PIFR: A novel approach for analyzing pose angle-based human activity to automate fall detection in videos.

Falls pose a significant health risk for elderly populations, necessitating advanced monitoring technologies. This study introduces a novel two-stage fall detection system that combines computer vision and machine learning to accurately identify fall events. The system uses the YOLOv11 object detection model to track individuals and estimate their body pose by identifying 17 key body points across video frames.

A novel methodology based on Contrast-Adaptive Threshold Model for accurate optic disc detection in retinal images of premature infants.

Identifying the exact location of the optic disc in retinal images is an important task while performing the retinal image analysis. Localization of the optic disc generally fails to detect its exact location due to unclear boundaries and low contrast images, especially in retinal images of infants where the process of retrieving the images should be very quick in low light conditions. The objective of this research paper is to detect the location of optic disc using a segmentation algorithm titled "Contrast-Adaptive Threshold Model" in infant retinal images.

Dual-Wide Multi-Band (DWMB) four-port flexible MIMO antenna for on-body multiple wireless applications including high diversity performance.

The single-input-single-output technology experiences loss of data in the communication channel due to the receiving antenna undergoing fading of the signal impinged on it. Today's need is faster data transfer with multiple applications in the single antenna with multiple-identical radiating elements, leading to multiple-input-multiple-outputDWMB (MIMODWMB) technology. The MIMODWMB configuration with multi-band capability is the objective of the proposed work with applications ranging between microwave-millimeterWave bands.

Enhancing DDoS detection in SDIoT through effective feature selection with SMOTE-ENN.

Internet of things (IoT) facilitates a variety of heterogeneous devices to be enabled with network connectivity via various network architectures to gather and exchange real-time information. On the other hand, the rise of IoT creates Distributed Denial of Services (DDoS) like security threats. The recent advancement of Software Defined-Internet of Things (SDIoT) architecture can provide better security solutions compared to the conventional networking approaches.

Correction: An efficient post-processing adaptive filtering technique to rectifying the flickering effects.

[This corrects the article DOI: 10.1371/journal.pone.

A novel investigation using thermal modeling and optimization of waste pyrolysis reactor using finite element analysis and response surface methodology.

The influence of humans on the environment is growing drastically and is pervasive. If this trend continues for a longer time, it can cost humankind, social and economic challenges. Keeping this situation in mind, renewable energy has paved the way as our saviour.

Explaining sentiment analysis results on social media texts through visualization.

Today, Artificial Intelligence is achieving prodigious real-time performance, thanks to growing computational data and power capacities. However, there is little knowledge about what system results convey; thus, they are at risk of being susceptible to bias, and with the roots of Artificial Intelligence ("AI") in almost every territory, even a minuscule bias can result in excessive damage. Efforts towards making AI interpretable have been made to address fairness, accountability, and transparency concerns.

Energy management system in smart buildings based coalition game theory with fog platform and smart meter infrastructure.

This paper proposes a central energy management system (EMS) in smart buildings. It is based on the coalition method for optimal energy sharing between smart buildings. Game theory is applied to obtain an optimal allocation of the building's surplus energy on the deficient energy buildings using the Shapley value, which enables the unequal energy distribution based on the energy demand.

Classification and diagnostic prediction of breast cancer metastasis on clinical data using machine learning algorithms.

Metastatic Breast Cancer (MBC) is one of the primary causes of cancer-related deaths in women. Despite several limitations, histopathological information about the malignancy is used for the classification of cancer. The objective of our study is to develop a non-invasive breast cancer classification system for the diagnosis of cancer metastases.

Redefining food safety traceability system through blockchain: findings, challenges and open issues.

In the last few decades, there has been an increase in food safety and traceability issues. To prevent accidents and misconduct, it became essential to establish Food Safety Traceability System (FSTS) to trace the food from producer to consumer. The traceability systems can help track food in supply chains from farms to retail.

Data augmentation using Variational Autoencoders for improvement of respiratory disease classification.

Computerized auscultation of lung sounds is gaining importance today with the availability of lung sounds and its potential in overcoming the limitations of traditional diagnosis methods for respiratory diseases. The publicly available ICBHI respiratory sounds database is severely imbalanced, making it difficult for a deep learning model to generalize and provide reliable results. This work aims to synthesize respiratory sounds of various categories using variants of Variational Autoencoders like Multilayer Perceptron VAE (MLP-VAE), Convolutional VAE (CVAE) Conditional VAE and compare the influence of augmenting the imbalanced dataset on the performance of various lung sound classification models.

