Investigating attention mechanisms for plant disease identification in challenging environments.

There is an increasing demand for efficient and precise plant disease detection methods that can quickly identify disease outbreaks. For this, researchers have developed various machine learning and image processing techniques. However, real-field images present challenges due to complex backgrounds, similarities between different disease symptoms, and the need to detect multiple diseases simultaneously. These obstacles hinder the development of a reliable classification model. The attention mechanisms emerge as a critical factor in enhancing the robustness of classification models by selectively focusing on relevant regions or features within infected regions in an image. This paper provides details about various types of attention mechanisms and explores the utilization of these techniques for the machine learning solutions created by researchers for image segmentation, feature extraction, object detection, and classification for efficient plant disease identification. Experiments are conducted on three models: MobileNetV2, EfficientNetV2, and ShuffleNetV2, to assess the effectiveness of attention modules. For this, Squeeze and Excitation layers, the Convolutional Block Attention Module, and transformer modules have been integrated into these models, and their performance has been evaluated using different metrics. The outcomes show that adding attention modules enhances the original models' functionality.

Metamaterial foundation for seismic wave attenuation for low and wide frequency band.

Metamaterials are periodic structures made by repeating a unit cell. Such a structure shows frequency-specific wave attenuation behaviour. In this work, a 2D metamaterial foundation is proposed for the seismic protection of buildings. The paramount challenge is to offer low frequency attenuation (~ 2-8 Hz), which is the dominant excitation during an earthquake. Based on the parametric study performed, a new type of metamaterial structure was proposed. It was found that the foundation consisting of repeating circular scatterers made of steel and plumbum embedded in rubber matrix can provide low and wide frequency wave attenuation from 2.6 to 7.8 Hz. The computational model of the structure was subjected to transient excitation against three pre-recorded earthquake excitations. The result showed that the novel foundation can resist the propagation of the seismic wave to the structure. Further, the response of a 2D building frame with metamaterial foundation was compared to a concrete foundation exposed to different earthquake excitations. The results are very promising as the frame vibration on the metamaterial foundation was significantly less than the same frame on the concrete foundation. The presented work opens the path to new research and development of seismic metamaterial foundation for earthquake attenuation.

An Ensemble-Based Multiclass Classifier for Intrusion Detection Using Internet of Things.

Internet of Things (IoT) is the fastest growing technology that has applications in various domains such as healthcare, transportation. It interconnects trillions of smart devices through the Internet. A secure network is the basic necessity of the Internet of Things. Due to the increasing rate of interconnected and remotely accessible smart devices, more and more cybersecurity issues are being witnessed among cyber-physical systems. A perfect intrusion detection system (IDS) can probably identify various cybersecurity issues and their sources. In this article, using various telemetry datasets of different Internet of Things scenarios, we exhibit that external users can access the IoT devices and infer the victim user's activity by sniffing the network traffic. Further, the article presents the performance of various bagging and boosting ensemble decision tree techniques of machine learning in the design of an efficient IDS. Most of the previous IDSs just focused on good accuracy and ignored the execution speed that must be improved to optimize the performance of an ID model. Most of the earlier pieces of research focused on binary classification. This study attempts to evaluate the performance of various ensemble machine learning multiclass classification algorithms by deploying on openly available "TON-IoT" datasets of IoT and Industrial IoT (IIoT) sensors.

A Real-Time Crowd Monitoring and Management System for Social Distance Classification and Healthcare Using Deep Learning.

Coronavirus born COVID-19 disease has spread its roots in the whole world. It is primarily spread by physical contact. As a preventive measure, proper crowd monitoring and management systems are required to be installed in public places to limit sudden outbreaks and impart improved healthcare. The number of new infections can be significantly reduced by adopting social distancing measures earlier. Motivated by this notion, a real-time crowd monitoring and management system for social distance classification is proposed in this research paper. In the proposed system, people are segregated from the background using the YOLO v4 object detection technique, and then the detected people are tracked by bounding boxes using the Deepsort technique. This system significantly helps in COVID-19 prevention by social distance detection and classification in public places using surveillance images and videos captured by the cameras installed in these places. The performance of this system has been assessed using mean average precision (mAP) and frames per second (FPS) metrics. It has also been evaluated by deploying it on Jetson Nano, a low-cost embedded system. The observed results show its suitability for real-time deployment in public places for COVID-19 prevention by social distance monitoring and classification.

Enhancing the sound transmission loss through acoustic double panel using sonic crystal and porous material.

Acoustic panels are widely used for sound insulation in various applications. Sound transmission loss (STL) through the panel is due to a change in acoustic impedance as sound travels from one medium to another. In double panels, STL further increases due to multiple reflections in air cavity. Recently the sonic crystal (SC) has emerged as an interesting research topic which provides sound attenuation in specific frequency bands. The present paper aims at combining the property of a SC with the acoustic panel for enhancing the STL through the double panel. Initially, an analytical method is developed to obtain the STL through the double panel. Further finite element (FE) simulations are performed using acoustic structure interaction to obtain the STL through the double panel which is in good agreement with the analytical predictions. The SC, along with the double panel, is analyzed using the FE method for the combined effect of both sound attenuators. Further, glass wool is considered as a filler material between the double panel as well as between the double panel and the SC assembly. It is found that the combined structure of the double panel and the SC with glass wool as filler gives the best STL for all different cases for the same external dimensions.