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The abnormalities of glaucoma have high impact on deciding and representing the causes that effects severity of blindness in human beings. The simulation experimental results would help the ophthalmologist in diagnosing of glaucoma abnormality accurately. The significant effect of glaucoma has a huge impact on the quality of human life, and its growth rate in world population tremendously increases. Glaucoma is considered as second largest cause for the blindness in the world; hence identification of it marks the importance of its detection at the earliest.

The prime objective of the work proposed is to build up a human intervention free image preparing framework for glaucoma screening. The disc calculation is assessed on retinal image dataset called retinal Image for glaucoma Analysis. The proposed method briefs a novel optic disc division calculation depending on applying a level-set strategy on a confined optic disc image. In the instance of low quality image, a twofold level set is designed, in which the principal level set is viewed as restriction for the optic disc. To keep the veins from meddling with the level-set procedure, an inpainting strategy has been applied. Also a significant commitment is to include the varieties in notion adopted by the ophthalmologists in distinguishing the disc localization and diagnosing the glaucoma. Most of the past investigations are prepared and tested depending on just a single feature, which can be thought to be one-sided for the ophthalmologist.

In continuation, the correctness has been determined depending on the quantity of image that matched with the investigation pattern adopted by the ophthalmologist. The 175 retinal images were utilized to test the results of proposed work with the manual markings of ophthalmologists. The error-free calculation in marking the optic disc region and centroid was 98.95% in comparison with the existing result of 87.34%.

In continuation, the correctness has been determined depending on the quantity of image that matched with the investigation pattern adopted by the ophthalmologist. The 175 retinal images were utilized to test the results of proposed work with the manual markings of ophthalmologists. The error-free calculation in marking the optic disc region and centroid was 98.95% in comparison with the existing result of 87.34%.

When a large number of project proposals are evaluated to allocate available funds, grouping them based on their similarities is beneficial. Current approaches to group proposals are primarily based on manual matching of similar topics, discipline areas and keywords declared by project applicants. When the number of proposals increases, this task becomes complex and requires excessive time. This paper aims to demonstrate how to effectively use the rich information in the titles and abstracts of Turkish project proposals to group them automatically.

This study proposes a model that effectively groups Turkish project proposals by combining word embedding, clustering and classification techniques. The proposed model uses FastText, BERT and term frequency/inverse document frequency (TF/IDF) word-embedding techniques to extract terms from the titles and abstracts of project proposals in Turkish. The extracted terms were grouped using both the clustering and classification techniques. Natural groups contained within the corpus were discovered using k-means, k-means++, k-medoids and agglomerative clustering algorithms. Additionally, this study employs classification approaches to predict the target class for each document in the corpus. To classify project proposals, various classifiers, including k-nearest neighbors (KNN), support vector machines (SVM), artificial neural networks (ANN), classification and regression trees (CART) and random forest (RF), are used. Empirical experiments were conducted to validate the effectiveness of the proposed method by using real data from the Istanbul Development Agency.

The results show that the generated word embeddings can effectively represent proposal texts as vectors, and can be used as inputs for clustering or classification algorithms. Using clustering algorithms, the document corpus is divided into five groups. In addition, the results demonstrate that the proposals can easily be categorized into predefined categories using classification algorithms. SVM-Linear achieved the highest prediction accuracy (89.2%) with the FastText word embedding method. A comparison of manual grouping with automatic classification and clustering results revealed that both classification and clustering techniques have a high success rate.

The proposed model automatically benefits from the rich information in project proposals and significantly reduces numerous time-consuming tasks that managers must perform manually. Thus, it eliminates the drawbacks of the current manual methods and yields significantly more accurate results. In the future, additional experiments should be conducted to validate the proposed method using data from other funding organizations.

