bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparative Analysis of Functionality and Aspects for Hybrid Recommender Systems
Authors: Shrivastava Vineet , Kumar Suresh 
Published: September 2023

Recommender systems are gradually becoming the backbone of profitable business which interact with users mainly on the web stack. These systems are privileged to have large amounts of user interaction data used to improve them. The systems utilize machine learning and data mining techniques to determine products and features to suggest different users correctly. This is an essential function since offering the right product at the right time might result in increased revenue. This paper gives focus on the importance of different kinds of hybrid recommenders. First, by explaining the various types of recommenders in use, then showing the need for hybrid systems and the multiple kinds before giving a comparative analysis of each of these. Keeping in mind that content-based, as well as collaborative filtering systems, are widely used, research is comparatively done with a keen interest on how this measures up to hybrid recommender systems.
Evaluating Text-to-Image GANs Performance: A Comparative Analysis of Evaluation Metrics
Authors: Kumar K. Dinesh , Srang Sarot , Valy Dona 
Published: September 2023

Generative Adversarial Networks (GANs) have emerged as powerful techniques for generating high-quality images in various domains but assessing how realistic the generated images are is a challenging task. To address this issue, researchers have proposed a variety of evaluation metrics for GANs, each with its own strengths and limitations. This paper presents a comprehensive analysis of popular GAN evaluation metrics, including FID, Mode Score, Inception Score, MMD, PSNR, and SSIM. The strengths, weaknesses, and calculation processes of these metrics are discussed, focusing on assessing image fidelity and diversity. Two approaches, pixel distance, and feature distance, are employed to measure image similarity, while the importance of evaluating individual objects using input captions is emphasized. Experimental results on a basic GAN trained on the MNIST dataset demonstrate improvement in various metrics across different epochs. The FID score decreases from 497.54594 at Epoch 0 to 136.91156 at Epoch 100, indicating improved differentiation between real and generated images. In addition, the Inception Score increases from 1.1533 to 1.6408, reflecting enhanced image quality and diversity. These findings highlight the effectiveness of the GAN model in generating more realistic and diverse images with training progression. However, when it comes to evaluating GANs on complex datasets, challenges arise, highlighting the need to combine evaluation metrics with visual inspection and subjective measures of image quality. By adopting a comprehensive evaluation approach, researchers can gain a deeper understanding of GAN performance and guide the development of advanced models.
Skin Cancer Detection using CNN (VGG16) inculcated with CLAH and Gaussian Filter
Authors: Mallick Shweta , Mishra S P 
Published: September 2023

Many techniques related to image analysis have been proposed by researchers which are being used to detect a large number of diseases. These images are carefully analyzed by radiologists and doctors, and after careful interpretation, the results are obtained which finally help in making an appropriate diagnosis. This is a complicated and time consuming task, which requires high levels of concentration. Therefore, the experts who analyze the images mustn't suffer from fatigue or other common problems that can impair their performance. The present study attempts to reveal how a deep learning model using CNN with VGG16 is effective for the diagnosis and detection of skin cancer at its early stages. Therefore under the scheme, the 4000 images of raw skin cancer tissues are evaluated. The diagnostic model starts with pre-processing of images using the CLAHE along with the inculcation of the Gaussian filter. Thereafter, using hyper-parameter optimizer stochastic gradient descent, along with the effective learning rate 0.001, incorporating the training epochs of 50 nos. and pertaining the batch size 32 is formed. Consequently, as a result, the accuracy achieved is 99.70%, with a loss value of 0.0055%, a precision of 99.75%, a recall of 99.75%, and an f1-score of 99.50% respectively.
Diagnosis of Rice Diseases using Canny Edge K-means Clustering and Convolutional Neural Network based Transfer Learning
Authors: Seelwal Pardeep , Rohilla Tilak Raj 
Published: September 2023

Recent breakthroughs in deep learning-based convolutional neural networks have significantly improved image categorization accuracy. Deep learning-based techniques for diagnosing illnesses from rice plant images have been created in this work, inspired by the realisation of CNNs in image classification. Smart monitoring technologies for the automatic identification of plant diseases are extremely beneficial to sustainable agriculture. Despite the fact that various mechanisms for plant disease categorization have been created in recent years, an inefficient technique based on evidence from picture samples is of concern for ground environments. In this study, an image processing technique for pre-processing and segmentation was used, as well as a multi-class convolutional neural network with transfer learning, to classify rice plant leaf diseases such as brown spot, hispa, leaf blast, and healthy class. The contaminated area was automatically separated from the healthy areas of the image using canny edge detection and k-means clustering, and the features were retrieved using the CNN model. In the experimental results, the CNN model without transfer learning is compared to the transfer learning model. VGGNet transfer learning is used to construct a multi-classification framework for each class of rice illness. The overall accuracy acquired by the CNN model without transfer learning is 92.14%, whereas the accuracy obtained by the transfer learning model is 94.80%.The current work demonstrates that the proposed technique is compelling and capable of recognizing rice plant illness for four classes.
Security Aware Virtual Machine Allocation Policy to Improve QoS
Authors: Bhonde Aparna Prashant , Devane Satish 
Published: September 2023

