bluuebunny/crossref_metadata_2025 huggingface dataset:
The Effect of Consumer-Centric Brand Equity Parameters on the Brand Loyalty: A Cross-Sectional Study
Authors: Kargeti Himanshu
Published: May 2023

Understanding brand equity is essential for marketers and brand managers because customer perception of a brand has a big impact on consumer behaviour. Consumer-centric brand equity metrics, such as brand loyalty, associations, perceived quality, and awareness, are used to evaluate a brand's value in the eyes of consumers. Brand loyalty and trust act as moderators in the interaction between consumer-centric brand equity features and brand loyalty. To learn more about this subject, information from a sample of customers from different firms can be gathered via a survey questionnaire. Then, data analysis and hypothesis testing can be done using regression and correlation modelling. The macro and micro approaches are two ways to determine a company's financial market worth based on brand equity. Integrated marketing communication can also have a substantial impact on business outcomes, giving businesses a competitive edge, enhancing brand equity, and assuring efficient and reliable communication. The final four steps in creating a strong brand are summarised by the Customer Based Brand Equity Model (CBBE). This area includes enhancing brand relevance, performance, graphics, and perceptions. By effectively completing these processes, a firm may create a powerful brand that resonates with its target audience and enhances business success.
Spam Detection and Spammer Community Detection in Social Media Platform Using Machine Learning Techniques
Authors: Gopal Lisa
Published: May 2023

In recent years, social media platforms have experienced a surge in popularity, leading to an increase in spam and spammer communities. This paper presents a comprehensive study on spam detection and spammer community detection in social media platforms using machine learning techniques. The proposed approach focuses on three main features: Review-Behavioral (RB) Based features, Review-Linguistic (RL) Based Features, and User-Behavioral (UB) Based Features. By combining these features, we aim to create a robust and accurate spam detection model that effectively identifies spam content and spammer communities in social media networks. The Review-Behavioral (RB) Based features focus on the patterns and tendencies observed in user-generated content, such as the frequency of posting, the time between posts, and the distribution of ratings. Review-Linguistic (RL) Based Features, on the other hand, analyze the linguistic characteristics of the content, including the use of specific keywords, the complexity of the text, and sentiment analysis. Lastly, User-Behavioral (UB) Based Features examine the behavior of users in the social media platform, including their social connections, interactions, and activity patterns. By incorporating these features into a machine learning model, we aim to develop an effective spam detection and spammer community detection system that can be used to protect social media platforms from malicious activities. The results of this study will provide valuable insights into the effectiveness of these features in identifying spam and spammer communities, as well as contribute to the ongoing efforts to improve the security and integrity of social media networks.
The Intersection of Science and Literature in the works of Robert Louis Stevenson
Authors: Singh Amar Pratap
Published: May 2023

The works of Robert Louis Stevenson depict his intense fascination in exploring scientific matters in his literary arena marking his keen interest in areas of psychology, anthropology and aspects of evolutionary theory. In works, such as “Treasure Island” and “Strange Case of Dr. Jekyll and Mr. Hyde”, Stevenson uses theories on psychology to interpret and present his characters, there by portraying intersection of science and literature. The life of Stevenson evidently shows the author’s attempts to learn significance of science in explaining and comprehending the mysteries of universe. It is this same interest that influences Stevenson in using his understanding of science to frame the psyche of his own characters. The literary style of imparting scientific knowledge to illustrate one’s characters and draw the narratives eventually opens a new scope of literary technique that develops intersection of science and fiction. The study and analysis of Robert Louis Stevenson’s works help contribute to the debates and discussions on interweaving of fiction and science by opening up various narrative styles and techniques of writing.
Pharmacognosy Basics for Understanding Herbal Drug Interactions Commonly Used for Sustained Home Remedies
Authors: Dhyani Archana
Published: May 2023

With the goal of providing the most possible benefit to the patient, integrative medicine involves combining conventional and evidence-based alternative medications and treatments. Herb-drug interactions (HDIs) provide a significant barrier to the same. Since these HDIs may have either positive or negative effects—even be fatal—a comprehensive knowledge of HDI outcomes is crucial for the effective integration of conventional and alternative medical practises. In this article, we provide a concise overview of HDIs, highlighting the interplays between drug metabolising enzymes and transporters while discussing the many kinds of HDIs and the tools/methods for studying and predicting HDIs. Future perspectives are also discussed in this paper, with an emphasis on the endogenous participants in the interplays and methods for predicting drug-disease-herb interactions to achieve the desired results.
Utilizing Machine Learning Techniques for Plant-Leaf Diseases Classification
Authors: Kumar Indrajeet
Published: May 2023

