bluuebunny/crossref_metadata_2025 huggingface dataset:
URBAN PLANNING AND HEALTH
Authors: Filippov Vasily
Published: May 2023

The evolution of ideas about the protection of the health of city residents is shown, starting with the emergence of the very science of urban planning in the last quarter of the 19th century. If in the works of its founder, Reinhard Baumeister, in relation to the health of citizens, the main attention was paid to their physical condition, namely sanitation and hygiene, then in the works of Camillo Sitte and Josef Stbben, who completed the formation of classical urban planning science, attention began to be paid not only to their physical, but and mental health and well-being. Ebenezer Howards idea of a garden city, which appeared as if in opposition to classical urban planning, actually turned out to be its development, as indicated by the successful implementation of the settlements of the Weimar Republic and Clarence Perrys neighborhood units, as examples of the integration of Howards ideas directly into the big city. Separately, the architecture of Swiss mountain resorts is considered, which is entirely subordinated to the promotion of health, and therefore had a direct impact on the entire architecture of the 20th century and its urban planning. In terms of time, this study is limited by the beginning of the global economic crisis of 1929 and the Great Depression, which resulted in radical changes in urban planning, due to which the issues of physical and mental health of city residents faded into the background.
NESNELLEŞTİRME TEORİSİNE DAYALI CEBİR ÖĞRETİMİ: ÖZDEŞLİKLER DURUMU
Authors: Okur Duygu , Akıncı Mustafa
Published: May 2023

Bu araştırmada nesnelleştirme teorisine göre hazırlanmış bir öğrenme ortamında sekizinci sınıf öğrencilerinin özdeşlikleri elde etme sürecinin incelenmesi amaçlanmaktadır. Bu amaç doğrultusunda, nesnelleştirme teorisine dayalı bir öğrenme ortamında akıl yürütmelerin ve sosyal süreçlerin derinlemesine incelenebilmesi için nitel araştırma yaklaşımlarından sınıf öğretim deneyi yöntemi kullanılmıştır. Araştırma Bitlis ilinde öğrenim görmekte olan 12 sekizinci sınıf öğrencisi ile gerçekleştirilmiştir. Çalışma grubu kolay ulaşılabilir örneklem tekniği ile seçilmiştir. Araştırmada sekizinci sınıf özdeşlikler konusuna yönelik nesnelleştirme teorisine uygun olarak 3 etkinlik hazırlanmıştır. Uygulama süreci, etkinliklerin tanıtılması ile başlayıp küçük grup tartışması ve gruplar arası tartışmalar ile devam etmiş ve sınıf tartışmasıyla sonuçlandırılmıştır. Araştırma sürecinde veri toplama aracı olarak öğrenci etkinlik dokümanları, etkinlik sürecine ait ses ve video kayıtları kullanılmıştır. Uygulama sürecinde elde edilen veriler içerik analizi yöntemi ile analiz edilmiş ve etkinlik süreci öğrencilerin akıl yürütmeleri, hataları ve etkinlik sürecinde yaşanan sosyal süreçler açısından incelenmiştir. Elde edilen bulgulara göre öğrencilerin akıl yürütmelerle cebir ve geometri ilişkisini kurabildikleri ve problemlere uygun stratejiler geliştirebildikleri görülmüştür. Öğrencilerin işbirliği ile problemlere çözüm ürettikleri, fikir ayrılıklarından dolayı zaman zaman çatışma yaşadıkları ve yaptıkları çözümler konusunda birbirlerini ikna etmeye çalıştıkları gözlemlenmiştir. Öğretmen öğrenci arasında gerçekleşen sosyal süreç incelendiğinde öğretmenin öğrencilere zorlandıkları durumlarda rehberlik ettiği ve gerekli yerlerde ipuçları verdiği tespit edilmiştir. Ayrıca öğretmenin sorular sorarak, öğrencilerin problem hakkındaki düşüncelerini anlamaya, ön bilgilerini hatırlatmaya ve hatalarını fark ettirmeye çalıştığı saptanmıştır. Sonuç olarak nesnelleştirme teorisine dayalı bir öğrenme ortamının birçok faktörden oluşan ve etkilenen dinamik bir ortam olduğu tespit edilmiştir. İleride yapılacak olan çalışmalarda etkinlik sürecinin farklı bağlamlarda gerçekleştirilmesi ve göstergebilimsel anlamda incelenmesi önerilmektedir.
Role of Government Policies on Success of New Ventures: An Empirical Study
Authors: Saini Ajay Kumar
Published: May 2023

