This paper presents a novel multiclass system designed to detect pleural effusion and pulmonary edema on chest X-ray images, addressing the critical need for early detection in healthcare. A new comprehensive dataset was formed by combining 28,309 samples from the ChestX-ray14, PadChest, and CheXpert databases, with 10,287, 6022, and 12,000 samples representing Pleural Effusion, Pulmonary Edema, and Normal cases, respectively. Consequently, the preprocessing step involves applying the Contrast Limited Adaptive Histogram Equalization (CLAHE) method to boost the local contrast of the X-ray samples, then resizing the images to 380 × 380 dimensions, followed by using the data augmentation technique. The classification task employs a deep learning model based on the EfficientNet-V1-B4 architecture and is trained using the AdamW optimizer. The proposed multiclass system achieved an accuracy (ACC) of 98.3%, recall of 98.3%, precision of 98.7%, and F1-score of 98.7%. Moreover, the robustness of the model was revealed by the Receiver Operating Characteristic (ROC) analysis, which demonstrated an Area Under the Curve (AUC) of 1.00 for edema and normal cases and 0.99 for effusion. The experimental results demonstrate the superiority of the proposed multi-class system, which has the potential to assist clinicians in timely and accurate diagnosis, leading to improved patient outcomes. Notably, ablation-CAM visualization at the last convolutional layer portrayed further enhanced diagnostic capabilities with heat maps on X-ray images, which will aid clinicians in interpreting and localizing abnormalities more effectively.

When it is important to authenticate a person based on his or her biometric qualities, most systems use a single modality (e.g. fingerprint or palm print) for further analysis at higher levels. Rather than using higher levels, this research recommends using two biometric features at the sensor level. The Log-Gabor filter is used to extract features and, as a result, recognize the pattern, because the data acquired from images is sampled at various spacing. Using the two fused modalities, the suggested system attained greater accuracy. Principal component analysis was performed to reduce the dimensionality of the data. To get the optimum performance between the two classifiers, fusion was performed at the sensor level utilizing different classifiers, including k-nearest neighbors and support vector machines. The technology collects palm prints and veins from sensors and combines them into consolidated images that take up less disk space. The amount of memory needed to store such photos has been lowered. The amount of memory is determined by the number of modalities fused.

Recently, indoor systems for growing plants have emerged as a promising approach to address the problems related to extreme weather conditions outdoors. However, such systems must manage the plants surrounding environments to satisfy the environmental and the economical requirements. In this line, any proposed solution must address challenging factors such as plants diseases and unordinary climate situations. In this paper, propose an internet of things (IoT) indoor system that can be used to facilitate the plant growing process. The proposed system is designed to provide alternatives for outdoor climate dependency such as the vitamins provided through sunlight. Moreover, renewable energy sources (sunlight) are employed to reduce the impact on the environment. With the help of several types of sensors, the system continuously monitors the plants through their growing journey. Whereas actuator devices are employed to control the plant-feeding process based on the sensors’ reported values. All the collected data will be uploaded to the cloud for analysis, utilizing a website. Additionally, the architecture of the provided system eliminates the need for human involvement, which has a degrading effect on the plant growing process.

Due to importance of measuring the degree of resemblance, the similarity measure is widely adopted in various areas of the information systems (e.g., medical informatics and information retrieval) and in several applications like medical diagnostic, image processing, and pattern recognition. However, most of the existing similarity measures focus mainly on the degree of similarity without consulting expert(s) about the results. In this paper, an efficient tool for measuring similarity and agreement of objects that embeds experts’ opinions is proposed to assess similarity among features and agreement of opinions among experts. To obtain such robust measuring tool, three construction steps were followed. Firstly, adapting soft expert set as a general structure that consists of four components: objects, attributes, experts, and experts’ opinions. Secondly, representing the soft expert set, without losing stored information, in such a way as to fit the proposed similarity-agreement measure and make it simpler and more meaningful than the similar existing measures. Thirdly, axiomatizing the similarity-agreement measure for the case of two experts to simplify the model. Further, a diagnostic prediction application and its algorithm is discussed in this context, along with analysis of the experimental results. Analysis of performance of the proposed similarity-agreement measure revealed that it has high accuracy, sensitivity, and value of the F-measure and that it has better performance than existing state-of-the-art tools.

The field of instructional technology has experienced significant growth in recent times. Due to the rapid shift towards online courses, the technology-based learning system is facing challenges in ensuring quality and assurance. The aim of this study was to develop online course evaluation tools by proposing a new algorithm to assess the success of the provided online courses and address quality assurance issues. The proposed algorithm is based on quality matters (QM) attributes and the use of fuzzy soft expert sets (FSESs). One key advantage of the proposed algorithm is that it incorporates experts’ opinions, which significantly contributes to achieving the study objective. The proposed algorithm was successfully implemented using the ASP.NET programming language. It resulted in the development of an EOC-FSES prototype system. The experimental evaluation of the prototype system confirms that it requires low effort and achieves high levels of performance, satisfaction, and behavioral intention to use. This paper includes several recommendations and suggestions.

While traditional teaching and learning methods in education have turned to be more active and engaging with the help of technology, this study proposes a Holistic Gamification Learning Adventure (HGLA) framework, which integrates gamification principles, multidimensional progression, and narrative-driven experiences for programming education. Different from most other gamification models, adaptive pathways and collaboration in the HGLA framework ensure novelty in accommodating diverse learning styles and proficiency levels of both novice and advanced programmers. It’s a framework that uses responsive design to ensure access across devices, creating ubiquitous learning environments. The embedment of gamified missions and narrative-driven challenges shall make students active participants instead of passive observers, while the motivation, engagement, and retention concerning programming concepts shall be developed. The multi-dimensional progression within the framework caters to individual learning paces, ensuring an individualized educational experience for deeper conceptual understanding and long-term retention. The present study shows how the HGLA framework has transformed programming education into an adaptive, inclusive, and impactful learning journey—a beacon of change in innovative teaching methodologies.