This study presents the application of word embedding methods to effectively use the rich information in the titles and abstracts of Turkish project proposals. Existing research studies focus on the automatic grouping of proposals; traditional frequency-based word embedding methods are used for feature extraction methods to represent project proposals. Unlike previous research, this study employs two outperforming neural network-based textual feature extraction techniques to obtain terms representing the proposals: BERT as a contextual word embedding method and FastText as a static word embedding method. Moreover, to the best of our knowledge, there has been no research conducted on the grouping of project proposals in Turkish.

The Metaverse and Internet of Things (IoT) have emerged like a tidal wave, and it is creating a transformative impact on society and industry. The metaverse and IoT changed the way companies were operating earlier and customers were living their lives. On the other hand, Metaverse enriches the customer experience by offering a matchless virtual experience using augmented reality and state-of-the-art technology. The metaverse and the IoT can be used in various sectors such as manufacturing, transportation, retailing, health care, banking, and automobiles to make cities smart. Metaverse and IoT provide real-time data, reduces operational cost and errors, improves efficiency, and helps industries to make intelligent decisions. Although the IoT and Metaverse offer significant benefits, it is not free from limitations. Ethical dilemmas, privacy issues, data breaches, and difficulty in extracting relevant data impose serious challenges that need to be addressed. There is an urgent and dire need to create a trade-off between the interest of the business and the privacy and security of customers. This chapter aims to discover the potential of Metaverse and IoT in various sectors (e.g., healthcare, transportation, and electronics). This study will bring significant insights to researchers and policymakers by exploring the likely benefits of IoT and metaverse in diverse sectors to develop smart cities. This chapter will also explain the challenges of metaverse and IoT, which can be addressed by integrating data analytics tools optimally and efficiently.

Road freight transportation companies need to take advantage of information and communication technologies to develop capabilities. This study proposes a framework to guide road freight transportation companies to achieve data visibility in their operations by developing such capabilities. By proposing this framework, this research contributes to literature and practice, highlighting the capabilities and the respective supporting technologies for improved data visibility in road freight transportation.

A mixed-method approach is used to develop the framework, considering three methodological steps. In phase 1, the capabilities are identified in the literature and validated by experts. In phase 2, an empirical assessment of cause–effect relationships between capabilities is performed using a multiple case study and DEMATEL. Lastly, in phase 3, an analysis of the cause model and significant associations is conducted to enable the development of the framework. In addition, the proposed framework was validated by the experts interviewed.

The results provide a framework that explains the link between the technology-enabled data visibility capabilities in road freight transportation operations. In addition, a pathway was established that road freight transportation companies could follow to achieve data visibility in their operations by developing such capabilities.

This work develops the first framework that provides a path for data visibility in road freight transportation operations from adopting certain technologies. The insights are compelling for researchers and practitioners to optimize the decision-making process for adopting technologies and developing capabilities related to data visibility.

The purpose of this paper is to invent a new functional coated fabric based on nanomaterials to shield UV and IR. Multifunctional surface coatings with ultraviolet (UV)/near infrared radiations protection and waterproof were widely applied in outdoor fabrics. Herein, ultrafine TiO2 and nano-antimony doped tin dioxide (ATO) were prepared and embedded into water-based polyurethane (PU) coatings and then coated on the nylon fabric.

ATO was prepared using the sol–gel method and the two powders were dispersed by ball milling. The results of zeta potential and particle size distribution showed that the ultrafine TiO2 and nano-ATO could be stably dispersed in water at pH 8 with the presence of sodium polycarboxylate. The optimal process was screened out by orthogonal design and scanning electron microscopy (SEM), UV protection, thermal insulation and water-pressure resistance were tested. SEM images indicated the nanoparticles could be uniformly dispersed in the coatings.

The effect of UV prevention can get to UPF > 50, UVA < 5 per cent, which meet up with the AATCC 183-2014. Coatings can effectively lower the temperature of fabric surface by 8∼9ºC through the self-made closed test system and by 3ºC through the open test system.

These PU coatings are environment-friendly and adhesive to impart waterproof, UV-proof and thermal insulation properties to nylon fabrics by coating finishing.