Accurately estimating the state-of-charge (SOC) of lithium-ion batteries (LIBs) in electric vehicles is a challenging task due to the complex dynamics of the battery and the varying operating conditions. To address this, this paper proposes the establishment of an Industrial Internet-of-Things (IIoT)-based digital twin (DT) through the Microsoft Azure services, incorporating components for data collection, time synchronization, processing, modeling, and decision visualization. Within this framework, the readily available measurements in the LIB module, including voltage, current, and operating temperature, are utilized, providing advanced information about the LIBs’ SOC and facilitating accurate determination of the electric vehicle (EV) range. This proposed data-driven SOC-estimation-based DT framework was developed with a supervised voting ensemble regression machine learning (ML) approach using the Azure ML service. To facilitate a more comprehensive understanding of historical driving cycles and ensure the SOC-estimation-based DT framework is accurate, this study used three application programming interfaces (APIs), namely Google Directions API, Google Elevation API, and OpenWeatherMap API, to collect the data and information necessary for analyzing and interpreting historical driving patterns, for the reference EV model, which closely emulates the dynamics of a real-world battery electric vehicle (BEV). Notably, the findings demonstrate that the proposed strategy achieves a normalized root mean square error (NRMSE) of 1.1446 and 0.02385 through simulation and experimental studies, respectively. The study’s results offer valuable insights that can inform further research on developing estimation and predictive maintenance systems for industrial applications.

Unanimous action to achieve specific goals is crucial for the success of a robotic swarm. This requires clearly defined roles and precise communication between the robots of a swarm. An optimized task allocation algorithm defines the mechanism and logistics of decision-making that enable the robotic swarm to achieve such common goals. With more nodes, the traffic of messages that are required to communicate inside the swarm relatively increases to maintain decentralization. Increased traffic eliminates real-time capabilities, which is an essential aspect of a swarm system. The aim of this research is to reduce execution time while retaining efficient power consumption rates. In this research, two novel decentralized swarm communication algorithms are proposed, namely Clustered Dynamic Task Allocation–Centralized Loop (CDTA-CL) and Clustered Dynamic Task Allocation–Dual Loop (CDTA-DL), both inspired by the Clustered Dynamic Task Allocation (CDTA) algorithm. Moreover, a simulation tool was developed to simulate different swarm-clustered communication algorithms in order to calculate the total communication time and consumed power. The results of testing the proposed CDTA-DL and CDTA-CL against the CDTA attest that the proposed algorithm consumes substantially less time. Both CDTA-DL and CDTA-CL have achieved a significant speedup of 75.976% and 54.4% over CDTA, respectively.

Occasionally intravenous insertion forms a challenge to a number of patients. Inserting an IV needle is a difficult task that requires a lot of skill. At the moment, only doctors and medical personnel are allowed to do this because it requires finding the right vein, inserting the needle properly, and carefully injecting fluids or drawing out blood. Even for trained professionals, this can be done incorrectly, which can cause bleeding, infection, or damage to the vein. It is especially difficult to do this on children, elderly people, and people with certain skin conditions. In these cases, the veins are harder to see, so it is less likely to be done correctly the first time and may cause blood clots. In this research, a low-cost embedded system utilizing Near-Infrared (NIR) light technology is developed, and two novel approaches are proposed to detect and select the best candidate veins. The two approaches utilize multiple computer vision tools and are based on contrast-limited adaptive histogram equalization (CLAHE). The accuracy of the proposed algorithm is 91.3% with an average 1.4 s processing time on Raspberry Pi 4 Model B.

Speech Emotion Recognition (SER) is vital for enhancing Human-Robot Interaction (HRI). In the past decade, various models have been applied to diverse datasets. However, these datasets often fell short in representing real-world scenarios. This paper introduces an innovative acoustic feature set, based on Mel Frequency Cepstral Coefficients (MFCC) and Mel-Spectrograms, applied to a combined dataset comprising RAVDESS, TESS, and EmoDB. Our proposed model achieves an impressive accuracy of 85.05 % on this combined dataset, demonstrating its suitability for real-world applications. This advancement holds significant promise for improving HRI systems, affective computing, and AI -driven applications, where understanding and responding to human emotions through speech is crucial. The robustness and high accuracy of our model provide valuable insights for re-searchers and practitioners seeking to implement SER technology in practical, real-world settings. This study offers two primary contributions. Firstly, it involves the compilation of a unified dataset from the three mentioned sources. Secondly, it achieved the highest accuracy of 85.05 %.

The world population is expected to grow to over 10 billion by 2050 and therefore impose further stress on food production. Precision agriculture has become the main approach used to enhance productivity with sustainability in agricultural production. This paper conducts a technical review of how robotics, artificial intelligence (AI), and thermal imaging (TI) technologies transform precision agriculture operations, focusing on sensing, automation, and farm decision making. Agricultural robots promote labor solutions and efficiency by utilizing their sensing devices and kinematics in planting, spraying, and harvesting. Through accurate assessment of pests/diseases and quality assurance of the harvested crops, AI and TI bring efficiency to the crop monitoring sector. Different deep learning models are employed for plant disease diagnosis and resource management, namely the VGG16 model, InceptionV3, and MobileNet; the PlantVillage, PlantDoc, and FieldPlant datasets are used respectively. To reduce crop losses, AI–TI integration enables early recognition of fluctuations caused by pests or diseases, allowing control and mitigation in good time. While the issues of cost and environmental variability (illumination, canopy moisture, and microclimate instability) are taken into consideration, the advancement in artificial intelligence, robotics technology, and combined technologies will offer sustainable solutions to the existing gaps.