There is a substantial worldwide effect, both in terms of disease and death, that is caused by pediatric pneumonia, which is a disorder that affects children under the age of five. Even while Streptococcus pneumoniae is the most prevalent agent responsible for this sickness, it may also be brought on by other bacteria, viruses, or fungi. An efficient approach utilizing deep-learning methods to forecast pediatric pneumonia reliably using chest X-ray images has been developed. The current study presents an updated version of the DenseNet-121 deep-learning model developed for identifying scans of pediatric pneumonia. The batch normalization, maximum pooling, and dropout layers introduced into the standard model were done so to improve its accuracy. The activations of the preceding layers are scaled and normalized using batch normalization, leading to a mean value of zero and a variance of one. This helps to decrease internal variability during training, which in turn speeds up the training process, promotes model stability, and improves the model’s overall capacity to generalize. Max pooling is a beneficial technique for cutting down on the number of model parameters, making the model more computationally effective. Meanwhile, dropout is a preventative measure against overfitting by decreasing the co-dependence of neurons. As a result, the network acquires more durable and adaptive features. Classifying instances of pediatric pneumonia with the help of the proposed model resulted in an exceptional accuracy rate of 97.03%.

6G mobile network technology will set new standards to meet performance goals that are too ambitious for 5G networks to satisfy. The limitations of 5G networks have been apparent with the deployment of more and more 5G networks, which certainly encourages the investigation of 6G networks as the answer for the future. This research includes fundamental privacy and security issues related to 6G technology. Keeping an eye on real-time systems requires secure wireless sensor networks (WSNs). Denial of service (DoS) attacks mark a significant security vulnerability that WSNs face, and they can compromise the system as a whole. This research proposes a novel method in blockchain 6G-based wireless network security management and optimization using a machine learning model. In this research, the deployed 6G wireless sensor network security management is carried out using a blockchain user datagram transport protocol with reinforcement projection regression. Then, the network optimization is completed using artificial democratic cuckoo glowworm remora optimization. The simulation results have been based on various network parameters regarding throughput, energy efficiency, packet delivery ratio, end–end delay, and accuracy. In order to minimise network traffic, it also offers the capacity to determine the optimal node and path selection for data transmission. The proposed technique obtained 97% throughput, 95% energy efficiency, 96% accuracy, 50% end–end delay, and 94% packet delivery ratio.

Small and medium-sized enterprises (SMEs) are considered to be the world’s largest. They play a vital role as they create jobs and improve the living conditions of their local communities as they contribute to the country’s GDP growth. Due to their contributions to the nation’s economy, they have been given much attention in entrepreneurship. This paper aims to study the impact of entrepreneurial education and entrepreneurial competencies on small and medium enterprises’ performance. Entrepreneurship is a vital component of any successful business strategy. It can be used to overcome the uncertainties of today’s business environment. The roles of these two independent factors in the business environment are also more relevant to small businesses. The goal of this paper is to develop a framework that explores the performance of firms from the perspective of their entrepreneurial education and entrepreneurial competencies. The proposed framework aims to provide a comprehensive view of the factors that influence the performance of businesses. It shows that entrepreneurial education is the antecedent of competencies that can affect firm performance, where entrepreneurial competencies is treated as a mediating variable. In addition, this study explores the role of entrepreneurial education and entrepreneurial competencies in developing financial and non-financial business performance. The framework is based on the resource-based view, a constantly applied approach to the small business context to utilize strengths and weaknesses to achieve sustainable competitive advantage. The paper provides valuable insights into the entrepreneurial factors that businesses can consider when enhancing their performance. It also helps entrepreneurs in developing their entrepreneurial education and competencies to increase their chances of achieving better results in their businesses which in return can help to reduce the number of business failures and thus, boost the country’s economy.

Semantic segmentation of cityscapes via deep learning is an essential and game-changing research topic that offers a more nuanced comprehension of urban landscapes. Deep learning techniques tackle urban complexity and diversity, which unlocks a broad range of applications. These include urban planning, transportation management, autonomous driving, and smart city efforts. Through rich context and insights, semantic segmentation helps decision-makers and stakeholders make educated decisions for sustainable and effective urban development. This study investigates an in-depth exploration of cityscape image segmentation using the U-Net deep learning model. The proposed U-Net architecture comprises an encoder and decoder structure. The encoder uses convolutional layers and down sampling to extract hierarchical information from input images. Each down sample step reduces spatial dimensions, and increases feature depth, aiding context acquisition. Batch normalization and dropout layers stabilize models and prevent overfitting during encoding. The decoder reconstructs higher-resolution feature maps using "UpSampling2D" layers. Through extensive experimentation and evaluation of the Cityscapes dataset, this study demonstrates the effectiveness of the U-Net model in achieving state-of-the-art results in image segmentation. The results clearly shown that, the proposed model has high accuracy, mean IOU and mean DICE compared to existing models.

Users of social media may use words and phrases literally to convey their views or opinion clearly. However, some people choose to utilise idioms or proverbs that are implicit and indirect in order to make a stronger impression on the audience or perhaps to catch their attention by utilising a funny, sarcastic, or metaphorical phrases. Idioms and proverbs are examples of figurative expressions with a thematically coherent totality that cannot be understood literally. In a previous work, the extension of IBM’s Sentiment Lexicon of Idiomatic Expressions was proposed to include around 9,000 idioms; both lexicons are manually annotated by crowdsourcing service. Therefore, in this research, we provide knowledge-based expansion approach to avoid human annotation of idioms. For sentiment classification, the proposed method has the advantage that it does not require any fine-tuning for the BERT model. Experimental comparisons show that the automated idiom enrichment and annotation are very beneficial for the performance of the sentiment classifier. The expanded annotated lexicon will be made available to the general public.