Real-time human detection for electricity conservation using pruned-SSD and arduino

Electricity conservation techniques have gained more importance in recent years. Many smart techniques are invented to save electricity with the help of assisted devices like sensors. Though it saves electricity, it adds an additional sensor cost to the system. This work aims to develop a system that manages the electric power supply, only when it is actually needed i.e., the system enables the power supply when a human is present in the location and disables it otherwise. The system avoids any additional costs by using the closed circuit television, which is installed in most of the places for security reasons. Human detection is done by a Modified-single shot detection with a specific hyperparameter tuning method. Further the model is pruned to reduce the computational cost of the framework which in turn reduces the processing speed of the network drastically. The model yields the output to the Arduino micro-controller to enable the power supply in and around the location only when a human is detected and disables it when the human exits. The model is evaluated on CHOKEPOINT dataset and real-time video surveillance footage. Experimental results have shown an average accuracy of 85.82% with 2.1 seconds of processing time per frame

An Effective Dual Level Flow Optimized AlexNet-BiGRU Model for Intrusion Detection in Cloud Computing

In recent years, several existing techniques have been developed to solve security issues in cloud systems. The proposed study intends to develop an effective deep-learning mechanism for detecting network intrusions. The proposed study involves three stages pre-processing, feature selection and classification. Initially, the available noises in the input data are eliminated by pre-processing via data cleaning, discretization and normalization. The large feature dimensionality of pre-processed data is reduced by selecting optimal features using the wild horse optimization-based feature selection (WHO-FS) model. The selected features are then input into a proposed dual-level flow optimized AlexNet-BiGRU detection model (DLFAB-IDS). Whereas the flow direction algorithm (FDA) approach optimally tunes the hyperparameters and helps to enhance the classification performance. In the proposed model, the intrusions are detected by AlexNet and the multiclass classification is performed through the BiGRU method. The proposed study used the NSL-KDD dataset, and the simulation was done by Python tool. The efficacy of a proposed model is measured by evaluating several performance metrics. The comparison over other existing techniques shows that the proposed model brings higher performance in terms of accuracy 96.81%, recall 95.84%, precision 96.24%, f1-score 96.75%, prediction time 0.43s and training time 152.84s

Improved functionality of roselle (Hibiscus sabdariffa) calyx extract blended Kombucha, a fermented beverage

Kombucha is a fermented drink with a range of medicinal benefits prepared from sweetened tea infusion (Camellia sinensis), which is cultured symbiotically with yeast and acetic acid bacteria. In the present investigation, kombucha was prepared from sugared black tea extract blended with aqueous calyx extract of roselle (Hibiscus sabdariffa) @15% and fermented with cultures viz., Komagataeibacter rhaeticus (NAIMCCTB-3976) and Brettanomyces bruxellensis (CAP9) at 35°C. The floating water insoluble mat of kombucha was observed under a scanning electron microscope, which revealed the cellulosic nanofibrils secreted by K. rhaeticus. The total phenolic and flavanoid content, DPPH, and ABTS activity of roselle calyx blended kombucha were significantly higher than black tea kombucha. Further, the compounds present in kombucha, when analyzed by fourier transform infra-red spectroscopy, denoted the presence of carbonyl compounds, aromatic olefinic compounds, ketones, aldehydes, and esters. The different bioactive metabolites formed during fermentation were elucidated using gas chromatography-mass spectrometry and the major compounds excited within the retention time of 45 min with maximum peak area were 13-hexyloxacyclotridec-10-en-2-one (37.64%), palmitins such as 1,3 dipalmitin (6.42%), glycidyl palmitate (3.30%), organic acids such as undecanedioic acid, linoleic acid, acetic acid (3.88%), etc. The results proved that blending black tea extract with 15% roselle calyx extract as a substrate for kombucha fermentation was highly accepted with an organoleptic score of 95% and improved functional properties compared to black tea extract kombucha alone

Video Saliency Detection by using an Enhance Methodology Involving a Combination of 3DCNN with Histograms

When watching pictures or videos, the Human Visual System has the potential to concentrate on important locations. Saliency detection is a tool for detecting the abnormality and randomness of images or videos by replicating the human visual system. Video saliency detection has received a lot of attention in recent decades, but due to challenging temporal abstraction and fusion for spatial saliency, computational modelling of spatial perception for video sequences is still limited.Unlike methods for detection of salient objects in still images, one of the most difficult aspects of video saliency detection is figuring out how to isolate and integrate spatial and temporal features.Saliency detection, which is basically a tool to recognize areas in images and videos that catch the attention of the human visual system, may benefit multimedia applications such as video or image retrieval, copy detection, and so on. As the two crucial steps in trajectory-based video classification methods are feature point identification and local feature extraction. We suggest a new spatio-temporal saliency detection using an enhanced 3D Conventional neural network with an inclusion of histogram for optical and orient gradient in this paper