Recently, precision agriculture has used wireless sensor networks (WSNs) to gain valuable insights and improve crop yields, promoting efficient resource use and data-driven decisions. However, WSNs face challenges, such as high power consumption from continuous sensing, data processing and communication, especially in large-scale setups, which limits their lifespan. This paper focuses on reducing power use in agricultural WSN sensor nodes during data transmission of soil moisture, rainfall, light intensity, air temperature and humidity from the transmitting sensor node to the base station. Four algorithms are proposed to cut power consumption. First, a sleep/wake (S/W) scheme using a simple duty cycle called S/W-DC. Second, the S/W scheme combined with adaptive data sampling (ADS) based on redundant data (RD), called S/W-ADS-RD. Third, the S/W scheme integrated with dynamic voltage scaling (DVS), named S/W-DVS. Fourth, a hybrid of all three, called S/W-ADS-RD-DVS. The sensor uses a 12 V/5 W solar panel for energy harvesting to maintain operation. The hybrid algorithm achieved 99.232% power savings and extended battery life to approximately 1.83 years. During a 6-h session, data transmission was reduced by 99.93%. This research could significantly improve WSN efficiency in precision agriculture and can be applied to energy-efficient WSN deployment across various fields, supporting Internet of Things (IoT) applications.

Over the past few years, remarkable progress has been made in Autonomous Robotic Systems (ARSs) due to their multidisciplinary nature and applications. Among the most in-demand applications is the Search and Rescue (SAR) task, particularly in high-risk environments where human entry is unsafe. Specifically, teleoperated SAR platforms deploy dedicated sensors and control algorithms to optimize search effectiveness and mitigate risks incurred by human responders. This systematic review analyzes 74 research papers published between 2016 and 2024, with a focus on the role of robots in SAR operations (sensor integration), the types of robots employed, and the control algorithms used. The work classifies robotic systems in the context of SAR as either aerial, ground, or marine platforms and analyzes their sensor technologies, autonomous navigation functions, and human-robot interaction capabilities. A major contribution of this survey is the proposal and design of a five-layered architecture for robotic SAR systems, which categorizes system functions into clearly differentiated layers; namely, the HRI Layer, Perception Layer, Navigation Layer, Control Layer, and Robot Layer. Such a structured method enables modular, scalable, and real-time decision-making under dynamic disaster conditions. Moreover, this paper identifies several research challenges, including multi-robot coordination, AI-based real-time decision-making, and energy efficiency, providing a related analysis for the future development of robotic SAR systems.

The development of remote blood pressure (BP) measurement algorithms using remote photoplethysmography (rPPG) has significant limitations, including the small size of publicly available datasets, privacy concerns regarding facial videos, and a lack of diverse, realistic datasets associated with actual BP measurements. To address these challenges, this study aimed to provide comprehensive, simultaneous recordings of participants' faces, along with reference physiological measurements, for 300 adult participants aged 18–65 years. For each imaging session, systolic and diastolic blood pressure and reference heart rate (HR) were recorded using clinical electronic BP monitors in addition to recording illuminance (lux) values for indoor and outdoor environments. The collected data, called CLBP-300, is a crucial resource for developing and evaluating remote vital signs from facial rPPG signals. A sample of videos is publicly available to demonstrate data quality, while academic researchers can access the complete dataset under a strict data use agreement. The data and python code presented in this study are available on https://sites.google.com/view/clbp-300?usp=sharing.

Nanomaterials are increasingly investigated for dental restorations to improve mechanical reliability. Zinc oxide nanoparticles (ZnO NPs) are attractive for reinforcement and antibacterial potential, yet optimisation is challenging because nanoparticle properties depend on synthesis conditions and experimental datasets are often limited. This study aimed to identify the ZnO loading that yields optimal mechanical performance in a photocured dental resin using an integrated synthesis–testing–modelling workflow. ZnO NPs were synthesised by pulsed laser ablation in liquid (PLAL) using an Nd: YAG laser at three pulse energies (30, 70, and 150 mJ) and characterised by SEM, XRD, FTIR, and UV–Vis spectroscopy. A selected ZnO batch was incorporated into a photocured dental resin at 0–0.7569 wt

