A State-of-the-Art Review of Time Series Forecasting Using Deep Learning Approaches

Time series forecasting has recently emerged as a crucial study area with a wide spectrum of real-world applications. The complexity of data processing originates from the amount of data processed in the digital world. Despite a long history of successful time-series research using classic statistical methodologies, there are some limits in dealing with an enormous amount of data and non-linearity. Deep learning techniques effectually handle the complicated nature of time series data. The effective analysis of deep learning approaches like Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long short-term memory (LSTM), Gated Recurrent Unit (GRU), Autoencoders, and other techniques like attention mechanism, transfer learning, and dimensionality reduction are discussed with their merits and limitations. The performance evaluation metrics used to validate the model's accuracy are discussed. This paper reviews various time series applications using deep learning approaches with their benefits, challenges, and opportunities

Real time Face Detection

Face detection puts forth a challenging problem in the field of Computer Vision. Detecting faces in real time have many applications especially in the safety and security domain. The primary aim of Face detection algorithms is to detect faces in an image or in a video stream successfully so that they could be further used for applications such as Face recognition. Object detection systems developed traditionally have had a lot of success in detecting objects such as humans, cars, buildings etc. but real time face detection presents a new challenge. In this paper, we provide efficient and robust face detection algorithms which adapt to real-world situations like the face being in poor light conditions, multiple faces in a window, pose, different orientations, varied expressions, to detect faces with maximum accuracy

Cytotoxicity of aluminium oxide nanoparticles towards fresh water algal isolate at low exposure concentrations

The growing commercial applications had brought aluminium oxide nanoparticles under,toxicologists' purview. In the present study, the cytotoxicity of two different sized aluminium oxide nanoparticles (ANP(1), mean hydrodynamic diameter 82.6 +/- 22 nm and ANP(2), mean hydrodynamic diameter 246.9 +/- 39 nm) towards freshwater algal isolate Chlorella ellipsoids at low exposure levels (<= 1 mu g/mL) using sterile lake water as the test medium was assessed. The dissolution of alumina nanoparticles and consequent contribution towards toxicity remained largely unexplored owing to its presumed insoluble nature. Herein, the leached Al3+ ion mediated toxicity has been studied along with direct particulate toxicity to bring out the dynamics of toxicity through colloidal stability, biochemical, spectroscopic and microscopic analyses. The mean hydrodynamic diameter increased with time both for ANP(1) 82.6 +/- 22 nm (0 h) to 246.3 +/- 59 nm (24h), to 1204 +/- 140 nm (72 h)] and ANP(2) 246.9 +/- 39 nm (Oh) to 368.28 +/- 48 nm (24 h), to 1225.96 +/- 186 nm (72 h)] signifying decreased relative abundance of submicron sized particles (<1000 nm). The detailed cytotoxicity assays showed a significant reduction in the viability dependent on dose and exposure. A significant increase in ROS and LDH levels were noted for both ANPs at 1 mu g/mL concentration. The zeta potential and FT-IR analyses suggested surface chemical interaction between nanoparticles and algal cells. The substantial morphological changes and cell wall damage were confirmed through microscopic analyses (SEM, TEM, and CLSM). At 72 h, significant Al3+ ion release in the test medium 0.092 mu g/mL for ANP(1), and 0.19 mu g/mL for ANP(2)] was noted, and the resulting suspension containing leached ions caused significant cytotoxicity, revealing a substantial ionic contribution. This study indicates that both the nano-size and ionic dissolution play a significant role in the cytotoxicity of ANPs towards freshwater algae, and the exposure period largely determines the prevalent mode of nano-toxicity