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Automatic lane detection to help the driver is an issue considered for the advancement of Advanced Driver Assistance Systems (ADAS) and a high level of application frameworks because of its importance in drivers and passerby safety in vehicular streets. But still, now it is a most challenging problem because of some factors that are faced by lane detection systems like as vagueness of lane patterns, perspective consequence, low visibility of the lane lines, shadows, incomplete occlusions, brightness and light reflection. The proposed system detects the lane boundary lines using computer vision-based technologies. In this paper, we introduced a system that can efficiently identify the lane lines on the smooth road surface. Gradient and HLS thresholding are the central part to detect the lane lines. We have applied the Gradient and HLS thresholding to identify the lane line in binary images. The color lane is estimated by a sliding window search technique that visualizes the lanes. The performance of the proposed system is evaluated on the KITTI road dataset. The experimental results show that our proposed method detects the lane on the road surface accurately in several brightness conditions.

Research on spam email filtering is drawing experts from all over the world, as these junk email messages continue to affect people's daily lives, whether consciously or unconsciously. The overwhelming use of irritating, destructive, and misleading emails appears to have damaged the values of email which prompted us to perform this research to construct a model for spam filtering with faster training time and enhanced accuracy. We have proposed two voting architectures built upon machine learning models and ensemble classifiers, respectively. In our work, we have also analyzed the performance of several individually applied classifiers and ensemble techniques with various feature retrieval strategies. Additionally, we have compared the training time of the proposed models with the deep LSTM-CNN hybrid model. Both of our suggested models have performed adequately, while the MLbased voting model (Type 1) produces the most accurate filtering (98%) taking bag of words for feature extraction and can be trained above 200 times faster than the LSTM-CNN model.

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