Spiral fractures are a frequent clinical manifestation of child abuse, particularly in non-ambulatory infants. Approximately 50% of fractures in children under one year of age are non-accidental, yet differentiating between accidental and abusive injuries remains challenging, as no single fracture type is diagnostic in isolation. The objective of this study is to investigate the biomechanics of spiral fractures in immature long bones and the role of the periosteum in modulating fracture behavior under torsional loading. Methods: Paired metatarsal bone specimens from immature sheep were tested using controlled torsional loading at two angular velocities (90°/s and 180°/s). Specimens were prepared through potting, application of a base coat, and painting of a speckle pattern suitable for high-speed digital image correlation (HS-DIC) analysis. Both periosteum-intact and periosteum-removed groups were included. Results: Spiral fractures were successfully induced in over 85% of specimens. Digital image correlation revealed localized diagonal tensile strain at the fracture initiation site, with opposing compressive zones. Notably, bones with intact periosteum exhibited broader tensile stress regions before and after failure, suggesting a biomechanical role in constraining deformation. Conclusion: This study presents a novel integration of high-speed digital image correlation (DIC) with paired biomechanical testing to evaluate the periosteum’s role in spiral fracture formation—an area that remains underexplored. The findings offer new insight into the strain distribution dynamics in immature long bones and highlight the periosteum’s potential protective contribution under torsional stress.

Recent advancements in online communication have made it possible to securely transmit sensitive data, such as medical images containing confidential patient information. As the medical industry embraces telemedicine applications, the need for solutions to protect such essential data grows. Within the scope of this paper, a novel image-to-image encryption-decryption-compression (ITIEDC) technique and a lossless compression technique are suggested for utilisation in a cryptosystem that employs chaos maps, piecewise linear chaotic maps (PWLCMs), DNA encoding technology, one-time pad (OTP), and MD5. Arithmetic coding is utilised as a compression approach in the proposed methodology. The suggested DNA-based chaos encryption method increases the image's entropy, according to experimental findings. For both encryption and encryption-compression techniques, the average time to encrypt a 512 x 512 pixel image is 3 s or less. There is a remarkable closeness between the correlation coefficient and zero. The NPCR for medical images is likewise quite close to 99.60, demonstrating the system's resistance to discriminatory attacks. The X-ray and MRI images are provided from actual patients at Al-Yarmouk Hospital in Baghdad, Iraq, while the rest are from an online data set. Notably, the proposed method achieves the process of encrypting and decrypting varying-sized medical images with a single computation. The simulation results validate the appropriateness of this approach for safeguarding sensitive medical images.

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Ali Saber Amsalam, Ali Al-Naji, Ammar Yahya Daeef; Facial palsy detection using pre-trained deep learning models: A comparative study. AIP Conf. Proc. 7 May 2024; 3097 (1): 020010. https://doi.org/10.1063/5.0209928 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search

Early diagnosis of eye diseases is very important to prevent visual impairment and guide appropriate treatment methods. This paper presents a unique approach that can detect numerous eye diseases automatically. Initially, this approach used the pre‐trained ImageNet models that provides various pre‐trained models for training the acquired data. The existing data sets are composed of 645 data images acquired clinically, represented by two groups of subjects as healthy and others holding the proposed eye defect like cataracts, foreign bodies, glaucoma, subconjunctival haemorrhage, and viral conjunctivitis. Followed by comparisons of the pre‐trained model's coefficients and prediction performance. Later, the first‐class execution model is integrated within the Raspberry Pi staging and the real‐time digital camera detection. The evaluation process used the confusion matrix, model accuracy, precision factor, recall coefficient, F1 score, and the Matthews Correlation Coefficient (MCC). Resulting in the performance of these pre‐trained ImageNet models used in this study represented by 93% (InceptionResNetV2), 90% (MobileNet), 86% (Residual Network ResNet50), 85% (InceptionV3), 78% (Visual Geometry Group VGG19), and 72% (Neural Architecture Search Network NASNetMobile). The results show that the InceptionResNetV2 achieved the highest performance. This proposed approach shows its efficiency and strength by early detection of the subject's unhealthy eyes through real‐time monitoring in the field of ophthalmology.

