This study aims to improve the performance of organic to recyclable waste through deep learning techniques. Negative impacts on environmental and Social development have been observed relating to the poor waste segregation schemes. Separating organic waste from recyclable waste can lead to a faster and more effective recycling process. Manual waste classification is a time-consuming, costly, and less accurate recycling process. Automated segregation in the proposed work uses Improved Deep Convolutional Neural Network (DCNN). The dataset of 2 class category with 25077 images is divided into 70% training and 30% testing images. The performance metrics used are classification Accuracy, Missed Detection Rate (MDR), and False Detection Rate (FDR). The results of Improved DCNN are compared with VGG16, VGG19, MobileNetV2, DenseNet121, and EfficientNetB0 after transfer learning. Experimental results show that the image classification accuracy of the proposed model reaches 93.28%.

In recent years, wireless communications have evolved significantly, and many mobile devices have reduced in size. The antennas used in mobile terminals must be lowered in size to fulfil the downsizing standards. Planar antennas, like microstrip and printed antennas, have a low profile, compact size, and conformability to mounting hosts, making them particularly desirable candidates for achieving these needs. Additionally, planar antennas are also being used in communication devices for 2.4 GHz (2400 – 2484 MHz) and 5.2 GHz wireless local area network (WLAN) systems (5150). In wireless applications, an antenna is a crucial component. At the transmitter, it transforms electrical signals into RF signals, and at the receiver, it converts RF signals to electrical signals. The patch inside the antenna is made of a conducting material such as Cu (Copper) or Au (Gold), and it can be rectangular, round, triangular, or elliptical. Two unique designs of microstrip planar antennas with an operating frequency of 5.2 GHz having S11 parameters as -16.0 dB and -15.7 dB have been offered and their performance has been studied in this research article.

This paper presents a terahertz (THz) metamaterial-based nanosensor employing a split ring resonator (SRR) for the detection of NT-proBNP, a cardiac biomarker released in response to increased myocardial pressure and volume overload within the heart. The sensor is designed and simulated in CST Studio to enable real-time detection via changes in the refractive index of NT-proBNP associated with cardiac abnormalities. Validation is performed through equivalent circuit modeling (ECM) using Advanced Design System (ADS). The nanosensor achieves a sensitivity of 1460 GHz/RIU, a Q-factor of 22.06, and a figure of merit (FOM) of 41.71. Assuming minimally invasive placement within the pericardium, signal attenuation is modeled using a path-loss framework that accounts for the serous and fibrous pericardial layers. Transmission line theory is applied to evaluate the intrinsic impedance, reflection coefficients, and attenuation characteristics of THz waves propagating through cardiac tissue. The model estimates the received power at a nanocontroller located at the fibrous layer, and is validated using COMSOL Multiphysics^®. By leveraging refractive index variations induced by NT-proBNP, this nanosensor enables intra-body THz communication as a diagnostic modality. The platform is particularly suited for detecting conditions such as pericarditis, where biomarker fluctuations and pericardial thickening jointly modulate the THz signal.

Vehicular Ad-hoc Networks (VANETs) are the most popular research area of wireless technology. It is offering various types of services such as safety and comfort for drivers as well as for passengers and requires less infrastructure than other technology. VANETs are being used in the concept of making smart cities, and smart traffic management systems all over the world. Due to vulnerability in VANETs, undesired data may attack the network traffic and broadcast false messages in the entire network which causes poor communication among the networks. False data or undesired information is termed malicious data. These malicious data can affect the performance of VANETs it increases vehicle delay, increases fuel consumption and there will be more safety threats. So, there must be a relative study, which will show the variation of network performance metrics with enhancing the number of malicious nodes in the network. This paper had focused to discuss the detection and a deep analysis of affects of malicious nodes on the network performance as throughput, average latency, and packets drop in the network. In this paper, we have discussed a novel approach to detect malicious data with Artificial Neural Networks as well as malicious nodes with Support Vector Machines in VANETs.