Cataracts are a leading cause of blindness in Pakistan, contributing to more than 54% of cases due to poor living condition, nutritional deficiencies, and limited healthcare access. Early detection is critical to avoid invasive treatments,but current diagnostic approaches often identify cataracts at advanced stages. This paper presents an advanced,automated cataract detection system using deep learning specifically the ResNet-50 architecture, to address this gap. The model processes fundus retinal images curated from diverse datasets, classified by ophthalmologic experts through a rigorous three-stage process. By leveraging the ResNet-50 model, cataracts are categorized into normal,moderate,... More >

The sharing of data and private information has been greatly improved by Industry 4.0's broad usage of cloud technologies. In their quest to improve their services, many firms have made automation and effective authentication a priority. As a result, in Industry 4.0, Attribute-Based Encryption (ABE) and Attribute-Based Authentication (ABA) have established themselves as dependable models for data sharing across cloud environments. For difficult situations like fine-grained access control and secure authentication, these models offer practical answers. Organizations can utilize ABA to specifically authenticate people based on their attributes, ensuring appropriate and safe access to critical... More >

We investigate the use of deep learning models for retail sales predictions in this research. Having a proper sales forecasting can lead to optimization in inventory management, marketing strategies, and other core business operations. This research proposes to assess deep learning models such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Multilayer Perceptron and hybrid CNN-LSTM model. The models are further improved by using some dense layers to embed daily sales data from the biggest pharmaceutical firm in the study. Models are then trained on 80% of the dataset and tested on remaining 20%. The accuracy of the proposed research is compared using evaluation metrics... More >

Precision plant protection involves a complex spray–droplet system in which atomization behavior is governed by the coupled effects of operating parameters and nozzle characteristics. However, a systematic understanding of how these parameters jointly influence droplet size and deposition performance remains limited in ground plant protection applications. In this study, a laser-based particle size measurement approach is employed to characterize the systematic dependence of atomized droplet size on spray pressure, nozzle flow rate (orifice), and spray angle under controlled conditions. The results reveal consistent parameter–response patterns: droplet size decreases with increasing spra... More >

With the increasing complexity of the working environment and the diversification of mission requirements of UAVs, traditional UAVs have a fixed structure and single function. It is difficult to be applied in occasions with complex environments and changing load demands. The modular reconfigurable flight array (MRFA) is composed of no less than four isomorphic unit modules that are freely spliced together. By adding or removing flight unit modules and adjusting the arrangement of flight unit modules, the configuration of the MRFA can be changed, so that it can adapt to complex environments and then complete different flight missions. In the process of MRFA research and development, online co... More >

Accurate predictions of traffic flow are very meaningful to city managers. With such information, traffic systems can better coordinate traffic signals and reduce congestion. By understanding traffic patterns, navigation systems can provide real-time routing suggestions that avoid traffic jams, save time, and reduce fuel consumption. However, traffic flow will be interfered with by multiple factors such as collection time and place. In this paper, stochastic configuration networks (SCNs) are proposed to predict the traffic flow. The network is trained through stepwise construction, and the network parameters are effectively optimized based on the approximation theorem and convergence analysi... More >

A Novel simulation framework using self-governing drones is used to locate and reduce unauthorized drones in interior environments. The recommended method uses Received Signal Strength Indicator (RSSI) to identify an alien agent drone, which has different signal characteristics than the approved swarm of UAVs. Real-time threat detection is possible with this technology. After detecting the drone, the swarm organizes itself to encircle and besiege it for 10 seconds, making it inert, before the swarm returns to its original formation. This unique solution uses RSSI to quickly identify and mitigate enclosed area concerns. It provides a reliable and effective indoor drone security solution. The... More >

The integration of graph neural networks (GNNs) with brain functional network analysis is an emerging field that combines neuroscience and machine learning to enhance our understanding of complex brain dynamics. We first briefly introduce the fundamentals of brain functional networks, followed by an overview of Graph Neural Network principles and architectures. The review then focuses on the applications of these networks and address current challenges in the field, such as the need for interpretable models and effective integration of multi-modal neuroimaging data. We also highlight the potential of GNNs in clinical areas such as perimenopausal depression research, demonstrating the broad a... More >

This paper proposes designing and structuring a Cyber-Physical System (CPS) with a specific focus on vehicles equipped with on-board diagnosis (OBD-II). The purpose of the CPS is to collect and assess data pertaining to the vehicle's Electronic Control Unit (ECU), such as engine RPM, speed, and other relevant parameters. The OBD-II scanner utilizes the obtained data on mass airflow (MAF) and vehicle speed to compute CO2 gas emissions and fuel consumption. The data is wirelessly communicated using a GSM module to a Semantic Web. The CPS also uses GPS tracking to ascertain the vehicle's whereabouts. A Semantic Web is utilized to construct a database management system that stores and manages se... More >

Nowadays, state estimation is widely used in fields such as autonomous driving and drone navigation. However, in practical applications, it is difficult to obtain accurate target motion models and noise covariance.This leads to a decrease in the estimation accuracy of traditional Kalman filters. To address this issue, this paper proposes an adaptive model free state estimation method based on attention parameter learning module. This method combines Transformer's encoder with Long Short Term Memory Network (LSTM), and obtains the system's operational characteristics through offline learning of measurement data without modeling the system dynamics and measurement characteristics. In addition,... More >

In recent years, deep learning techniques have been increasingly applied to the detection of remote sensing images. However, the substantial size variation and dense distribution of objects in these images present significant challenges to detection algorithms. Current methods often suffer from low efficiency, missed detections, and inaccurate bounding boxes. To address these issues, this paper presents an improved YOLO algorithm, YOLOv7-bw, designed for efficient remote sensing image detection, thereby advancing object detection applications in the remote sensing industry. YOLOv7-bw enhances the original SPPCSPC pooling pyramid network by incorporating a Bi-level Routing Attention module, w... More >

A pedestrian navigation system (PNS) that only relies on the foot-mounted IMU is useful for various applications, especially under some severe conditions, such as tracking of firefighters and miners. Due to the complexity of the indoor environment, signal occlusion problems could lead to the failure of certain positioning methods. In complex environments such as fire rescue and emergency rescue, the barometric altimeter fails because of the influence of air pressure and temperature. This paper used an improved zero velocity detection algorithm to improve the accuracy of gait detection. Then, combine the Kalman filter with the zero velocity update algorithm to recognize gait accurately and ta... More >

To predict future trends based on the data from sensors is an important technology for many applications, such as the Internet of Things, smart cities, etc. Based on the predicted results, further decisions and system controls can be made. Raw sensor data sets are often complex non-linear data with noise, which results in the difficulty of accurate prediction. This paper proposes a distributed deep prediction network based on a covariance intersection (CI) fusion algorithm in which the deep learning networks, such as long-term and short-term memory networks (LSTM) and gated recurrent unit networks (GRU) are fused by CI fusion algorithm to effectively develop the performance of prediction. Mo... More >

Front-end feature tracking based on vision is the process in which a robot captures images of its surrounding environment using a camera while in motion. Each frame of the image is then analyzed to extract feature points, which are subsequently matched between pairwise frames to estimate the robot’s pose changes by solving for the variations in these points. While feature matching methods that rely on descriptor-based approaches perform well in cases of significant lighting and texture variations, the addition of descriptors increases computational cost and introduces instability. Therefore, in this paper, a novel approach is proposed that combines sparse optical flow tracking with Shi-Tom... More >

Presents information on the new ICCK Transactions on Intelligent Systematics. More >