Respiration rate (RR) holds significance as a human health indicator. Presently, the conventional RR monitoring system requires direct physical contact, which may cause discomfort and pain. Therefore, this paper proposes a non-contact RR monitoring system integrating RGB and thermal imaging through RGB-thermal image alignment. The proposed method employs an advanced image processing algorithm for automatic region of interest (ROI) selection. The experimental results demonstrated a close correlation and a lower error rate between measured thermal, measured RGB, and reference data. In summary, the proposed non-contact system emerges as a promising alternative to conventional contact-based approaches without the associated discomfort and pain.

Diagnosing diseases in their early stages allows people to see a specialist doctor before the disease reaches advanced stages and avoid any future complications. The use of a real-time imaging system to reliably give various information about the patient's condition, including gender classification, pterygium, and Bell's paralysis, contributes to reducing the duration of diagnosis and human errors. This study focuses on the use of three artificial intelligence algorithms based on deep learning, namely Visual Geometry Group (VGG16), Vision Transformer (ViT), and Xception, and evaluates their performance in detecting gender, pterygium, and Bell's paralysis. VIT has the highest overall performance results from the rest of the algorithms.

The diagnosis of tongue disease is based on the observation of various tongue characteristics, including color, shape, texture, and moisture, which indicate the patient’s health status. Tongue color is one such characteristic that plays a vital function in identifying diseases and the levels of progression of the ailment. With the development of computer vision systems, especially in the field of artificial intelligence, there has been important progress in acquiring, processing, and classifying tongue images. This study proposes a new imaging system to analyze and extract tongue color features at different color saturations and under different light conditions from five color space models (RGB, YcbCr, HSV, LAB, and YIQ). The proposed imaging system trained 5260 images classified with seven classes (red, yellow, green, blue, gray, white, and pink) using six machine learning algorithms, namely, the naïve Bayes (NB), support vector machine (SVM), k-nearest neighbors (KNN), decision trees (DTs), random forest (RF), and Extreme Gradient Boost (XGBoost) methods, to predict tongue color under any lighting conditions. The obtained results from the machine learning algorithms illustrated that XGBoost had the highest accuracy at 98.71%, while the NB algorithm had the lowest accuracy, with 91.43%. Based on these obtained results, the XGBoost algorithm was chosen as the classifier of the proposed imaging system and linked with a graphical user interface to predict tongue color and its related diseases in real time. Thus, this proposed imaging system opens the door for expanded tongue diagnosis within future point-of-care health systems.

In recent years, the use of renewable energy sources by many power grid companies worldwide has increased significantly. The trend towards the use of renewable energy sources is mainly due to environmental issues and rising fuel prices associated with conventional electricity generation. Distributed generation units are power generation plants that are very important for the grid architecture of today’s power system. The benefit of adding these Distributed Generation (DG) units is to increase the power supply to the grid. However, the installation of DG units can cause a negative impact if not properly allocated and/or sized. Therefore, there is a need for their optimal sizing and allocation to avoid situations such as voltage instability and high investment cost. In this paper, four heuristic based algorithms, namely Particle Swarm Optimization (PSO) algorithm, Whale Optimization Algorithm (WOA), Dolphin Echolocation Optimization (DEO), and Slime Mould Algorithm (SMA) are applied to solve the optimal placement and sizing of DG units in distribution network planning. Three cases were used to address the network problems, which are represented by adding photovoltaic cells and wind turbines individually. In the last case, both were used, and the feasibility of algorithms was confirmed for two systems, IEEE 33-bus and 69-bus test systems. The comparison results showed that the SMA algorithm produces good solutions. In general, the SMA algorithm was able to reduce the two system losses. The reduction of real power losses in SMA, taking into account the technical and economic constraints in the IEEE 33 system, is reduced to a minimum of 66.31%, 67.3%, and 81.1%, While in 69 bus, reduced to a minimum of 90.7%,91%,and 97.30% for three cases respectively, as well as improving the voltage profile, thus obtaining a more efficient system.