New venture success in India is greatly influenced by government policy. The Indian government has put into effect a number of laws and measures to encourage entrepreneurship and foster an atmosphere that is favorable for startups. The development of new businesses in the nation has been significantly influenced by the policies the government has put in place.The Startup India project, which was introduced in 2016, is one of the important regulations that the government has put in place. Through tax breaks, financial opportunities, and streamlined compliance procedures, this effort strives to help and encourage businesses throughout the nation. To encourage entrepreneurship among women and marginalized communities, the government has also developed a number of plans and programmes, such as the Stand-Up India scheme.In addition to helping the overall expansion of new enterprises in the nation, the project has given these groups financial support to launch their businesses.The government has also loosened FDI regulations, streamlined the business registration process, and implemented bankruptcy rules to make it simpler for new businesses to operate in the nation. The development of a supportive environment for entrepreneurs in India has been greatly aided by these regulations.
Key Success Factors of Saving Behaviour and Retirement Planning: A Cross-Sectional Study
Authors: Punj Divya
Published: May 2023

The main success elements for retirement planning and saving behaviour in India are essential for guaranteeing financial security in the post-work years. It's critical to have a clear grasp of the significance of retirement savings. People can be inspired to take action by being informed about the long-term advantages of saving and the possible repercussions of poor planning. The importance of financial literacy cannot be overstated. By making readily available information and tools on several retirement savings choices, including pensions, mutual funds, and provident funds, people are better equipped to make educated decisions and select the best investment vehicles. Consistent saving practices are essential. Developing a routine of consistent saving, no matter the amount, might eventually result in sizable retirement funds. Employer-sponsored retirement plans or systematic investment programmes can encourage people to automate their savings, which can reinforce this practice. Planning for retirement can be considerably improved by enabling laws and incentives.
Implementation of Long Short-Term Memory (LSTM) Model for Stress Detection Using EEG Signal
Authors: Jain Ayushi
Published: May 2023

Stress is a common issue in modern society and can lead to various health problems when left unaddressed. Accurate stress detection is, therefore, crucial in order to provide effective interventions and improve overall well-being. This study presents the implementation of a Long Short-Term Memory (LSTM) model to detect stress using electroencephalogram (EEG) signals. EEG signals were collected from a sample of participants while they were exposed to stress-inducing tasks and control tasks. The data was pre-processed using filtering and artifact removal techniques to ensure high quality and reliability. The pre-processed EEG signals were then used to extract relevant features, such as spectral power and coherence, which served as inputs to the LSTM model. A deep learning architecture was developed, incorporating the LSTM layers and other components to optimize the model's performance. The LSTM model was trained and validated using the available dataset. The results showed that the LSTM model significantly outperformed the other algorithms in terms of accuracy, sensitivity, and specificity. Furthermore, the model demonstrated robustness in detecting stress across various tasks and EEG channels. These findings suggest that LSTM-based models have the potential to be used as effective tools for stress detection in real-life scenarios, and can contribute to the development of more personalized stress management interventions. Future research should focus on refining the model and exploring its applicability in different populations and settings.
Role of ICT in Managerial Effectiveness in the Changing Era of Technology: A Quantitative Investigation
Authors: Rani Neha
Published: May 2023

Information and Communication Technology is one of the vital forces important for the growth and survival of an organization. It is crucial for every organization to perceive the idea of ICT. ICT in today’s technological era is more than just a study; it is a kind of innovation and new business style which is important for the managerial effectiveness of an organization. It is vital for the top managers to successfully implement the notion of Information and Communication Technology. It doesn’t matter whether the organization is small, medium or large ICT is essential for the smooth running of the business. In a capital intensive business ICT helps in the efficient functioning of the business. As the world is drastically transforming towards digitization ICT has emerged as a tool to bring effectiveness in the managerial activities of an organization. This paper will highlight the role of ICT in managerial effectiveness and some of the issues and challenges related with the same.
Factors That Influence the Rural Women Consumer on Making Purchase Decision
Authors: Negi Rakshit
Published: May 2023

The availability and popularity of computers have risen in recent years. E-rise commerce's to prominence along with the expansion of the internet itself is a direct result of these two factors. The e-commerce sector in India has also expanded rapidly. Consumers are becoming more knowledgeable about the options available to them via online marketplaces. In this research, we investigate the ways in which rural women's purchasing decisions are influenced by advertisements and by other factors. Virtually every product may benefit from advertising in the mass media. Advertising's primary purpose is to increase product worth in every way possible. It plays a crucial role in getting people to learn about products and ultimately buy them. The purchasing habits of rural women have a far-reaching and everlasting effect. The advertising medium is crucial for the success of any product promotion. The most common kind of advertising seen nowadays is on television. Consumer demand will be understood, and consumers will be encouraged to see the relevance of the product in light of modern trends.
Import and Export Database Management System
Authors: Bordoloi Dibyahash
Published: May 2023

Project components include login, customer registration, add products, view products, purchase order, sales order, payment, and report in the import export management system. Import manager, customers, and suppliers must provide their user name and password to log in to the login module. Supplier will enter information about the product, including its name, category, price, selling price, and quantity, in the add products module. The user may examine product information in the view products module. Using the database's product id, customers can create purchase orders in the purchase order module. The supplier will validate the product's purchase order in the sales order module. When a client makes a payment using the payment module, the money is deducted from their account once the payment has been confirmed based on the customer's name, bank name, account number, company name, and customer ID. Import manager will provide reports for customer information, product information, purchase order, sales order, and supplier information in the report module.
Bayesian Classification Model for Network Intrusion Detection Using Clustering Analysis
Authors: Sejwal Lakshita
Published: May 2023