Improvise approach for respiratory pathologies classification with multilayer convolutional neural networks.

Every respiratory-related checkup includes audio samples collected from the individual, collected through different tools (sonograph, stethoscope). This audio is analyzed to identify pathology, which requires time and effort. The research work proposed in this paper aims at easing the task with deep learning by the diagnosis of lung-related pathologies using Convolutional Neural Network (CNN) with the help of transformed features from the audio samples.

A novel air quality monitoring and improvement system based on wireless sensor and actuator networks using LoRa communication.

In this paper, we study the air quality monitoring and improvement system based on wireless sensor and actuator network using LoRa communication. The proposed system is divided into two parts, indoor cluster and outdoor cluster, managed by a Dragino LoRa gateway. Each indoor sensor node can receive information about the temperature, humidity, air quality, dust concentration in the air and transmit them to the gateway.

Comparative study with practical validation of photovoltaic monocrystalline module for single and double diode models.

A photovoltaic (PV) module is an equipment that converts solar energy to electrical energy. A mathematical model should be presented to show the behavior of this device. The well-known single-diode and double-diode models are utilized to demonstrate the electrical behavior of the PV module.

IoT-IIRS: Internet of Things based intelligent-irrigation recommendation system using machine learning approach for efficient water usage.

In the traditional irrigation process, a huge amount of water consumption is required which leads to water wastage. To reduce the wasting of water for this tedious task, an intelligent irrigation system is urgently needed. The era of machine learning (ML) and the Internet of Things (IoT) brings it is a great advantage of building an intelligent system that performs this task automatically with minimal human effort.

EnsemV3X: a novel ensembled deep learning architecture for multi-label scene classification.

Convolutional neural network is widely used to perform the task of image classification, including pretraining, followed by fine-tuning whereby features are adapted to perform the target task, on ImageNet. ImageNet is a large database consisting of 15 million images belonging to 22,000 categories. Images collected from the Web are labeled using Amazon Mechanical Turk crowd-sourcing tool by human labelers.

Location-proximity-based clustering method for peer-to-peer multimedia streaming services with multiple sources.

Social distancing to reduce the spread of coronavirus disease 2019 (COVID-19) made a huge increase in the global OTT market, and OTT service providers get millions of new subscribers. Recently OTT service providers are extending their service to video broadcasting. As a one type of video broadcasting, this paper covers multimedia streaming with multiple sources.

An efficient post-processing adaptive filtering technique to rectifying the flickering effects.

Compression at a very low bit rate(≤0.5bpp) causes degradation in video frames with standard decoding algorithms like H.261, H.

COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach.

The main objective of this paper is to further improve the current time-series prediction (forecasting) algorithms based on hybrids between machine learning and nature-inspired algorithms. After the recent COVID-19 outbreak, almost all countries were forced to impose strict measures and regulations in order to control the virus spread. Predicting the number of new cases is crucial when evaluating which measures should be implemented.

Chaotic-map based authenticated security framework with privacy preservation for remote point-of-care.

The challenge of COVID-19 has become more prevalent across the world. It is highly demanding an intelligent strategy to outline the precaution measures until the clinical trials find a successful vaccine. With technological advancement, Wireless Multimedia Sensor Networks (WMSNs) has extended its significant role in the development of remote medical point-of-care (RM-PoC).

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques.

In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost.

CT-RPL: Cluster Tree Based Routing Protocol to Maximize the Lifetime of Internet of Things.

Energy conservation is one of the most critical challenges in the Internet of Things (IoT). IoT devices are incredibly resource-constrained and possess miniature power sources, small memory, and limited processing ability. Clustering is a popular method to avoid duplicate data transfer from the participant node to the destination.

Brain-Computer Interface-Based Humanoid Control: A Review.

A Brain-Computer Interface (BCI) acts as a communication mechanism using brain signals to control external devices. The generation of such signals is sometimes independent of the nervous system, such as in Passive BCI. This is majorly beneficial for those who have severe motor disabilities.