Cloud service providers find managing the energy consumption for datacentres as a critical operation. Significant energy is being used by a rising spike in the number of data centres. To overcome this challenge datacentres, attempt to reduce the number of active physical servers by carrying out virtual machine consolidation process. However, due to inadequate security measures to verify hostile cloud users, the security threats on cloud multitenancy platform have escalated. In this paper we propose energy efficient virtual machine consolidation using priority-based security aware virtual machine allocation policy to improve datacentre security. The proposed security solution considers the host threat score before virtual machine placement, which has reduced the security threats for co-residency attacks without impacting datacentre energy consumption.
An Integrated Framework for the Detection of Lung Nodules from Multimodal Images Using Segmentation Network and Generative Adversarial Network Techniques
Authors: C. Vinod , D. Menaka 
Published: September 2023

Medical imaging techniques are providing promising results in identifying abnormalities in tissues. The presence of such tissues leads to further investigation on these cells in particular. Lung cancer is seen widely and is deadliest in nature if not detected and treated at an early stage. Medical imaging techniques help to identify the presence of suspicious tissues like lung nodules effectively. But it is very difficult to know the presence of the nodule at an early stage with the help of a single imaging modality. The proposed system increases the efficiency of the system and helps to identify the presence of lung nodules at an early stage. This is achieved by combining different methods for reaching a common outcome. Multiple schemes are combined and the extracted features are used for obtaining a conclusion. The accuracy of the system and the results depend on the quality and quantity of the authentic training data. But the availability of the data from an authentic source for the study is a challenging task. Here the generative adversarial network (GAN), is used as a data source generator. It helps to generate a huge amount of reliable data by using a minimum number of real time and authentic data set. Images generated by the GAN are of resolution 1024 x 1024.Fine tuning of the images by using the real images increases the quality of the generated images and thereby improving the efficiency. Luna 16 is the primary data source and these images are used for the generation of 1000000 images. Training process with the huge dataset improves the capability of the proposed system. Various parameters are considered for evaluating the performance of the proposed system. Comparative analysis with existing systems highlights the strengths of the proposed system.
Securing the Skies: Cybersecurity Strategies for Smart City Cloud using Various Algorithams
Authors: Padmalal S. , Dayanand I. Edwin , Rao Goda Srinivasa , Reddy T Sunilkumar , Ravuri Ananda , C Vanmathi , Gore Santosh 
Published: September 2023

As smart cities continue to evolve, their reliance on cloud computing technologies becomes increasingly apparent, enabling the seamless integration of data-driven services and urban functionalities. However, this transformation also raises concerns about the security of the vast and interconnected cloud infrastructures that underpin these cities' operations. This paper explores the critical intersection of cloud computing and cybersecurity within the context of smart cities.
This research is dealing with challenges posed by the rapid expansion of smart city initiatives and their reliance on cloud-based solutions. It investigates the vulnerabilities that emerge from this technological convergence, emphasizing the potential risks to data privacy, urban services, and citizen well-being. The abstract presents a comprehensive overview of the evolving threat landscape that smart cities face in the realm of cloud computing.
To address these challenges, the abstract highlights the importance of proactive cybersecurity strategies tailored specifically to the unique needs of smart cities. It underscores the significance of adopting a multi-layered approach that encompasses robust encryption protocols, intrusion detection systems, threat intelligence sharing, and collaborative efforts among stakeholders. Drawing insights from existing research and real-world case studies, the abstract showcases innovative solutions that leverage advanced technologies like artificial intelligence and blockchain to fortify the security posture of smart city cloud infrastructures. It explores the role of data governance, user authentication, and anomaly detection in creating a resilient cybersecurity framework that safeguards critical urban systems.
Coronally repositioned flap procedure for gingival root coverage using human derived regenerative membranes - A case series
Authors: Mutthineni Ramesh Babu , Ramya Sri Lakshmi NCH. , Arpita Paul R. , Muralidharan Gayathri , Madhulatha  , Duddukuri Hema , Alekhya Burli VV 
Published: September 2023