In order to construct an IDS that is both computationally effective and efficient, the goal of this work is to pinpoint significant decreased input characteristics. For this, we use information gain, gain ratio, and correlation-based feature selection to examine the effectiveness of three common feature selection techniques. NSL KDD dataset to identify assaults on the four attack types: Probe (information gathering), DoS (denial of service), U2R (user to root), and R2L (remote to local). The signatures of known attacks are often kept in a regularly updated database. It must be educated for new attacks before it can detect them. The goal of anomaly detection is to spot behavior that deviates from the usual. This method revolves around the recognition of unusual traffic patterns. Two methods are frequently used for feature reduction. A Wrapper assesses the value of features using the intended learning method itself, whereas a filter does so using heuristics based on the overall properties of the data.
Use of data auditing for encrypted data stored in cloud environment
Authors: Harbola Aditya
Published: May 2023

The exponential growth of data generation and the increasing reliance on cloud-based storage solutions have raised significant concerns regarding data security and privacy. The widespread adoption of encryption techniques has been effective in protecting data confidentiality, but it also introduces new challenges in terms of data auditing. This paper explores the use of data auditing techniques for encrypted data stored in cloud environments to ensure data integrity, availability, and accountability while preserving privacy. We first provide an overview of the current state of cloud storage security and encryption techniques, followed by a discussion on the importance of data auditing for encrypted data. The paper then delves into existing data auditing approaches, specifically focusing on Public Key-based Auditing (PKA) and Private Key-based Auditing (PrKA) schemes. We examine their advantages, drawbacks, and suitability for different scenarios, highlighting their ability to maintain data privacy without compromising the auditing process. To address the limitations of current auditing techniques, we propose an innovative hybrid auditing framework that combines the strengths of PKA and PrKA schemes. Our approach enables efficient data auditing while ensuring data confidentiality, integrity, and privacy. It also supports dynamic data operations, including insertion, deletion, and modification, allowing for seamless adaptation to various cloud storage environments. We validate the effectiveness of our proposed hybrid auditing framework through a series of experiments, comparing it with existing PKA and PrKA solutions. The results demonstrate the superiority of our framework in terms of security, privacy preservation, and performance, making it a promising solution for auditing encrypted data in cloud environments.
Deep Learning-Based Automated Detection of Lung Cancer from CT Scans: A Comparative Study
Authors: Negi Charu
Published: May 2023

ABSTRACT

Lung cancer is one of the leading causes of death resulting from the disease on a global scale. In order to provide effective therapy and achieve better outcomes for patients, early detection and diagnosis are essential. Deep learning (DL) algorithms have demonstrated significant promise in recent years for their use in medical imaging analysis, particularly in the detection and diagnosis of lung cancer. This paper includes a comparative comparison of a number of different DL based models for the automated diagnosis of lung cancer from computed tomography (CT) data.
A Multi-Criteria Movie Recommendation System based on User Preferences and Movie Features
Authors: Singh Kamred Udham
Published: May 2023

In this research, we develop a multi-criteria movie recommendation system that provides personalised recommendations by taking into consideration both user preferences and movie aspects. To get over each method's specific drawbacks, the suggested system takes a hybrid approach that combines collaborative filtering with content-based techniques. The system uses collaborative filtering to capture user preferences based on historical ratings, while content-based methods analyze movie features such as genre, director, actors, and keywords to enhance the recommendation process. Additionally, we integrate various external data sources like movie reviews, social media sentiment, and box office performance to enrich the movie feature set. The system employs a weighted aggregation method to combine these criteria and generate a comprehensive recommendation score. The effectiveness of the proposed system is evaluated utilizing standard metrics including recall, precision, and F1-score on a publicly available dataset. The results demonstrate that our multi-criteria recommendation system effectively captures user preferences and provides more accurate and diverse recommendations compared to traditional single-criterion approaches.
Development of a Smart Traffic Light Control System Using IoT Technology
Authors: Gupta Rajeev
Published: May 2023

The development of a smart traffic light control system using IoT technology has become an essential topic in urban traffic management. This research paper focuses on the development of an IoT-based real-time traffic monitoring system for city governance. The system integrates traffic data from various sources, including cameras, sensors, and other data sources, to provide real-time traffic updates to city officials and drivers. The proposed system utilizes IoT technology to collect data from various traffic sources and processes it to determine the optimal time for traffic lights to switch between green, yellow, and red. The system uses a combination of machine learning algorithms and traffic models to predict traffic flow patterns and optimize traffic light timings. Additionally, the system can identify potential traffic accidents and send alerts to nearby emergency services. The proposed system is evaluated using simulation techniques and has shown a significant improvement in reducing traffic congestion and improving traffic flow in a simulated urban environment. Furthermore, the system provides real-time data analytics to city officials, allowing them to make informed decisions on traffic management and city planning. Overall, the development of a smart traffic light control system using IoT technology has significant potential in improving traffic management in urban areas. The proposed system can reduce traffic congestion, improve traffic flow, and enhance overall traffic safety. Therefore, it is essential to continue research and development in this area to implement the proposed system on a larger scale.
Automating Fraud Detection in Financial Services: An AI-based Approach
Authors: Negi Deepti
Published: May 2023