Infectious diseases of plants provide a substantial danger to the world's food supply and the agricultural industry. The early detection and classification of diseases that affect plant leaves is essential for minimizing crop loss and creating disease management measures that are both efficient and effective. For the purpose of this research, we present a unique approach that utilizes advanced machine learning techniques in order to classify plant diseases in a manner that is both accurate and efficient. In the first part of this multi-part series, we will begin by providing a detailed analysis of several different machine learning techniques, such as deep learning, convolutional neural networks (CNNs), and K-nearest neighbor (KNN), support vector machines (SVMs). Next, we provide an overview of a methodology for preprocessing the leaf images, which includes the addition of enhancements to the images, segmentation of the images, and the extraction of features. Next, we apply various machine learning algorithms to a large, diverse dataset of plant-leaf images that have varying degrees of disease severity and compare the performance of these algorithms as they are implemented on the dataset. Our findings provide evidence that the method being proposed is successful in correctly recognizing and categorizing plant diseases that affect leaf tissue. In terms of accuracy, precision, and recall, the models that are based on deep learning, in particular CNNs, perform significantly better than classical machine learning techniques. In addition, we investigate various methods to enhance the interpretability of the model and provide insights into the primary factors that contribute to the accuracy of categorization.
An IOT based Accident Severity Prediction Mechanism using Machine Learning
Authors: Verma Aditya
Published: May 2023

The significant number of fatalities and serious injuries caused by traffic accidents around the world is a worrying problem. Developing nations typically bear a heavier weight of casualties. As a result, developing a model to forecast the likelihood of accidents is extremely difficult. However, the application of machine learning algorithms is one of the significant techniques to forecast the seriousness of such events. As a result, the main goal of the suggested thesis is to automate the process of accident detection by evaluating the severity levels and filtering a set of influential factors that could cause a road accident and generating them using IoT. SMOTE's theoretical notions are put into practice in order to address data imbalance and to ensure that the dataset is balanced. In a later step, the dataset is put to use in the process of building a framework that is constructed from five machine learning algorithms and one stacking algorithm. In the final step of the process, a study is conducted using variables such as the state of the weather and the varying degrees of severity that can have a role in the occurrence of traffic accidents. According to the findings of the experimental analysis that was carried out as part of the research project, the random forest model generated a higher level of accuracy than any of the other models that were put into use, achieving 74%.
Business Strategies of Corporate Technology Driven World: A Quantitative Investigation of the Tech Experts Opinions
Authors: Rawat Monika
Published: May 2023

Digitization has become one of the important attribute of business world. As the technology is changing day by day, the need for implementation of different strategies has also emerged. As the digitization has pick up the speed the traditional and old strategies has lost its significance in today’s world. Every single and small business needs a good digital strategy to run efficiently. Since the digital place has numerous opportunities hidden, it is very important for a business to identify these places and implement the accurate strategy for the success of the business. To harness the competitive advantage, an effective, efficient and accurate business strategy is needed. In this paper we will be discussing the important attributes of business strategy needed to survive in a healthy competitive world. This paper will also highlight the importance of business strategy in shaping a profitable business.
Internet of Things as a Transformation Agent for New Age Businesses: An Empirical Study Current Status and Future Challenges
Authors: Negi Rakshit
Published: May 2023

Internet of Things is transforming the world in every aspect.  Businesses are one of the biggest beneficiaries of IoT as they are adapting with the transformation swiftly. Not only the businesses but everything which is connect to these businesses are getting advantages be it a Consumer, Government or other organizations. Digitization and IoT has changed the working approach of the companies. They are shifting more towards a digital path rather staying on a traditional way. This shift has benefitted the companies in many ways like the reach to the potential customer has increased and only interested customer can be targeted. Though the IoT proved to be beneficial but it does posses many threats to the business and to the consumer in the form of security breach and data exploitation etc. This paper with will discuss the current status of IoT in business and also highlight the future challenges might face by them.
Role of MSME Sector in the Development of National Economy: An analytical Study
Authors: Srivastava Barun
Published: May 2023