After different consecutive waves, the pandemic phase of COVID-19, which started in Wuhan, China, seems to have no imminent end worldwide transmission of the SARS coronavirus has prompted the World Health Organization to proclaim a worldwide pandemic (known as COVID-19). Its effects on regular life and health have been devastating. In order to stop the spread of the disease and provide immediate care for infected patients, Rapid identification of positive cases is essential. The demand for diagnostic aids has grown. Recent advances in medical imaging show that these pictures reveal important information about the COVID-19 virus. The current shortage of specialists can be alleviated in part through the use of cutting-edge user artificial intelligence technologies and medical imagery in making correct diagnoses of this condition. The small size of the virus cells makes it difficult to distinguish them visually with the naked eye. Therefore, the transmission electron microscope with different magnifications was used to detect and image the virus cells. In recent years, medical diagnoses have significantly benefited from the widespread use of machine learning-based image processing technologies and have proven to be an effective method for helping diagnostic professionals to identify subtle lesions in images accurately. This work presents a customized computer-assisted automatic detection system specifically focused on quantifying and counting viral cells detected in blood samples from patients using electron microscopy images to detect infected blood cells. The average accuracy of the proposed custom model is 86.5% mAP, which makes it suitable for the upcoming COVID-19 surveillance system.

Efficient irrigation water use directly affects crop productivity as demand increases for various agricultural products due to population growth worldwide. While technologies are being developed in various fields, it has become desirable to develop automatic irrigation systems to reduce the waste of water caused by traditional irrigation processes. This paper presents a novel approach to an automated irrigation system based on a non-contact computer vision system to enhance the irrigation process and reduce the need for human intervention. The proposed system is based on a stand-alone Raspberry Pi camera imaging system mounted at an agricultural research facility which monitors changes in soil color by capturing images sequentially and processing captured images with no involvement from the facility’s staff. Two types of soil samples (sand soil and peat moss soil) were utilized in this study under three different scenarios, including dusty, sunny, and cloudy conditions of wet soil and dry soil, to take control of irrigation decisions. A relay, pump, and power bank were used to achieve the stability of the power source and supply it with regular power to avoid the interruption of electricity.

Introduction: Despite the many attempts made by researchers to diagnose jaundice non-invasively using machine learning techniques, the low amount of data used to build their models remains the key factor limiting the performance of their models. Objective: To build a system to diagnose neonatal jaundice non-invasively based on machine learning algorithms created based on a dataset comprising 767 infant images using a computer device and a USB webcam. Methods: The first stage of the proposed system was to evaluate the performance of four machine learning algorithms, namely support vector machine (SVM), k nearest neighbor (k-NN), random forest (RF), and extreme gradient boost (XGBoost), based on a dataset of 767 infant images. The algorithm with the best performance was chosen as the classifying algorithm in the developed application. The second stage included designing an application that enables the user to perform jaundice detection for a patient under test with the minimum effort required by capturing the patient’s image using a USB webcam. Results: The obtained results of the first stage of the machine learning algorithms evaluation process indicated that XGBoost outperformed the rest of the algorithms by obtaining an accuracy of 99.63%. The second-best algorithm was the RF algorithm, which had an accuracy of 98.99%. Following RF, with a slight difference, was the k-NN algorithm. It achieved an accuracy of 98.25%. SVM scored the lowest performance among the above three algorithms, with an accuracy of 96.22%. Based on these obtained results, the XGBoost algorithm was chosen to be the classifier of the proposed system. In the second stage, the jaundice application was designed based on the model created by the XGBoost algorithm. This application ensured it was user friendly with as fast a processing time as possible. Conclusion: Early detection of neonatal jaundice is crucial due to the severity of its complications. A non-invasive system using a USB webcam and an XGBoost machine learning technique was proposed. The XGBoost algorithm achieved 99.63% accuracy and successfully diagnosed 10 out of 10 NICU infants with very little processing time. This denotes the efficiency of machine learning algorithms in healthcare in general and in monitoring systems specifically.