One of the most common reasons why financial fraud occurs is due to credit card fraud. Unfortunately, traditional methods of detecting this issue have not been able to effectively prevent it. This has prompted the need for more sophisticated and efficient fraud detection techniques. Artificial intelligence has emerged as a promising tool for this problem. The paper looks into the use of AI methods to detect credit card fraud in the financial services industry. We analyze the performance of different algorithms, such as the Random Forest, the Neural Network, and the Naive Bayes. We also perform various preprocessing steps in order to get the data ready for analysis. The paper presents an evaluation of the four AI techniques in terms of their precision, recall, accuracy, and F1 score. It indicates that the Neural Network is the best performer when it comes to detecting credit card fraud. The study emphasizes the importance of utilizing AI-based methods for detecting financial fraud. It also highlights the potential of the Neural Network algorithm. The findings of the study have important implications for the advancement of financial fraud detection systems, and it can serve as a guide for future research. The study's findings provide valuable insight into the use of AI in identifying credit card fraud in the financial services industry. It also shows the potential of this technology to help combat this issue.
An Improved Method Of Cluster Head Selection Using Machine Learning In Wsn
Authors: Kohli Priya
Published: May 2023

Wireless Sensor Networks (WSNs) are comprised of numerous small, low-cost, and energy-limited sensor nodes that gather information from their immediate surroundings and relay it to a sink node. The utilisation of cluster-based routing protocols has been widely employed in Wireless Sensor Networks (WSNs) with the aim of enhancing network efficacy and extending network longevity. In the context of Wireless Sensor Networks (WSNs) that are cluster-based, the network is partitioned into clusters, with each cluster being assigned a Cluster Head (CH) responsible for data aggregation and forwarding to the sink node. The process of selecting Cluster Heads (CHs) is of utmost importance for optimising the performance of a network in terms of energy efficiency, network longevity, and communication overhead.
Real Time Spatial Transmission Analysis Based Improved Black Hole Attack Detection In WSN For Improved QoS
Authors: Bhatt Neha
Published: May 2023

ABSTRACT

Despite the extensive utilisation of Wireless Sensor Networks (WSNs), they remain vulnerable to various types of security breaches, including black hole attacks. The detection and prevention of black hole attacks are crucial for safeguarding the security and reliability of Wireless Sensor Networks (WSNs). The present study introduces a technique for real-time detection of black hole attacks in Wireless Sensor Networks (WSNs) by means of spatial transmission analysis, with the aim of enhancing Quality of Service (QoS). In order to enhance the identification of black hole attacks, we propose a methodology that leverages real-time spatial transmission analysis to quantify the Euclidean distance separating the source and destination nodes of every packet. An algorithm with a high detection rate and a low false positive rate was developed by us. In order to evaluate the efficacy of the proposed mechanism and establish a comparative analysis with existing methodologies, we conducted extensive experimentation. The results indicate that the proposed mechanism exhibits superior performance compared to existing techniques in terms of both detection rate and false positive rate. Our proposed mechanism aims to safeguard the quality of service of Wireless Sensor Networks (WSNs) against black hole attacks in real-time.
Investigation of the Performance of Different Battery Technologies for Electronic Devices
Authors: Saini Amit
Published: May 2023

For electronics and renewable energy systems to function reliably, battery technology performance is crucial. Numerous tools, including simulation, testing, and materials science, are used to assess and enhance the functionality of battery technology. However, issues like energy density, cost, and safety make it difficult to create and optimize battery technology. Solid-state batteries, lithium-sulfur batteries, sodium-ion batteries, flow batteries, and metal-air batteries are just a few examples of the recent advancements in battery technology that have the potential to revolutionize the industry and enhance the performance of electronic devices. These innovations could someday become the norm for powering electronic devices, taking the place of conventional lithium-ion batteries. A more sustainable and efficient future will be enabled by the proliferation of electronic gadgets and renewable energy sources, both of which will benefit greatly from the continuous improvement and development of battery technologies.
Data Mining for Fraud Detection: An Overview of Techniques and Applications
Authors: Gupta Richa
Published: May 2023

