Search

In recent days, stress is a major phenomenon that adversely affects both individuals and communities. The research in computing the stress factor has wider advantages as it improves personal learning, learning operations, and high productivity that benefits society. Several computational techniques come into concern to avoid and reduce the stress level using the electrocardiogram (ECG) signals. In this study, the stress level was classified using the feature extraction approach in combination with the classifier. The signal is processed using the variational mode decomposition denoising technique to reconstruct the original signal. The decomposed signal was further extracted using the time–frequency domain technique as characteristics of the ECG signal such as R-wave and T-wave constructed. Further, the support vector machine classifier was used to classify the stress level (low, medium, and high) of the extracted signal. Based on stress classification outcomes, the robot offers a range of personalized interventions to users. These interventions include relaxation exercises, deep breathing techniques, or guided mindfulness sessions. The average accuracy obtained using the proposed technique is 98.98% but without using the feature extraction technique, it is 97.71%. The other performance parameters also get improved and the results are finally compared with the existing techniques.

To study the development trend of embedded platforms in home appliances Internet of Things (IoT), an application of embedded Linux in the design of IoT gateway is designed. ARM microprocessor S3C2410, EM310 radio frequency (RF) module, and Ethernet DM9000A module are used to realize the home network information-processing platform. The platform utilizes existing network facilities, RF modules, and network interface modules to realize the interconnection of the home perception network through wireless or wired links. For experimental analysis, Ping the central server (gateway) from the IoT node B is set to twice: The first time ttl = 255 time = 1.9 ms, the second time: ttl = 255 time = 2.4 ms. After application verification, the system has the characteristics of low cost, small size, low power consumption, and vital ease of use. It can not only integrate the home Internet and the IoT gateway but also directly run application-layer communication protocols such as HTTP/MQTT between IoT nodes and Internet terminals. The design supports multi-hop communication and significantly increases the deployment range of IoT nodes without signal relay equipment, hence reducing the cost of IoT networking and the complexity of the network architecture.

The mechanization of farming is currently the most pressing problem facing humanity and a burgeoning academic field. Over the last decade, there has been an explosion of Internet of Things (IoT) application growth in agriculture. Agricultural robotics is bringing about a new era of farming because they are growing more intelligent, recognizing causes of variation on the farm, consuming fewer resources, and optimizing their efficiency to more flexible jobs. The purpose of this article is to construct an IoT-Fog computing equipped robotic system for the categorization of weeds and soy plants during both the hazy season and the normal season. The used dataset in this article included four classes: soil, soybean, grass, and weeds. A two-dimensional Convolutional Neural Network (2D-CNN)-based deep learning (DL) approach was implemented for data image classification with dataset of height and width of 150 × 150 and of three channels. The overall proposed system is considered an IoT-connected robotic device that is capable of applying classification through the Internet-connected server. The reliability of the device is also enhanced as it is enabled with edge-based Fog computing. Hence, the proposed robotic system is capable of applying DL classification through IoT as well as Fog computing architecture. The analysis of the proposed system was conducted in steps including training and testing of CNN for classification, validation of normal images, validation of hazy images, application of dehazing technique, and at the end validation of dehazed images. The training and validation parameters ensure 97% accuracy in classifying weeds and crops in a hazy environment. Finally, it concludes that applying the dehazing technique before identifying soy crops in adverse weather will help achieve a higher classification score.

The identification of gas leakages is a significant factor to be taken into consideration in various industries such as coal mines, chemical industries, etc., as well as in residential applications. In order to reduce damage to the environment as well as human lives, early detection and gas type identification are necessary. The main focus of this paper is multimodal gas data that were obtained simultaneously by using multiple sensors for gas detection and a thermal imaging camera. As the reliability and sensitivity of low-cost sensors are less, they are not suitable for gas detection over long distances. In order to overcome the drawbacks of relying just on sensors to identify gases, a thermal camera capable of detecting temperature changes is also used in the collection of the current multimodal dataset The multimodal dataset comprises 6400 samples, including smoke, perfume, a combination of both, and neutral environments. In this paper, convolutional neural networks (CNNs) are trained on thermal image data, utilizing variants such as bidirectional long–short-term memory (Bi-LSTM), dense LSTM, and a fusion of both datasets to effectively classify comma separated value (CSV) data from gas sensors. The dataset can be used as a valuable source for research scholars and system developers to improvise their artificial intelligence (AI) models used for gas leakage detection. Furthermore, in order to ensure the privacy of the client's data, this paper explores the implementation of federated learning for privacy-protected gas leakage classification, demonstrating comparable accuracy to traditional deep learning approaches. [ABSTRACT FROM AUTHOR]

