Pneumonia is a pulmonary infection that causes thoracic discomfort, typically caused by bacteria, or viruses. The pneumonia in children and elderly is medical emergency and hence appropriate diagnosis and treatment is necessary. Clinical-level screening of pneumonia is frequently executed using the chest X-ray and its analysis will help in treatment planning and execution. Recently, several pre-trained deep-learning (PDL) based systems are developed to identify disease in different imaging modalities, including the chest X-ray. This study aims to develop a PDL-based tool to analyse chest X-ray dataset to identify the pneumonia. This PDL-tool performs the following tasks on the X-ray database; (i) detection of healthy/pneumonia, and (ii) detecting the viral/bacterial pneumonia. Along with the traditional deep-features based classification using the SoftMax, this work also considered Harris-Hawks Algorithm (HHA) algorithm based features optimization and serial features integration to generate fused-features vector (FFV). The experimental outcome authenticates that this PDL-tool helps to offer improved accuracy with the HHA-optimized features. This work provided an accuracy of 99.3750% during healthy/pneumonia detection with FFV and Support Vector Machine (SVM), and detection accuracy of 88.5417% during viral/bacterial pneumonia detection with FFV and SVM.

The field of generative artificial intelligence (AI) is experiencing rapid advancements, impacting a multitude of sectors, from computer vision to healthcare. This paper provides a comprehensive review of generative AI’s evolution, significance, and applications, including the foundational architectures such as generative adversarial networks (GANs), variational autoencoders (VAEs), autoregressive models, flow‐based models, and diffusion models. We delve into the impact of generative algorithms on computer vision, natural language processing, artistic creation, and healthcare, demonstrating their revolutionary potential in data augmentation, text and speech synthesis, and medical image interpretation. While the transformative capabilities of generative AI are acknowledged, the paper also examines ethical concerns, most notably the advent of deepfakes, calling for the development of robust detection frameworks and responsible use guidelines. As generative AI continues to evolve, driven by advances in neural network architectures and deep learning methodologies, this paper provides a holistic overview of the current landscape and a roadmap for future research and ethical considerations in generative AI.

The availability of numerous types of aircraft and their technical capabilities are offering a wide range of alternatives. As customers have different expectations, aircraft type selection is a business strategy for the airline companies. The choice on carriers should be made in accordance with disparate dimensions such as customers' expectations, profit of the company, capacity limitations and market conditions. This study formulates aircraft type selection as a Multi Criteria Decision Making (MCDM) problem and proposes a novel model that incorporates fuzzy trigonometric norms to solve. Being differentiated from the existing models in the literature, a two-stage model is identified. In the 1st stage, a fuzzy trigonometric-based Ordinal Priority Approach (OPA) determines the criteria weights. In the 2nd stage, RAFSI (Ranking of Alternatives through Functional Mapping of Criteria Subintervals into Single Intervals) is integrated to determine the optimal aircraft type. The model simulated for a case of Turkish Airline company. Sensitivity tests justify robustness of the model. Results show that among the four options, medium-scale high-qualified but not luxury aircraft is the best option.

Sustainable food supplier selection (SFSS) can be handled as an uncertain decision-making issue. The Pythagorean fuzzy set (PFS), a type of non-standard fuzzy set, offers an expanded description space for articulating fuzzy and uncertain data. Accordingly, this paper proposes a Pythagorean fuzzy synthetic decision method-based selection framework for solving the SFSS problem within a subjective context. Then, the weighted distance measures for the PFS are introduced to derive the importance degrees of the experts, which can provide a more objective decision result. Then, an information fusion method with a PFS-weighted power average (WPA) operator is introduced to form a group decision matrix competent to accommodate the deviation effect. Next, an extended PF-measurement of alternatives and ranking according to compromise solution (MARCOS) method integrating PF-criteria importance through inter-criteria correlation (CRITIC) is presented to calculate the priority of each supplier, which can capture the inter-correlations between criteria. Finally, a numerical example of SFSS is implemented to show the application of the proposed synthetic decision approach. Subsequently, the sensitivity analysis of distance parameters and comparison analysis among different SFSS approaches were conducted to test the rationality and advantages of the proposed framework for resolving the SFSS problem. The results show that the reported method can provide a practical way to resolve the SFSS problems with uncertain data.

Medical imaging systems installed in different hospitals and labs generate images in bulk, which could support medics to analyze infections or injuries. Manual inspection becomes difficult when there exist more images, therefore, intelligent systems are usually required for real-time diagnosis. Melanoma is one of the most common and severe forms of skin cancer that begins from the cells beneath the skin. Through dermoscopic images, it is possible to diagnose the infection at the early stages. In this regard, different approaches have been exploited for improved results. In this study, we propose a two-stream deep neural network information fusion framework for multiclass skin cancer classification. The proposed technique follows two streams: initially, a fusion-based contrast enhancement technique is proposed, which feeds enhanced images to the pretrained DenseNet201 architecture. The extracted features are later optimized using a skewness-controlled moth–flame optimization algorithm. In the second stream, deep features from the fine-tuned MobileNetV2 pretrained network are extracted and down-sampled using the proposed feature selection framework. Finally, most discriminant features from both networks are fused using a new parallel multimax coefficient correlation method. A multiclass extreme learning machine classifier is used to classify lesion images. The testing process is initiated on three imbalanced skin data sets—HAM10000, ISBI2018, and ISIC2019. The simulations are performed without performing any data augmentation step in achieving an accuracy of 96.5%, 98%, and 89%, respectively. A fair comparison with the existing techniques reveals the improved performance of our proposed algorithm.

The mutually beneficial blend of artificial intelligence with internet of things has been enabling many industries to develop smart information processing solutions. The implementation of technology enhanced industrial intelligence systems is challenging with the environmental conditions, resource constraints and safety concerns. With the era of smart homes and cities, domains like automated license plate recognition (ALPR) are exploring automate tasks such as traffic management and fraud detection. This paper proposes an optimized decision support solution for ALPR that works purely on edge devices at night-time. Although ALPR is a frequently addressed research problem in the domain of intelligent systems, still they are generally computationally intensive and unable to run on edge devices with limited resources. Therefore, as a novel approach, we consider the complex aspects related to deploying lightweight yet efficient and fast ALPR models on embedded devices. The usability of the proposed models is assessed in real-world with a proof-of-concept hardware design and achieved competitive results to the state-of-the-art ALPR solutions that run on server-grade hardware with intensive resources.