The process of data mining involves extracting knowledge and insights from vast amounts of data. It can be done through the use of various computational and statistical techniques to identify anomalous, correlational, and pattern patterns. On the other hand, fraud detection is a process that involves identifying and preventing activities that are fraudulent. This process can be carried out through the use of various technologies and techniques, such as artificial intelligence and data mining. It aims to minimize the financial losses caused by these types of activities and ensure that the company follows proper regulatory and legal requirements. The ability to identify fraud prior to it happening is very important for organizations to prevent it from happening. This paper looks into the various aspects of data mining and how it can be utilized for fraud detection. This paper discusses the various aspects of fraud detection and how it can be utilized for organizations to prevent it from happening. We then talk about the various techniques that are used for this process, such as clustering, unsupervised learning, and neural networks. In addition, we talk about the various data preprocessing techniques that are used in the detection of fraud. These include data normalization, feature selection, and extraction. Data visualization is also important in interpreting and understanding the results of mining analyses. The paper then covers the various fraud detection applications of data mining. These include healthcare fraud, credit card fraud, financial statement fraud, and insurance fraud. We provide examples of how these techniques have been utilized to identify fraudulent activities. The paper then discusses the limitations of mining data for fraud detection, as well as the need for an integrated approach that combines various techniques, such as human intervention and audit trails. This paper provides an extensive overview of the various aspects of this field and highlights the significance of this technology in the fight against financial crime.
Mixture of Gaussian Blur Kernel Representation for Blind Image Restoration
Authors: Vimal Vrince
Published: May 2023

The use of blind image restoration, sharpness of edges may frequently be restored using previous information from a picture. De-blurring is the technique of taking out blurring flaws of the steady photographs, including motion or defocus aberration-related blur. the appearance of fast-moving the appearance of fast-moving entities flashing in still images flashing in a still photograph is known as motion blur. When an image is blurred using a Gaussian function, the result is a Gaussian blur. The employment of different sparse priors, either for the implicit photos or the motion blur kernels, contributes to the success of contemporary single-image approaches. De-blurring is the technique of taking out blurring flaws of the steady photographs, including motion or defocus aberration-related blur. The apparent flashing of quickly moving item in a static photograph is known as motion blur. When a picture is blurred utilizing a Gaussian function, the result is a Gaussian blur. The employment of different sparse priors, either for the latent photos or the motion blur kernels, contributes to the success of contemporary single-image approaches. On digital datasets, KSR also discovers effective kernel matrix approximation to hasten blurring and provide effective de-blur performances. The licence plate, which serves as the vehicle's distinctive identifier, is an important indicator of speeding or hit-and-run cars. However, the image of a fast-moving car taken by a security camera is usually blurred and not even humanly discernible.These observed plate pictures are frequently poor resolution and have significant edge information lost, which presents a significant challenge to the current blind deblurring techniques. The blur kernel may be thought of as a linear uniform convolution and parametrically modelled with angle and length for licence plate picture blurring brought on by rapid movement. This research suggests unique technique for locating the blur kernel based on sparse representation. We determine the angle of the kernel by looking at the sparse representation coefficients of the restored picture because the retrieved photo has the highest sparse representation when the kernel angle coincides with the real motion angle. Afterwards, using Radon transform in Fourier domain, we estimate the size of the motion kernel. Even when the licence plate is impossible for a person to read, our system handles big motion blur rather effectively. We assess our method using actual photographs and contrast it with a number of well-known cutting-edge blind image deblurring techniques. Experimental findings show that our suggested technique is superior in terms of efficiency and resilience.
Implementation of Machine Learning Techniques for Big Mart Sales Forecast Analysis
Authors: Manwal Manika
Published: May 2023

The sales data for each individual item is now tracked by supermarket run-centers, Big Marts, in order to forecast possible consumer demand and revise inventory control. By mining the data store of the data warehouse, wide-ranging trends as well as anomalies are frequently found. The generated data may be utilised by merchants such as Big Mart that employ a number of machine learning techniques in order to forecast forthcoming sales volume. A prediction algorithm was developed to estimate the sales of an organisation like Big-Mart utilising Xgboost, Linear regression, Polynomial regression, also Ridge regression methodologies. It was shown that the model beats other models.
A Novel Approach for Driving Fatigue Statue Recognition Using Side Face Contour Extraction Algorithm
Authors: Yadav Rishika
Published: May 2023

In this procedure, a novel colour space modelling is introduced that improves face skin extraction performance by using multiple thresholds as decision criteria. A three-step technique is also used to reduce noise and other negative impacts. The first critical stage in automated facial image analysis is frequently face identification, which tries to identify the existence along with subsequent location of human faces in an image. For the purpose of enhancing the effectiveness of extracting the colour of the face's skin, this procedure adopts a novel colour space modelling that employs several thresholds as decision criteria. Additionally, a three-step approach is employed to reduce noise and other negative impacts. The results of the experiments demonstrate that the suggested method could efficiently extract the side face contour line and should offer a theoretical foundation for the real-time tracking of fatigue applications.
Improved Mask R-CNN Segmentation for Contour Extraction of Individual Cattle from an Image
Authors: Chauhan Rahul
Published: May 2023