The deployment of fifth-generation (5G) wireless networks has already laid the ground-work for futuristic smart cities but along with this, it has also triggered the rapid growth of a wide range of applications, for example, the Internet of Everything (IoE), online gaming, extended/virtual reality (XR/VR), telemedicine, cloud computing, and others, which require ultra-low latency, ubiquitous coverage, higher data rates, extreme device density, ultra-high capacity, energy efficiency, and better reliability. Moreover, the predicted explosive surge in mobile traffic until 2030 along with envisioned potential use-cases/scenarios in a smart city context will far exceed the capabilities for which 5G was designed. Therefore, there is a need to harness the 6th Generation (6G) capabilities, which will not only meet the stringent requirements of smart megacities but can also open up a new range of potential applications. Other crucial concerns that need to be addressed are related to network security, data privacy, interoperability, the digital divide, and other integration issues. In this article, we examine current and emerging trends for the implementation of 6G in the smart city arena. Firstly, we give an inclusive and comprehensive review of potential 6th Generation (6G) mobile communication technologies that can find potential use in smart cities. The discussion of each technology also covers its potential benefits, challenges and future research direction. Secondly, we also explore promising smart city applications that will use these 6G technologies, such as, smart grids, smart healthcare, smart waste management, etc. In the conclusion part, we have also highlighted challenges and suggestions for possible future research directions. So, in a single paper, we have attempted to provide a wider perspective on 6G-enabled smart cities by including both the potential 6G technologies and their smart city applications. This paper will help readers gain a holistic view to ascertain the benefits, opportunities and applications that 6G technology can bring to meet the diverse, massive and futuristic requirements of smart cities. [ABSTRACT FROM AUTHOR]

Community mask use is an efficacious non-pharmacologic way to minimize viral infection spread. It is a recommendation that individuals wear face masks as protective gear. Under ideal weather conditions, machine and artificial intelligence techniques can typically determine if a person is wearing a mask properly. Identification becomes more difficult under inclement weather such as fog, clouds, haze or rain. In this work, we propose a technique that can detect a human face wearing a mask even in adverse weather. For this, homogeneous foggy images have been considered. The main challenge with this problem is that video quality degrades because of fog. Here, diverse Deep learning models train regular datasets containing digital pictures of persons with facial and non-facial masks. The training and validation parameters ensure 97% accuracy in classifying faces wearing a mask. [ABSTRACT FROM AUTHOR]

Fire chemical sensing for indoor detection of fire plays an essential role because it can detect chemical volatiles before smoke particles, providing a faster and more reliable method for early fire detection. A thermal imaging camera and seven distinct fire-detecting sensors were used simultaneously to acquire the multimodal fire data that is the subject of this paper. The low-cost sensors typically have lower sensitivity and reliability, making it impossible for them to detect fire at greater distances. To go beyond the limitation of using solely sensors for identifying fire, the multimodal dataset is collected using a thermal camera that can detect temperature changes. The proposed pipeline uses image data from thermal cameras to train convolutional neural networks (CNNs) and their many versions. The training of sensors data (from fire sensors) uses bidirectional long-short memory (BiLSTM-Dense) and dense and long-short memory (LSTM-DenseDenseNet201), and the merging of both datasets demonstrates the performance of multimodal data. Researchers and system developers can use the dataset to create and hone cutting-edge artificial intelligence models and systems. Initial evaluation of the image dataset has shown densenet201 as the best approach with the highest validation parameters (0.99, 0.99, 0.99, and 0.08), i.e., Accuracy, Precision, Recall, and Loss, respectively. However, the sensors dataset has also shown the highest parameters with the BILSTM-Dense approach (0.95, 0.95, 0.95, 0.14). In a multimodal data approach, image and sensors deployed with a multimodal algorithm (densenet201 for image data and Bi LSTM- Dense for Sensors Data) has shown other parameters (1.0, 1.0, 1.0, 0.06). This work demonstrates that, in comparison to the conventional deep learning approach, the federated learning (FL) approach performs privacy-protected fire leakage classification without significantly sacrificing accuracy and other validation parameters. [ABSTRACT FROM AUTHOR]

Mobile Ad-hoc networks are extremely susceptible to various misbehaviours and a variety of trust management schemes have been proposed to detect and mitigate them. Trust computation and management are highly challenging issues in MANETs due to computational complexity constraints, and the independent movement of component nodes. Here an implementation based layered model for trust is proposed. The main goal is to cover all the questions that a programmer needs to design a trust based system. The model is divided into three layers which are data collection, trust calculation and application layer. Each layer has its specific functions which work in integrating manner with another layer in order to build a complete system. [ABSTRACT FROM AUTHOR]