Volume 12 | Issue 4

 Abstract

The business of picture subscription requires both language and visual comprehension. Picture models need to analyse visual material in order to construct words in human languages. Because the focus strategy may provide deeper sequential model training with more precise picture information, it has been widely employed in image subtitles. The automated description of visual information using natural languages is a critical and challenging task. There are several options. For example, it might make the image's content easier to interpret. In situations like photo sharing on social media sites, it may also offer more accuracy and concise picture information. Deep neural networks are used in this study to accomplish this. From real-time video (image frames), a convolutional neural network (CNN) is used to extract vectors, and an LSTM network is then used to generate replacements from these vectors. The dataset that is most frequently used to evaluate the model is Flickr 8K. It has more than 8,000 photos in it. Picture captions are created using the process by compiling data from image and caption pairings.

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Recurrent neural networks (RNN); Deep neural networks (DNNs); Convolutional Neural Networks (CNN); Long Short-Term Memory networks (LSTM).

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Abstract-- Muslim Women Welfare and Development Schemes focus on enhancing the well-being and progress of Muslim women within societal frameworks. These initiatives aim to address issues such as gender equality, education, and economic empowerment. Schemes often emphasize the implementation of Islamic laws to protect women from various forms of exploitation, ensuring their dignity and independence. Programs typically include provisions for financial support, education, and skill development, recognizing the importance of empowering women economically and socially. Efforts may also be directed towards legal frameworks, ensuring that Muslim women have equal access to justice and protection. By amalgamating Islamic principles with contemporary needs, these welfare and development schemes strive to create an inclusive environment that fosters the holistic growth and empowerment of Muslim women, reinforcing their rights and status within society.

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 Keywords

Muslim Women, Welfare Schemes, Development Initiatives, Gender Equality, Economic Empowerment, Islamic Laws, Dignity and Independence, Legal Frameworks, Education Programs, Social Empowerment

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This study investigates the correlation between household income and the occurrence of child labour and trafficking in Suleja, Niger State. A model was created using the analytical ideas proposed by Basu and Van (1988) and Fan (2011). The study used a multistage sampling strategy to get data from three specifically chosen rural districts in the Suleja Local Government Area of Niger State. The data was collected by distributing questionnaires to 367 families in the three chosen rural areas of the Suleja Local Government Area. The study findings indicate a significant occurrence of child labour and trafficking in Suleja, Niger State, Nigeria. It is recommended that both individuals and the government pay attention to and actively participate in efforts to eliminate child labour and trafficking. This will help to limit the involvement of children in these harmful practices.

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Child Labour, Trafficking, Household Income, Rural Districts, Elimination Efforts

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Being a country full with diversity in everything, it has a very key role to play in the field of language. Starting from the most ancient language to the current's natinal language "Hindi" and how this evolution has taken place and what else has been the impact of this revolutionising journey.

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 Keywords

Language, diversity in language, indian language, Tamil, Hindi

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The deployment of 4G/ LTE( Long Term Evolution) mobile network has answered the major challenge of high capacities, to make real broadband mobile Internet. This was possible substantially through veritably strong physical subcaste and flexible network archi- tecture. still, the bandwidth empty services have been developed in unprece- depressed way, similar as virtual reality( VR), stoked reality( AR), etc. likewise, mobile networks are facing other new services with extremely demand of advanced trustability and nearly zero- quiescence performance, like vehicle dispatches or Internet- of- Vehicles( IoV). Using new radio interface grounded on massive MIMO, 5G has crushed some of these challenges. In addition, the relinquishment of software defend networks( SDN) and network function virtualization( NFV) has added a advanced degree of inflexibility allowing the drivers to support veritably demanding services from different perpendicular requests. still, network drivers are forced to consider a advanced position of intelligence in their networks, in order to deeply and directly learn the operating terrain and druggies actions and requirements. It's also important to read their elaboration to make apro-actively and efficiently( tone-) updatable network. In this chapter, we describe the part of artificial intelligence and machine literacy in 5G and beyond, to make cost-effective and adaptable perform- ing coming generation mobile network. Some practical use cases of AI/ ML in network life cycle are bandied.