Forest species recognition has typically been approached as a texture classification problem and investigated by employing conventional texturing techniques like Local Convolutional Neural Network (CNN). Procedures have been the subject of current research for classification issues, with cutting-edge technique outcomes for object recognition and other activities, but are not yet commonly employed for texture problems. Several forest datasets, including one with macroscopic photos and another with microscopic images, are examined in this process to see how deep learning strategy, more specifically Convolutional Neural Networks (CNN), may be used to classify textures. Given the better resolution photos of these issues, we provide a strategy that can deal with them in order to get excellent accuracy and steer clear of the load of training and constructing an architecture with a lot of free parameters. On the first dataset, the suggested CNN-based technique superior to cutting-edge technology, which achieves an accuracy of 95.77%. It surpasses the best reported result of 93.2% on the dataset of microscopic pictures, achieving 97.32%.
Sparse Gradient Regularized Deep Retinex Network for Robust Low-Light Image Enhancement
Authors: Taluja Resham
Published: May 2023

An algorithm for improving images was created in this procedure. It is founded on the linear domain simultaneous estimate of light and reflectance. For single picture low-light enhancement, many image priors have been used. However, the most effective technique is to evenly enhance the illumination directly. By extending the dynamic pixel intensity of a picture, histogram equalisation (HE) can solve the issue of lighting and make dark images visible. Instead than changing the lighting, HE seeks to improve the contrast. As a result, the findings are resilient to noise and improve image quality. The test findings demonstrate the good performance of the suggested approach to provide lighting and reflectance with increased visual outcomes and a promising convergence rate. The suggested technique produces equivalent or superior outcomes in subjective as well as objective evaluations when compared to previous testing methodologies. Image enhancement’s main goal is to treat an input image such that the final output is more suitable for a certain application than the original image. It draws attention to or emphasises visual elements like borders, limits, or contrast to make a graphic presentation more useful for study and display. The improvement enhances the dynamic range of the selected characteristics, making it easier to recognise them even while it does not increase the data's intrinsic information richness.
Optimization Techniques for Resource Allocation in Cloud Computing Systems
Authors: Kumain Kiran
Published: May 2023

The distribution of available resources is an essential aspect of cloud computing systems. It entails allocating computer resources to various programmes and users in order to guarantee that the available resources are utilized in an efficient, effective, and equitable manner. The optimization of resource allocation in cloud computing systems presents a number of challenges, such as heterogeneity, multi-objective optimization, large-scale optimization, dynamic optimization, and user satisfaction. Other challenges include privacy and security, large-scale optimization, and dynamic optimization. Researchers have made tremendous progress in designing algorithms, models, and frameworks to address these concerns despite the difficulties they face in doing so. In this overview of the relevant literature, we focus on research that investigates methods for resource allocation and optimization in cloud computing systems. We provide a brief synopsis of the most important findings from these studies and provide them in table form, drawing attention to the study methodologies, algorithms, and optimization strategies that were applied. In addition, we address potential future research areas and outline the obstacles that researchers encounter when attempting to optimize the allocation of resources in cloud computing systems. The purpose of this literature review is to offer a complete overview of the current state of research in resource allocation and optimization in cloud computing systems, and it may also serve as a valuable reference for researchers and practitioners working in this subject.
Towards Energy-Efficient Data Dissemination in Vehicular Ad-Hoc Networks (VANETs): A Cross-Layer Design
Authors: Gupta Richa
Published: May 2023

Due to their capacity to permit communication between automobiles and infrastructure components, Vehicular Ad-Hoc Networks (VANETs) are growing in popularity. Intelligent transportation systems (ITS) have seen the emergence of Vehicular Ad-Hoc Networks (VANETs) as a promising technology, but effective data distribution is still a problem. Conventional methods of data sharing require a lot of energy and are inappropriate for VANETs with limited resources. The efficient transmission of data between vehicles, which consumes a lot of energy, is one of the major difficulties in VANETs. We suggest a cross-layer design strategy in this study to increase the energy efficiency of data distribution in VANETs. To maximize network performance, the design incorporates the capabilities of many layers of the protocol stack. We implement several energy-saving techniques, such as cooperative communication, power control, priority-based message scheduling, and sleep mode. The simulation findings demonstrate that the suggested design strategy can greatly lower energy usage and enhance VANETs' sustainability.
Internet of Things (IoT) Networks: Architecture, Applications, and Future Directions
Authors: Mishra Amit Kumar
Published: May 2023

The Internet of Things (IoT) networks, including their design, applications, future directions, difficulties, and constraints, are thoroughly discussed in this research study. The explanation of IoT networks' design, covering definitions and components, as well as centralized and decentralized networks, follows an overview of IoT networks' significance. The applications of IoT networks are examined in the next part, along with specific use examples from the fields of healthcare, manufacturing, transportation, and smart cities. The following section of the article examines new developments in IoT networks, including edge computing, 5G networks, AI, and blockchain technology. The difficulties and constraints of IoT networks, such as security and privacy issues, interoperability and compatibility problems, scalability, and network congestion, are also examined. The final section of the study summarizes the main ideas and research conclusions, highlighting the significance of IoT networks in our increasingly linked society.
Secure and Efficient Association Rule Mining over Encrypted Cloud Data
Authors: Agnihotri Aditya
Published: May 2023

