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This paper presents a comparison of Electroencephalogram (EEG) signals classification for Brain Computer-Interfaces (BCI). At present, it is a challenging task to extract the meaningful EEG signal patterns from a large volume of poor quality data and simultaneously with the presence of artifacts noises. Selection of the effective classification technique of the EEG signals at classification stage...
The prevention of any type of cyber attack is indispensable because a single attack may break the security of computer and network systems. The hindrance of such attacks is entirely dependent on their detection. The detection is a major part of any security tool such as intrusion detection system (IDS), intrusion prevention system (IPS), adaptive security alliance (ASA), check points and firewalls...
Farmer needs alternatives for weed control due to the desire to reduce chemicals used in farming. However, conventional mechanical cultivation cannot selectively remove weeds and there are no selective herbicides for some weed situation. Since hand labor is costly, an automated weed control system could be feasible. A robotic weed control system can also reduce or eliminate the need for chemicals...
The identification and classification of weeds are of major technical and economical importance in the agricultural industry. To automate these activities, like in shape, color and texture, weed control system is feasible. The goal of this paper is to build a real-time, machine vision weed control system that can detect weed locations. The algorithm is developed to classify images into broad and narrow...
This paper deals with the development of an algorithm for real time specific weed recognition system based on Sample Variance of an image that is used for the weed classification and comparison of its result with the algorithm based on population variance. The population variance has been used before for weed classification. The processing time for calculating population variance and sample variance...
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