The aim of this case series was to evaluate the effectiveness of platelet rich fibrin (PRF), Amnion and chorionic membrane for root coverage procedures using a coronally repositioned flap (CRF).Three subjects with Miller’s class I gingival recession were randomly selected for treatment using CRF with PRF, Amnion and Chorion membranes. The clinical parameters evaluated at baseline and 6 months follow up included: probing depth (PD), relative attachment level (RAL), recession height (RH), recession width (RW), width of keratinized tissue (WKT) and Gingival thickness (GT). Within the limits, this case series has shown that the use of human derived regenerative membranes along with CRF resulted in complete root coverage in Miller’s Class I gingival recessions in much lesser time. The rapid healing and regeneration of these membranes stands as an alternative for connective tissue autografts and holds promise for future.Gingival thickness (GT), Relative Attachment level (RAL), Width of Keratinized Tissue (WKT)
Retrato estatístico e demográfico das cooperativas em Portugal
Authors: Pedroso Eduardo 
Published: September 2023

Las cooperativas, al combinar la actividad económica con los principios de solidaridad, equidad y democracia, desempeñan un papel esencial en la creación de riqueza y el refuerzo de la cohesión social y el desarrollo sostenible. Este estudio, al reunir las principales estadísticas sobre el sector cooperativo portugués, pretende presentar una imagen lo más completa posible de la actividad del sector en Portugal y de su posición en la economía nacional, así como caracterizar otros ámbitos de su actividad. Estos datos permiten así, entre otros aspectos, mostrar un sector resiliente, con alta participación de la sociedad, que, a pesar de algunas debilidades en términos de gestión organizativa, demuestra una fuerte contribución económica en múltiples sectores, genera empleo duradero e inclusivo, y es un actor importante en el desarrollo sostenible.
Recibido: 5 mayo 2023Aceptado: 12 junio 2023
Integração de lacunas do Código Cooperativo-paradoxo ou o futuro do direito cooperativo português?
Authors: Ramos Maria Elisabete 
Published: September 2023

El presente artículo, partiendo del régimen jurídico cooperativo de la responsabilidad civil de los consejeros con respecto a la cooperativa, pretende testar la autonomía sustancial del derecho cooperativo en relación con el derecho societario. El Código Cooperativo portugués determina que el Código de Sociedades Mercantiles y, en particular, la normativa de las sociedades anónimas es una ley subsidiariamente aplicable a las cooperativas, siempre que sea conforme a los principios cooperativos. El artículo concluye que el cumplimiento de los principios cooperativos no es suficiente en sí mismo para frenar la societarización del derecho cooperativo portugués y que la aplicación subsidiaria del derecho de las sociedades anónimas introduce en las cooperativas soluciones normativas que no son decididas por el legislador cooperativo.
Recibido: 5 mayo 2023Aceptado: 9 junio 2023
Training of specialists to provide laboratory support in the field of personalized medicine at the Almazov Centre
Authors: Vavilova T. V. , Sirotkina O. V. 
Published: September 2023

The system of personnel training for work in modern clinical diagnostic laboratories should meet the challenges that the development of personalized medicine sets for the medical community.Traditionally, departments and courses of clinical laboratory diagnostics in medical higher educational institutions as part of the training of highly qualified personnel — residency and postgraduate studies, carry out such work.The clinical orientation of laboratory diagnostics and its special role in providing personalized medicine urgently requires the close integration with clinical departments, which is implemented in the Almazov National Medical Research Centre within the framework of the University Clinic.
Duplex RTqPCRs for detection and relative quantification of SARS-CoV-2 variants of concern (VOC)
Authors: Carcereny Albert , Garcia-Pedemonte David , Bosch Albert , Pintó Rosa M , Guix Susana 
Published: September 2023

Abstract

During the evolution of the SARS-CoV-2 pandemic, new variants of the virus have emerged and spread worldwide. The increased transmissibility and proclivity of some of these variants to cause more serious disease threatens public health responses against the virus, and they are classified as variants of concern (Variants of Concern, VOCs). While Next-Generation-Sequencing (NGS) is the gold standard to identify VOC, it cannot always be rapidly implemented in some settings to provide information as an early warning tool. Duplex quantitative real time RTqPCR assays offer a sensitive and easy-to-use tool to detect, discriminate, and estimate relative proportions of SARS-CoV-2 variants containing VOC-specific signature mutations from variants lacking it, using allelic discrimination probes. We developed eight multiplexed RTqPCR assays that can detect Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), Omicron (B.1.1.529), Omicron BA.1 (B.1.1.529.1), Omicron BA.2 (B.1.1.529.2) and Omicron BA.2.86 (B.1.1.529.2.86) VOCs by targeting 21765_21770DelTACATG, 22281_22289DelCTTTACTTG, 28262_28263InsAACA, 22029_22034DelAGTTCA, 28362_28370DelGAGAACGCA, 22121InsGAGCCAGAA, 21633_21641DelTACCCCCTG and ins21610ATGCCGCTGTTTdeletions/insertions in their genomes, respectively. Alpha, Beta, Delta, Omicron BA.1, Omicron BA.2 and Omicron BA.2.86 markers are mapped to the S gene (residues 69/70, 241/243, 157/158, 214, 25/27 and 16 respectively), Gamma insertion is located between the end of ORF8 and the beginning of N gene, and Omicron (B.1.1.529) deletion is mapped to the N gene (residues 31/33). The duplex RTqPCR assay targeting 21765-21770DelTACATG mutation affecting residues HV69/70 has been previously used to estimate the relative proportion of Alpha VOC (Carcereny et al., 2021), and is also suitable to estimate the proportions of Omicron BA.4 and BA.5. All duplex RTqPCR assays targeting signature mutations of VOCs may be used as a complementary tool to NGS for rapid variant tracking and surveillance in wastewater-based epidemiology.
A protocol for a systematic review on motivation assessments in ABI patients: a survey of available tools
Authors: Rodarie Béatrice , Ribault Shams , Mateo Sébastien , Chabanat Eric , Havé Laurence , Luauté Jacques , Jacquin-Courtois Sophie , Reilly Karen T. 
Published: September 2023

