Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This brief highlights the association of different performance variables that influences archery performance and the employment of different machine learning algorithms in the identification of potential archers. The sport of archery is often associated with a myriad of performance indicators namely bio-physiological, psychological, anthropometric as well as physical fitness. Traditionally, the determination of potential archers is carried out by means of conventional statistical techniques. Nonetheless, such methods often fall short in associating non-linear relationships between the variables. This book explores the notion of machine learning that is capable of mitigating the aforesaid issue. This book is valuable for coaches and managers in identifying potential archers during talent identification programs.

Performance --- Measurement. --- Engineering. --- Computer simulation. --- Sports-Psychological aspects. --- Biomedical engineering. --- Social sciences --- Computational Intelligence. --- Sport. --- Simulation and Modeling. --- Sport Psychology. --- Biomedical Engineering and Bioengineering. --- Computer Appl. in Social and Behavioral Sciences. --- Data processing. --- Clinical engineering --- Medical engineering --- Bioengineering --- Biophysics --- Engineering --- Medicine --- Computer modeling --- Computer models --- Modeling, Computer --- Models, Computer --- Simulation, Computer --- Electromechanical analogies --- Mathematical models --- Simulation methods --- Model-integrated computing --- Construction --- Industrial arts --- Technology --- Computational intelligence. --- Sports. --- Sports—Psychological aspects. --- Application software. --- Application computer programs --- Application computer software --- Applications software --- Apps (Computer software) --- Computer software --- Field sports --- Pastimes --- Recreations --- Recreation --- Athletics --- Games --- Outdoor life --- Physical education and training --- Intelligence, Computational --- Artificial intelligence --- Soft computing

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book highlights the fundamental association between aquaculture and engineering in classifying fish hunger behaviour by means of machine learning techniques. Understanding the underlying factors that affect fish growth is essential, since they have implications for higher productivity in fish farms. Computer vision and machine learning techniques make it possible to quantify the subjective perception of hunger behaviour and so allow food to be provided as necessary. The book analyses the conceptual framework of motion tracking, feeding schedule and prediction classifiers in order to classify the hunger state, and proposes a system comprising an automated feeder system, image-processing module, as well as machine learning classifiers. Furthermore, the system substitutes conventional, complex modelling techniques with a robust, artificial intelligence approach. The findings presented are of interest to researchers, fish farmers, and aquaculture technologist wanting to gain insights into the productivity of fish and fish behaviour.

Wildlife. --- Fish. --- Computational intelligence. --- Computer simulation. --- Signal processing. --- Image processing. --- Speech processing systems. --- Fish & Wildlife Biology & Management. --- Computational Intelligence. --- Simulation and Modeling. --- Signal, Image and Speech Processing. --- Pictorial data processing --- Picture processing --- Processing, Image --- Imaging systems --- Optical data processing --- Computational linguistics --- Electronic systems --- Information theory --- Modulation theory --- Oral communication --- Speech --- Telecommunication --- Singing voice synthesizers --- Intelligence, Computational --- Artificial intelligence --- Soft computing --- Fish --- Pisces --- Aquatic animals --- Vertebrates --- Fisheries --- Fishing --- Ichthyology --- Computer modeling --- Computer models --- Modeling, Computer --- Models, Computer --- Simulation, Computer --- Electromechanical analogies --- Mathematical models --- Simulation methods --- Model-integrated computing --- Processing, Signal --- Information measurement --- Signal theory (Telecommunication)

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Psychology --- Human biochemistry --- Programming --- Computer architecture. Operating systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Recreation. Games. Sports. Corp. expression --- neuronale netwerken --- fuzzy logic --- medische biochemie --- cybernetica --- psychologie --- sport --- applicatiebeheer --- apps --- mineralen (chemie) --- mijnbouw --- KI (kunstmatige intelligentie) --- architectuur (informatica)

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book highlights the fundamental association between aquaculture and engineering in classifying fish hunger behaviour by means of machine learning techniques. Understanding the underlying factors that affect fish growth is essential, since they have implications for higher productivity in fish farms. Computer vision and machine learning techniques make it possible to quantify the subjective perception of hunger behaviour and so allow food to be provided as necessary. The book analyses the conceptual framework of motion tracking, feeding schedule and prediction classifiers in order to classify the hunger state, and proposes a system comprising an automated feeder system, image-processing module, as well as machine learning classifiers. Furthermore, the system substitutes conventional, complex modelling techniques with a robust, artificial intelligence approach. The findings presented are of interest to researchers, fish farmers, and aquaculture technologist wanting to gain insights into the productivity of fish and fish behaviour.

Fauna. Zoological determination guides --- Fishes --- Electronics --- Engineering sciences. Technology --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- neuronale netwerken --- DIP (documentimage processing) --- digitale signaalverwerking --- beeldverwerking --- fuzzy logic --- cybernetica --- spraaktechnologie --- signal processing --- mineralen (chemie) --- mijnbouw --- KI (kunstmatige intelligentie) --- dieren --- vissen (biologie) --- signaalprocessoren --- signaalverwerking --- AI (artificiële intelligentie)