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Coming Generation mobile Networks, 5G, Artificial Intelligence, Machine Learning, Deep Learning, Physical Layer, Big Data, Network Control

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This paper aims to give a comprehensive overview of crop recommendation systems, fastening on the operation of machine learning (ML) algorithms. Crop recommendation systems have achieved significant attention in recent times because of their capability to help growers in making proper opinions regarding crop selection and optimization. This paper reviews the vital generalities, ways, challenges, and advancements related to ML algorithms employed in crop recommendation systems. likewise, it discusses various datasets, evaluation criteria, and case studies available in the literature to illustrate the capabilities and limitations of being systems. The check concludes by relating implicit avenues for future disquisition and pressing the significance of ML in revolutionizing husbandry.

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Bixa orellana the sindur plant belonging to Bixaceae family and has a phytochemical and pharmacological uses. The extraction of essential oil and the chemical composition of oil from the bixa orellana seed are described along with antimicrobial, antibacterial and other uses. Bixin can be use as a decoction or an infusion to treat bacterial infections, according to the literature. Soxhlet extraction was used to carry out Extraction on dried seed. The lozenges were prepared by molding method with sucrose, peppermint oil, gelatin, honey, turmeric etc. The prepared medicated lozenges were characterized for Hardness, weight variations, moisture content method. These activities was done against various pathogens bacteria such as E.coli,streptococcus aureus and other l, these activities was investigating using disc diffusion method.

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lozenges, throat infection, bixa orellana seeds,antimicrobial activity.

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Nowadays, individuals are apprehensive about visiting hospitals for medical consultations because of the COVID-19 epidemic. As a result, internet consultations have become more popular. However, physicians are compelled to treat patients without enough data since they are unable to do the diagnostic checks typically performed on them in hospitals. Our gadget captures the necessary data and sends it to the doctor, solving these difficulties

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COVID-19, Hospitals, Patients, IoT

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While educational games have been increasingly popular in education, insufficient studies have comprehensively reviewed their effectiveness. To complement this missing link, this study explored game-based learning outcomes including academic achievements, problem-solving, and critical thinking abilities, knowledge, learning efficiency, skills, student attitudes, and behaviours. Both negative and positive effects of educational games on motivation were also explored based on comprehensive literature analysis. The role of engagement in game-based learning was studied, coupled with the ways to enhance student engagement. We also explored the importance of gamified components in student satisfaction and provided constructive suggestions for designers and practitioners. Future research should highlight learning analytics and data mining techniques of educational game-based learning and try to find out solutions to address various problems to improve the effectiveness of educational games.

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Educational games, Game-based learning, Effectiveness, Critical thinking abilities, Knowledge acquisition, Student attitudes

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The constitution of India emphasizes on right to education to all, irrespective of caste, creed, religion, gender and age. Every citizen has moral right for education. Education, knowledge gaining help's for elevating social, economic and cultural fabric of every country. Education opens wide range of opportunities with which society can lead to a dignified life. Education for Girls or woman is of utmost importance. Earlier education for girls was a task, but with profound efforts of social reformers, various movement, special amendment's for women and Government intervention many doors are now open for women in various fields and professions. However inspite of beleaguered determinations still the girl's education has not reached the proportion in comparison with boys, especially in rural parts of India the ratio is inverse. As a matter of concern and surprise even in cities the woman drop-out numbers are increasing. Hence this paper's intention is to look out, research and analyse various aspects leading to diminishing enrollment of girls in Women College in Metropolitan city like Mumbai. The present paper attempts to understand the socio-economic huddles faced by today's girls to continue education

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Diminishing, Constitution, Education, knowledge, beleaguered

