, the volume of biological sequences since that has increased exponentially due to the advanced of computer technology, has brought to existence a new research area, bioinformatics. In short, bioinformatics attempts to conceptualize biology in terms of molecules (in the sense of physical-chemistry) and applies informatics techniques to understand and organize the information associated with these molecules on a large scale. The biggest challenge remains to overcome, is the analysis and extraction of knowledge from these data repositories. Indeed, these databases constitute the genetic heritage of all humanity, like the human genome and different sequences of plant and animal biological species identified so far, and this is one of the most important challenges in bioinformatics is the discovery of motifs in biological sequences in order to define the function or the family of biochemical molecules (DNA, RNA, and Protein). Since this challenge depends on analyzing textual data, pattern matching algorithms are a suitable candidate to tackle this problem. In this thesis we designed a novel motif discovery algorithm to meet the demand for finding motifs over biological sequences using pushdown automata as a mechanism of matching process alongside with a counter in an optimistic way.