The importance of the “Micro, Small, and Medium Enterprises” (MSME) sector in the growth of the national economy is the main topic of this essay. MSMEs are regarded as the backbone of the economy since they make a considerable contribution to GDP, job creation, and general economic growth. The goal of the article is to undertake an analytical study to assess the contributions of MSMEs to the national economy, identify the problems they face, and suggest solutions to help MSMEs flourish. Interviews with MSME owners and managers, official statistics, and pertinent literature are only a few of the primary and secondary data sources that will be used in this study. Policymakers, MSME stakeholders, and development professionals will all benefit from the study's findings, which will give them new perspectives on how to encourage MSME development and boost the overall efficiency of the national economy.
Analytical Study on Relationship Between Shg and Poverty Alleviation
Authors: Kaparwan Cs Ruchitra
Published: May 2023

Microfinance is showing great promise as a means of combating poverty in India. Although making significant contributions to the home and national economies, women's work is often undervalued or ignored. The rapid growth of SHGs and the Bank linkage model have sparked a nationwide campaign to increase women's economic independence. Many empirical investigations have demonstrated that the members of SHGs have been able to better their socioeconomic situation thanks to microfinance provided via SHGs and the Bank linkage model. Moreover, it empowers women to make better financial decisions for their families and boosts their self-esteem. The questionnaire serves as the primary data collector, while non-parametric tests (such as the chi-square and analysis of variance) are used for analysis and presentation. Self-help Training of Swarozgaris, infrastructure development, marketing and technology support, member communication, member self-confidence, reduction in family violence, increase in outsider contact, monthly savings by SHG members, monthly savings by SHG members as a whole, political participation, social harmony, social justice, and community involvement are just some of the areas where groups have made a significant impact.
Understand the Relationship Between the Risk-Taking Abilities Among Citizens
Authors: Rawat Monika
Published: May 2023

Our results suggest that risk-taking inclinations in the financial sphere decline dramatically with older age. In contrast, social risk-taking increases somewhat between the ages of 18 and 24, then declines considerably in later life, whereas the decline in risk-taking for recreational purposes is steeper between the ages of 18 and 24 than in later life. Taking risks with one's health and one's morals tends to decrease significantly with age. Later in life, men, but not women, lowered their financial risk-taking significantly, whereas women reduced their social risk-taking more substantially than men did. "Risk variables" are thought to predispose a person or group to some bad consequence and consequently to constitute a danger to well-being. These elements may be human qualities or environmental ones, inherent in the family, community, school or peer group environment.
Evaluate the Consumers’ Perception and Satisfaction Level Towards Nature of Eco-Friendly Packages
Authors: Rani Neha
Published: May 2023

A increasing consciousness about the need to save the natural world has led to a shift in consumer preferences toward eco-friendly goods. Changes in consumer concern for the environment may be seen in how people are now shopping. An increasing number of people are showing concern for the environment by showing a preference for eco-friendly items in their shopping habits. Finding out how consumers feel about and value eco-friendly packaging is the focus of this research. The current study uses an empirical survey to gather data that is then applied to the original research question. A total of sixty shoppers were randomly chosen for the study. The focus of this study is to look at how packaging may affect how customers feel about a product. In this section, we explain the variances in the nature of these signals to illustrate the intricacy of perceiving packaged ecological information.
Computer-Aided Diagnosis in Lung Cancer Detection: A Review
Authors: Dubey Bhanu Prakash
Published: May 2023

The greatest cause of death for both men and women globally is lung cancer. This is mostly because it manifests no symptoms at first and is only identified at a late stage, when the patient's condition has already gotten worse. Typically, a skilled lung expert will thoroughly examine CT scans to make the diagnosis of lung cancer. Finding and interpreting abnormalities on a CT scan can be quite challenging, even for highly skilled medical professionals. In order to help doctors identify malignant cells, a lot of research is being done in the area of computer-aided diagnosis (CAD) of lung cancer. This study's main goal is to examine some CAD methods that have been used to diagnose lung cancer.
CP-CDN: Content Delivery Powered By Context and User Intelligence
Authors: Sharma Abhishek
Published: May 2023