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dqathHkwr "kCn& vkS|ksfxd bdkbZ] Jfed] lkekftd lqj{kk] v)zzzZ&csjkstxkjh] dk;ZlkeF;ZaA

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Deep Learning and artificial intelligence are two of the most ground- breaking technologies of our day. When a machine can suppose, learn, perceive, and make opinions tasks that generally bear mortal intelligence this is appertained to as artificial intelligence. A fashion for tutoring neural networks to learn from data and gain delicacy over time is known as deep learning, a subset of artificial intelligence. In fields including image identification, natural language processing, and independent buses, deep learning has achieved outstanding results. In a number of tasks, similar as speech recognition, language restatement, and image bracket, deep learning models can presently outperform humans in terms of delicacy.

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Artificial Intelligence, Deep Learning, clinical decision support, health diligence

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This project report delves into the pivotal role of Small Finance Banks (SFBs) in advancing microfinance initiatives, with a focus on the context of Godhra City. Microfinance has emerged as a powerful tool for poverty alleviation and financial inclusion, particularly in regions where traditional banking services are scarce or inaccessible. Through an extensive literature review, this report examines the theoretical underpinnings and practical implications of microfinance interventions facilitated by SFBs. The study employs a mixed-methods approach, combining quantitative analysis of financial data with qualitative insights gathered through interviews and surveys. By scrutinizing the operations, outreach strategies, and impact assessment mechanisms of SFBs operating in Godhra City, the report aims to offer a comprehensive understanding of their contribution to microfinance penetration and socio-economic development.

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Microfinance SFB Financial Development

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The landscape of video game development has been continuously evolving, with the advent of powerful hardware and sophisticated software tools enabling increasingly immersive and visually stunning gaming experiences. First, it delves into the importance of realism and immersion in modern 3D games, highlighting advancements in graphics rendering, physics simulations, and artificial intelligence. It discusses the integration of ray tracing technology and machine learning algorithms to create more realistic lighting, physics, and character behavior. Second, the abstract explores the impact of virtual reality (VR) and augmented reality (AR) on the gaming industry. It discusses the growth of esports and the need for robust network infrastructures to support large-scale, real-time games. Key Features of this game are Entertainment: Creating an engaging and immersive gaming experience for players to enjoy and have fun, Challenge: Providing players with challenging puzzles, obstacles, or opponents to test their skills and keep them engaged, Exploration: Allowing players to explore virtual worlds, discover new places, and interact.

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3D Game Development, Games, mobile game developer, game development courses, mobile game development, game design, game maker.

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 Abstract

Frequent pattern mining is widely used in many real-world applications. The mining of common patterns from accurate data has drawn the attention of numerous scholars since its introduction. The mining of unclear data has received more attention in the last few years. Every transaction involving this uncertain data often has items associated with existential probabilities that indicate the likelihood that these articles will be present at the time of the transaction. Because of the existential probability, there is a much larger area available for the search and solution of uncertain data as compared to precise data extraction. The popular MapReduce and Apriori algorithms serve as the foundation for the models that are offered. The proposed algorithms are divided into three main types. The goal of two Apriori MapReduce and AprioriMR techniques is to accurately identify patterns in large datasets. These algorithms retrieve any available data item, regardless of how frequently it occurs. Put tape on the antimonotonic feature of the search space. AprioriMR and top AprioriMR, two more space trimming techniques, are presented with the aim of finding any shared data patterns. maximum number of frequent patterns. We also live in the Big Data era. Moreover, we propose certain enhancements to further boost its efficiency. The MapReduce for Big Data Analytics method is effective at extracting common patterns from undefined data, as demonstrated by experimental results.

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 Keywords

Parallel Frequent Pattern Growth (PFPG), MapReduce (MR); Hadoop Archives (HAR); Sequential Pattern Mining (SPM).

