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Zinc ion [Zn(II)] is the one of the most toxic and common among the heavy metal pollutants of industrial effluents. The present investigation has been aimed at describing the synthesis and characterization of biodegradable nanoparticle based on chitosan, then blending with biodegradable synthetic polymer (polyvinyl alcohol, PVA) and which was applied for the removal of Zn(II) ions from the aqueous system. The prepared nanoparticle and the chitosan blended polymers were characterized using transmission electron microscope (TEM), scanning electron microscope (SEM), and X‐ray diffraction (XRD) analyses. The adsorption behaviour of chitosan blended polymer for Zn(II) ions has been studied as a function of solution pH, adsorbent dose, contact time, and adsorbate concentration in a batch system. The maximum adsorption was observed as 99.45% for an initial Zn(II) ions concentration of 10 mg/L for the solution pH of 5.0 and contact time of 30 min and at 5 g/L of adsorbent dose. The adsorption kinetics was investigated by fitting the experimental data to the pseudo‐first order, pseudo‐second order kinetic models, Weber and Morris intraparticle diffusion and Boyd kinetic models. Adsorption equilibrium data were analyzed by the Langmuir and Freundlich adsorption isotherm models and it was found that the Langmuir isotherm model fits best with the experimental data. The desorption of Zn(II) ions from the spent chitosan/PVA blend was carried out by HCl solution. The results of the present study showed the polymer blends may act as an effective adsorbent for the removal of Zn(II) ions from the aqueous solution. © 2014 American Institute of Chemical Engineers Environ Prog, 34: 15–22, 2015

IoT (Internet of Things) usage in industrial and scientific domains is progressively increasing. Currently, IoTs are utilized in numerous applications in different domains, similar to communication technology, environmental monitoring, agriculture, medical services, and manufacturing purposes. But, the IoT systems are vulnerable against various intrusions and attacks in the perspective on the security view. It is essential to create an intrusion detection model to detect and secure the network from different attacks and anomalies that continually happen in the network. In this paper, the anomaly detection model for an IoT network using deep neural networks (DNN) with chicken swarm optimization (CSO) algorithm was proposed. Presently, the DNN has demonstrated its efficiency in different fields that are applicable to its usage. Deep learning is the type of algorithm based on machine learning which used many layers to gradually extricate more significant features of level from the raw inputs. The UNSW-NB15 dataset was utilized to evaluate the anomaly detection model. The proposed model obtained 94.85% accuracy and 96.53% detection rate which is better than other compared techniques like GA-NB, GSO, and PSO for validation. The DNN-CSO model has performed well in detecting most of the attacks, and it is appropriate for detecting anomalies in the IoT network.

Glaucoma is the second most common cause for blindness around the world and the third most common in Europe and the USA. Around 78 million people are presently living with glaucoma (2020). It is expected that 111.8 million people will have glaucoma by the year 2040. 90% of glaucoma is undetected in developing nations. It is essential to develop a glaucoma detection system for early diagnosis. In this research, early prediction of glaucoma using deep learning technique is proposed. In this proposed deep learning model, the ORIGA dataset is used for the evaluation of glaucoma images. The U-Net architecture based on deep learning algorithm is implemented for optic cup segmentation and a pretrained transfer learning model; DenseNet-201 is used for feature extraction along with deep convolution neural network (DCNN). The DCNN approach is used for the classification, where the final results will be representing whether the glaucoma infected or not. The primary objective of this research is to detect the glaucoma using the retinal fundus images, which can be useful to determine if the patient was affected by glaucoma or not. The result of this model can be positive or negative based on the outcome detected as infected by glaucoma or not. The model is evaluated using parameters such as accuracy, precision, recall, specificity, and F-measure. Also, a comparative analysis is conducted for the validation of the model proposed. The output is compared to other current deep learning models used for CNN classification, such as VGG-19, Inception ResNet, ResNet 152v2, and DenseNet-169. The proposed model achieved 98.82% accuracy in training and 96.90% in testing. Overall, the performance of the proposed model is better in all the analysis.