Association rule mining and frequent itemset mining are two approaches to data analysis that have garnered a lot of interest and have been subjected to a lot of research. These approaches can be used for a range of applications. In this effort, our primary focus is on mining encrypted cloud databases while maintaining strict adherence to privacy standards. The owners of the data in this scenario are interested in discovering the association rules or frequent item sets derived from a collective data collection, but they want to do so while limiting the amount of their sensitive raw data that is made available to other owners of the data and to third parties as much as is practically possible. This study looks at the topic of outsourcing jobs related to association rule mining and doing it in a cloud environment. The study does so within the context of various frameworks that are supposed to preserve corporate privacy. According to the findings of the study, there is a novel approach that can be used to guarantee that every item that is updated and sent from the data owner to the server can be interchanged without the attackers needing any further prior knowledge. Our methods are efficient, scalable, and safeguard users' privacy when applied to a transaction database that is both large and authentic. This database is a representation of our complete algorithm. In this technique, the final protocol for mining association rules is proposed, in addition to mining that protects users' privacy by using a cloud infrastructure that is both safe and resistant to data breaches.
Comparison of Hydrographic Survey Data with Crowdsourced Bathymetry Data
Authors:   University of Split, Faculty of Maritime Studies Split, Croatia, Radić Toni , Pavić Ivica , Mišković Jakša
Published: May 2023

A hydrographic survey is a standardized procedure for collecting data for the production of nautical charts and publications. It is a lengthy and costly procedure, so the survey is carried out depending on the capabilities of hydrographic organizations. It is known that relatively large parts of the world's oceans are very poorly covered by hydrographic surveys. To increase the amount of data collected, the International Hydrographic Organization (IHO) has introduced the concept of crowdsourced bathymetry (CSB). Under the CSB concept, all vessels meeting certain minimum technical requirements (carrying a global navigation satellite system and a single beam echo sounder) can participate in voluntary bathymetric data collection. The paper analyzes the method of collecting bathymetric data from CSB. The depth data collected as part of the CSB are compared with official data displayed on electronic navigational charts (ENC) in the United States of America. Four sea areas were selected in which 104 depths were compared at the same positions, and categorization was also made according to the criterion of navigational importance, i.e., the category zones of confidence (CATZOC). By comparing the official depth data from the hydrographic survey with the depth data collected from public sources for the same positions, their mutual relationships were established, from which it can be concluded that the CSB data, despite its limitations, is a very valuable supplement to the existing official data.
Analysis of Current Threats to Ships from 2019 to 2022 in Selected Regions
Authors:   University of Split, Faculty of Maritime Studies Split, Croatia, Miloš Frane , Mišković Jakša , Pavić Ivica
Published: May 2023

International maritime security is of paramount importance to the maritime sector, particularly seafarers. The perception and importance of maritime security have evolved and are the subject of ongoing analysis. The concept of maritime security is closely linked to potential threats that may pose a danger to crews, ships, or the cargoes they carry. These threats are also subject to change over time. Different types of threats can have different implications for maritime security. Currently, the greatest threats to the world merchant fleet come from pirate attacks, armed robbery, terrorism, and military operations in certain maritime regions. These threats vary in nature, frequency, and motivation in different parts of the world, and so there are certain areas that are particularly vulnerable to such threats. This paper provides an analysis of maritime security from the perspective of piracy, armed robbery, and maritime terrorism by analysing individual attacks in different regions from 2019 to 2022. Each reported attack is broken down into various characteristics. Recurring characteristics in different and unrelated attacks are observed. This results in a pattern from which specific recommendations characteristic of individual regions are derived. The recommendations are subject to the dynamics of changes in international maritime security and should be actively modified and adapted accordingly.
Annelerde Postpartum Depresyon, Sosyal Destek ve Emzirme Öz Yeterliliği Arasındaki İlişkinin İncelenmesi
Authors: ÇINAR Nursan , TOPAL Sümeyra , YALNIZOĞLU ÇAKA Sinem , ALTINKAYNAK Sevin
Published: May 2023

Amaç: Bu çalışmanın amacı, annelerde postpartum depresyon, sosyal destek ve emzirme öz yeterliliği arasındaki ilişkiyi belirlemektir.

Gereç ve Yöntem: Tanımlayıcı ve kesitsel desende gerçekleştirilen çalışma, doğumundan sonraki 2 hafta- 4 ay içerisindeki 155 anne ile yürütülmüştür. Veriler, Soru Formu, Çok Boyutlu Algılanan Sosyal Destek Ölçeği (ÇBASDÖ), Edinburgh Doğum Sonrası Depresyon Ölçeği (EDSDÖ) ve Emzirme Öz Yeterlilik Ölçeğinin kısa formu (EÖYÖ) kullanılarak toplandı.