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 Abstract

The Indian scenario of the banking industry has a robust network of public and private sector banks that continue to work seamlessly reinforcing the Indian economy. The operational scope and targets of the public sector and private sector differ to a great extent even though they work together under several governmental schemes. While the greatest objective of public sector banks is social welfare, private sector banks are more target-oriented focusing on profitability. The study attempts to derive a comparison between the financial performance of the public and private sectors concerning internal and external factors that influence the overall performance of the banking industry. This study might prove helpful in reinforcing the central policies and identifying factors that are adversely affecting performance so that banks can equip themselves in a better way to counteract any financial shocks that may occur in the future. The goal of this research was to examine the financial performance of a few public and private sector banks from 2017 to 2021. The study concluded that both public sector banks and private sector banks need to focus on decreasing their non-performing assets as they seriously dent the profitability of banks by affecting its revenue. Banks need to work from the perspective of improving revenue and also cutting expenses if they wish to reinforce their banking performance.

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Financial Performance, Financial Analysis, Public Banks, Private Banks

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The dynamic and complex field where the protection of invention and the encouragement of market competition meet is known as the intersection of intellectual property (IP) and competition law. An overview of the theoretical underpinnings, research approaches, and current concerns pertaining to the interaction between intellectual property and competition law is given in this abstract. Drawing from a wide array of scholarly sources, such as legal studies, economic evaluations, and empirical investigations, the abstract emphasises the intricacies involved in striking a balance between the demands of competition enforcement and innovation incentives. Theoretical underpinnings of intellectual property and competition law, the effect of IP rights on market dynamics, limitations placed on IP holders by competition law, and new issues in the digital economy are some of the major topics covered.

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Intellectual Property , Competition Law, market, law

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This study aims to explore the influence on social media marketing for the revenue growth of online small enterprises, given the importance of social media and the dearth of knowledge in this field. We offer a research model that examines the effects of social media advertising methods (i.e., message material and format) on the consumer participation, brand awareness, and online small business sales performance. This model is based on the theories of media richness, uses, and gratifications. It is anticipated that brand recognition and customer engagement will moderate the relationships between messaging tactics and the success of online small company sales. To validate our model, we will collect data from various social media platform in India. This study is expected to contribute to research and practice on social media marketing to attract customers to brands of small companies.

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Social media marketing, attract customers, brands, small companies

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With more than 300 million members, Twitter is a well-known online social networking platform where a tonne of information is shared daily. The most significant element of Twitter is its ability for users to tweet about events, situations, feelings, opinions, or even something new in real time. Since there is no framework in place to guarantee veracity or reliability, anyone can say whatever they want. This kind of Twitter war could be an easy way to attack your opponents and allow them to attack you in return. This survey covered a number of machine learning and hate speech identification apps that help identify sarcasm and shame tweets. With the rise of public shaming events and online social network expansion, it is necessary to evaluate shaming tweets, divide them into different categories, and restrict them in order to stop them.

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Naive Bayes(NB);Application programming interface(API);Online social networks (OSNs); K-nearest neighbours (KNN);Random forest(RF).

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The human face is a crucial component in determining the behavior and mood of a single organ. Sorting through a music collection by hand and building a playlist according to distinct emotional qualities is a laborious process. Various methods have been proposed and put into practice for playlist construction that is automated. Nevertheless, current methods are less precise, compute more slowly, and may even call for additional hardware like sensors or EEG. The proposed method, which is based on facial expression extraction, minimizes the time and work required to manually produce the process by creating a playlist. Along with system-to-person questions like "how did you go?" the system will also suggest songs based on their lyrics. As a consequence, the recommended method is able to increase the overall accuracy of the systems while minimizing the total cost and computation time required to get the results. Facial expressions are captured using an integrated camera. Determines how well computers are in identifying emotions in real-time photos. Incorporate a collaborative filter, content-based recommendation, sentiment-based music, share workload between high-end servers and mobile low-end devices, and promote user-specific data in order to alleviate overloaded problems.

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Gaussian Mixture Model (GMM) ; k-Nearest Neighbors (KNN); Convolution Neural Networks (CNN)

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