Advances in Computer Science for Engineering and Education VII

Volume 1

AvZhengbing Hu,Felix Yanovsky

Häftad, Engelska, 2025

Del 242 i serien Lecture Notes on Data Engineering and Communications Technologies

1 833 kr

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Beskrivning

The book contains high-quality refereed research papers presented at the 7th International Conference on Computer Science, Engineering, and Education Applications (ICCSEEA2024), which took place in Kyiv, Ukraine, on April 27–28, 2024, and was organized by the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute, the National Aviation University, Lviv Polytechnic National University, Kharkiv National University of Radio Electronics, Wuhan University of Technology, Polish Operational and Systems Society, and the International Research Association of Modern Education and Computer Science. The book covers a variety of topics, including cutting-edge research in computer science, artificial intelligence, engineering techniques, smart logistics, and knowledge representation with educational applications. The book is an invaluable resource for academics, graduate students, engineers, management professionals, and undergraduate students who are interested in computer science and its applications in engineering and education.

Produktinformation

Utgivningsdatum:2025-04-03
Mått:155 x 235 x 41 mm
Vikt:1 118 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes on Data Engineering and Communications Technologies
Antal sidor:731
Förlag:Springer International Publishing AG
ISBN:9783031842276

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Teknik: allmänt inom Naturvetenskap och teknik
Databaser inom Data och IT

Innehållsförteckning

Experimental Study and Value Analysis of Green Jadeite Image Acquisition based on HSL Chromaticity Parameters.- Fingerprint Localization Technology of WiFi Signal for Intelligent Parking Space Navigation.- Design of an All-Terrain Unmanned Tracking Model Vehicle.- Accuracy Improvement of Spoken Language Identification System for Close-related Languages.- Evaluation of Machine Learning Models for Static Analysis of Smart Contract Vulnerabilities.