Bulgular: Katılımcıların yaş ortalaması 27.21±5.48 idi. Araştırmaya katılan annelerin çeşitli tanıtıcı özellikleri ile ölçekler arasında anlamlı ilişki olduğu saptandı. EDSDÖ kesme puanı 13 alındığında, %33.5’inin ölçekten aldıkları puan ortalaması 16.67±2.90 ile kesme puanının altında kaldıkları (depresyon var), %66.5’inin puan ortalaması ise 6.72±2.93 ile kesme puanının üstünde oldukları ve aradaki bu farkın anlamlı olduğu görüldü (Z=-10.171; p
Another neglected symptom among the overweight young: an analysis of the self-reported anterior knee pain scores of the secondary school children
Authors: BELİBAĞLI Mehmet Cenk , AYGÜN BİLECİK Nilüfer
Published: May 2023

Objective: Knee pain is the most common joint complaint among the young and is linked with excessive weight by the World Health Organization. The study aimed to define the prevalence of anterior knee pain in secondary school children and search its’ relations to weight and academic performance.

Method: The cross-sectional and descriptive study was conducted in Adana, between Sept 15 and Oct 15, 2020. Middle school students aged between 10 and 18 years were included. Anterior knee pain was assessed using the Anterior Knee Pain Scale short form (AKPS-SF), and the numerical rating scale (NRS) was used for pain intensity. Data were collected by questionnaires sent by e-mail.

Results: There were 709 replies eligible for analysis. The NRS mean score was 0.78±1.47 for 249 (35.12 %) participants who reported knee pain. The number of students with an AKPS-SF score of ≤40 in the study group was 46 (6.40%). AKPS-SF scores showed weak negative linear associations with weight and the school year (r=-0.346/p=0.019, and r=-0.292/p=0.049, respectively), and no correlations were found with academic performance.

Conclusion: The study has shown that more than a third of the students reported various levels of knee pain. The inverse correlation of AKPS-SF with weight and the weak link with the school year might attract the attention of healthcare givers to pay more attention to knee examination and help avoid future impairments in the knee joints of school age children.
Evaluation of Brilaroxazine (RP5063) in a Bleomycin-Induced Rodent Model of Idiopathic Pulmonary Fibrosis
Authors: Bhat Laxminarayan , Nault Marie-Claude , Bhat Seena , Biernat Marzena , Labbe Sebastien
Published: May 2023

Idiopathic pulmonary fibrosis pathology involves serotonin (5-HT), with an increased expression of 5-HT2A/2B/7 receptors in the lungs. This study tests the hypothesis that brilaroxazine (RP5063), an agent with a potent binding affinity for serotonin 5-HT1A/2A/2B/7 and dopamine D2/3/4 receptors, and moderate affinity for the 5-HT transporter, dosed at 15 mg twice daily (b.i.d.) shows efficacy as compared with placebo in a bleomycin (BLM)-induced model using Sprague Dawley rats. On Day 0, four groups received BLM-induction, and one received placebo. On Day 1, one group started on brilaroxazine (RPT). On Day 10, two groups started on brilaroxazine (RPI) and one continued on the vehicle (BLM). All interventions continued until Day 20. Compared with BLM, RPT and RPI sustained survival at 90.5% and 89.5%, respectively (P<0.05) and maintained weight (P<0.01). RPT normalized pulse pressure and cardiac output. It also lowered respiratory resistance, hydroxyproline, lung weight, bronchoalveolar lavage fluid cell counts, and total protein (P<0.05). RPI decreased hydroxyproline and reduced cell counts (P<0.01). Brilaroxazine lowered Ashcroft scores and Masson’s Trichome staining (P<0.001). Both brilaroxazine groups reduced proinflammatory and fibrotic cytokines (P<0.05). Brilaroxazine attenuated BLM-induced pulmonary fibrosis, inflammation, and extracellular deposition and improved cardiac and pulmonary functions in rodents.
The Drivers of Business Performance: Does Sales Force Strategy and Network Capability Matter?
Authors: Johan Ahmad , Zulganef Zulganef , Pratminingsih Sri Astuti
Published: May 2023

Many factors that SMEs must consider in driving their business to achieve success. This study attempts to identify and analyze the factors that can encourage the business performance of SMEs by considering sales force strategy and network capability. This study also examines the mediating role of network capability in the influence of sales force strategy on business performance. This study used a quantitative approach, with 140 creative SMEs in Bandung, West Java, as the respondents. The data was collected using a survey and further analyzed using regression and path analysis with SPSS 25 software. The findings indicated that sales force strategy and network capability positively influenced business performance and that sales force strategy influences network capability. Furthermore, this study confirmed that network capability mediated the relationship between sales force strategy and business performance. This study provided implications for both practice and theory.