Abstract
Acquired Brain Injury (ABI) is a term that covers several aetiologies, including stroke and traumatic brain injury. Patients with ABI can experience a range of symptoms, both physical and cognitive, that can limit their quality of life and participation. Specialised rehabilitation centres can help patients improve their capacities and develop compensatory strategies that enable them to more fully participate in social and family roles and to have a higher quality of life. The rehabilitation process is, however, long and challenging, and success requires active participation of the patient and collaboration between the patient and the rehabilitation team. One essential element of this active participation is the patient’s motivation to improve and to engage in rehabilitation. Even though it is essential in the rehabilitation process, motivation is often measured subjectively. The aim of this review is to survey the tools that have been used to measure motivation in the context of rehabilitation.
Following the PRISMA guidelines, we will conduct a systematic review including four databases: PubMed, Embase, Web of Science, Google Scholar. Selected studies will include all types of studies related to motivation and rehabilitation (e.g., randomized, non-randomized controlled trials, case reports …). Studies involving children and elderly people will be excluded as well as articles regarding traumatic brain injury in the context of sport concussion without subsequent rehabilitation. The data will be classified according to patient population, rehabilitation context, and tools used to assess motivation. We will summarise these data and make recommendations for clinicians and researchers regarding which tools to use in which contexts. We will also identify potential future needs in terms of tool development and/or cross-cultural validation of existing tools.
Multiplex CRISPR screening to identify E3 ligase substrates
Authors: Timms Richard T , Mena Elijah L , Elledge Stephen J 
Published: September 2023

Abstract
The ubiquitin-proteasome system (UPS) is the primary route through which the cell achieves selective protein degradation. Specificity is thought to be primarily achieved via the E3 ubiquitin ligases, but for many E3s their substrates remain largely unknown. Thus, we have developed a multiplex CRISPR screening platform to assign E3 ligases to their cognate substrates at scale. This approach combines GPS expression screens with loss-of-function CRISPR screening to simultaneously identify the E3 ligases responsible for the instability of hundreds of GFP-tagged substrates in a single experiment. This approach is compatible with both full-length protein substrates and peptide substrates fused to GFP, can accommodate starting pool of substrates of varying stabilities, and, when combined with saturation mutagenesis, can assign E3 ligases to their cognate degron motifs. In total, a typical multiplex screen can be completed in a few months.
How Will Climate-Induced Migration Affect the Future Distribution of Population in the US?
Authors: Carbone Jared , Jiang Leiwen , O'Neill Brian , Chen Yuzhou , Zoraghein Hamidreza 
Published: September 2023

Abstract
Climate change is a potentially important driver of migration within the US but its impact on future population distribution remains under-explored. We project the impact of climate change over the 21st century on state-level population by integrating a detailed demographic model with a model of migration responses to changes in temperature and their consequences for regional economic conditions. We find that climate change is unlikely to fundamentally alter the large-scale distribution of people across the US, but it could have important consequences for some states. End-of-century population in states projected to experience either large, direct climate impacts or large inflows of climate migrants is altered by 10-30% relative to comparable projections where this influence is omitted. The dominant pattern of climate-induced migration is from the South to the Northeast and West with the pattern driven by the relative change in climate experienced across locations, distance, and the changes in regional economic conditions wrought by migration itself. The follow-on effects of migration on natural growth also influence state population change. Climate scenarios which produce the greatest degree of warming do not unambiguously produce the largest changes in population, because widespread warming results in relatively few safe havens, rendering migration an ineffective form of adaptation to climate change.