The electronic and electrical industrial sector is exponentially growing throughout the globe, and sometimes, these wastes are being disposed of and discarded with a faster rate in comparison to the past era due to technology advancements. As the application of electronic devices is increasing due to the digitalization of the world (IT sector, medical, domestic, etc.), a heap of discarded e-waste is also being generated. Per-capita e-waste generation is very high in developed countries as compared to developing countries. Expansion of the global population and advancement of technologies are mainly responsible to increase the e-waste volume in our surroundings. E-waste is responsible for environmental threats as it may contain dangerous and toxic substances like metals which may have harmful effects on the biodiversity and environment. Furthermore, the life span and types of e-waste determine their harmful effects on nature, and unscientific practices of their disposal may elevate the level of threats as observed in most developing countries like India, Nigeria, Pakistan, and China. In the present review paper, many possible approaches have been discussed for effective e-waste management, such as recycling, recovery of precious metals, adopting the concepts of circular economy, formulating relevant policies, and use of advance computational techniques. On the other hand, it may also provide potential secondary resources valuable/critical materials whose primary sources are at significant supply risk. Furthermore, the use of machine learning approaches can also be useful in the monitoring and treatment/processing of e-wastes. HIGHLIGHTS: In 2019, ~ 53.6 million tons of e-wastes generated worldwide. Discarded e-wastes may be hazardous in nature due to presence of heavy metal compositions. Precious metals like gold, silver, and copper can also be procured from e-wastes. Advance tools like artificial intelligence/machine learning can be useful in the management of e-wastes. 

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Concepts of circular economy for sustainable management of electronic wastes: challenges and management options

Drowsiness has become one of the major causes of road accidents now-a-days. In order to alleviate this issue, a system has been developed, which uses electroencephalogram (EEG) signals to detect drowsiness with sufficient reliability. This experiment was conducted on a small population and the EEG signals were acquired using a 14-channel wireless headset, while they were in a virtual driving environment. To extract the eye closures, the EEG signal was segmented, and pre-processed. Further the scalograms which describes the time-frequency characteristics of these segments were taken. Pretrained Convolutional Neural Network based architectures viz. ResNet-152, ResNet101, VGG16, VGG19, AlexNet were used to distinguish three states of the driver namely “Sleepy or Drowsy”, “Asleep” and “Awake”.

A Comparative Study of Drowsiness Detection From Eeg Signals Using Pretrained CNN Models

Drowsiness poses a serious challenge to road safety and various in-cabin sensing technologies have been experimented with to monitor driver alertness. Cameras offer a convenient means for contactless sensing, but they may violate user privacy and require complex algorithms to accommodate user (e.g., sunglasses) and environmental (e.g., lighting conditions) constraints. This paper presents a lightweight convolution neural network that measures eye closure based on eye images captured by a wearable glass prototype, which features a hot mirror-based design that allows the camera to be installed on the glass temples. The experimental results showed that the wearable glass prototype, with the neural network in its core, was highly effective in detecting eye blinks. The blink rate derived from the glass output was highly consistent with an industrial gold standard EyeLink eye-tracker. As eye blink characteristics are sensitive measures of driver drowsiness, the glass prototype and the lightweight neural network presented in this paper would provide a computationally efficient yet viable solution for real-world applications.

/pdf/a-cnn-based-wearable-system-for-driver-drowsiness-detection-1zcudwv7.pdf

A CNN-Based Wearable System for Driver Drowsiness Detection

Hyperparameter-tuned batch-updated stochastic gradient descent: Plant species identification by using hybrid deep learning

5G or ZTE the latest improvement to the existing 4G communication standard. These technologies could be evaluated by various metrics called performance indicators among which throughput plays a major role. Throughput is the measure of the rate of data transferred to the device. Higher the throughput, better is the performance of the network. This work models and analyses the throughput obtained with the variations observed on the identified parameters on which it depends on. Here the problem is analysed as a regression problem and hence regressor models are applied. Multiple models ranging from statistical to probabilistic and machine learning to deep recurrent networks are analysed with a 10 fold cross validation. Also, the effect of dimensionality reduction is applied to the dataset and the performance is observed. It is noticed from the work that the top performing models are consistent in performance measured using the regression metrics. 

Throughput Analysis with Effect of Dimensionality Reduction on 5G Dataset using Machine Learning and Deep Learning Models

Forewarning drowsy drivers can reduce the number of road accidents. A non-intrusive drowsiness detection system is implemented, which alerts the driver on the onset of drowsiness. A Pi camera module attached to Raspberry Pi is used to acquire and process the live video of the driver. Haar face detector in OpenCV is used for face detection followed by 68 points of facial landmark identification. Eye and Mouth Aspect Ratios, blink rate and yawning rate are the features extracted. Drowsiness detection is done using two methodologies viz. a threshold-based one and the other, employing artificial intelligence. The machine learning techniques used are LDA and SVM. Feedback is provided as an alarm if a driver is found to be drowsy. The analysis shows that machine learning-based techniques viz. LDA and SVM outperform threshold technique for the dataset considered.

Driver Drowsiness Detection System Using Conventional Machine Learning

Taylor-student psychology based optimization integrated deep learning in IoT application for plant disease classification

Because of the rapid population growth and growing interest in food in India, agriculture plays an important role. As a result, it is necessary to boost harvest yield. An infection caused by microorganisms, infection, and organisms is a serious cause of low collect yield. The main objective of this study is to identify plant diseases. Each leaves have a minute diseases like bacterial and fungal disease which can decrease the harvest yield. This can be done by incorporating Convolutional neural network methodology for detecting the plant diseases. This could be processed by capturing digital images for making the technique more reliable. Using the idea of machine learning, PyTorch can be used to separate the plant's leaves from the rest of the plant. While using Convolutional neural network (CNN), detection of the plant disease can be found effectively as it gives the high accuracy of about 96.1 % to cure the infection of the plant disease.

Ankisetty Balaji

Papers

Driver Drowsiness Detection System Using Conventional Machine Learning

Taylor-student psychology based optimization integrated deep learning in IoT application for plant disease classification

Plant Diseases Detection Based on Convolutional Neural Networks (CNN)