In order to improve end users' Web access experiences, today's Internet content providers generally employ Content Delivery Networks (CDNs) to provide material to end users. To comprehend reasons, methods, and amounts of CP-level intelligences may aid content distribution, we employ a measurement-based approach. The largest content provider in China, Tencent, constructed a CDN, which we first offer as a measurement study. Contrarily, CPCDN may leverage context intelligence—which captures how contents are constantly manipulated then synthesized in various circumstances —in order to optimise the user experience and service quality throughout content delivery since it is aware of the precise environment in which material is consumed. In this study, we use a measurement-based approach to reasons, methods, and amounts of CP-level information may aid content delivery in order to support the quick transition to CPCDN. We proactively replicate content based on interest groups based on crowd patterns so that users can access their desired content from nearby CPCDN servers. CPCDN is able to raise the level of both content delivery efficiency and user experience by using the potent user intelligence that captures how contents are constantly exchanged and disseminated across social networked users.
A Decentralized Cloud Firewall Framework with Resources Provisioning Cost Optimization
Authors: Srivastava Prateek
Published: May 2023

A new flexible method for supplying more computer power in a shared media is called cloud computing. It offers a distributed paradigm based on self-evaluation methods to increase the system's processing power with less managerial worries. Clients, applications, platforms, servers, and infrastructures make up its components. This computing architecture provides end users with calculation skills as a computed service from the aforementioned components.  Security management will lose importance when more devices and their integration become available. One needs to manage data, system, and confidentiality problems carefully since cloud users are growing daily.  Therefore, in order to address protected access and integrity concerns in a cloud environment, new security firewall services must be deployed in addition to the current system. Numerous network-related issues, including load balancing, scheduling, traffic divergence, filtering, regulating the rate of arrival, instance management, and attack detection, make it difficult to implement firewalls for cloud computing.  A centralised cloud firewall makes it very difficult to predict the response time. Consequently, a new line of work has been established for actually implementing the new firewall techniques for the cloud. In order to reduce the cost of ownership and operation, it also strives to achieve resource optimization-based provisions and guidelines.
Minimizing User Costs in Multi-Broker Mobile Cloud Computing Networks
Authors: Baligo Anil Kumar
Published: May 2023

The success and disruption of cloud computing over the past few years is unparalleled.In order to quickly allocate or reallocate virtual computing as well as storage resources, Infrastructure as a Service (IaaS) providers offer commercially accessible services that enable scaling out or in computing capabilities in response to changing needs.Users can provide new apps using IaaS services without having to manage the supporting infrastructure. In this study, the system takes into account an MCC model in which several brokers allot cloud resources to mobile consumers. The approach is distinguished by a heterogeneous cloud architecture and by the many cloud service providers' pricing models. Both a public cloud and a cloudlet are part of it. A competitive strategy and a compete-then-cooperate approach are investigated by the system as a performance bound. The computer creates a cooperative issue with the restriction that none broker should be required to shell out beyond the dispute price, with the objective of lowering the overall average broker price.  The system uses a mix of the branch and bound paradigm with sophisticated convex relaxation techniques to develop a novel globally optimum solution algorithm to handle the ensuing non-convex cooperative issue.
Improving Efficiency of High Utility Sequential Pattern Extraction
Authors: Sharma Vikrant
Published: May 2023

Text mining used on texts and publications in the biomedical and molecular biology fields is referred to as "biomedical text mining." It is a relatively new area of study at the intersection of computational linguistics, bioinformatics, and natural language processing. Superior usefulness the goal of sequential pattern mining is to identify statistically significant patterns among data instances when the values are presented sequentially. Time series mining is typically regarded as a distinct activity even if it is closely linked since it is typically assumed that the values are discrete. Structured data mining has a unique use known as sequential pattern mining. High utility pattern (HUP) mining is one of the most relevant study areas in data mining nowadays since it is capable of taking into consideration the nonbinary frequency values of items in transactions as well as different profit values for each item. The utilization of previous data structures as well as mining outcomes, yet, enables incremental and interactive data mining to eliminate the need for further calculations when a database is updated or the minimum threshold is modified. The method in this study suggests three innovative tree architectures for effective incremental and interactive HUP mining. The high utility sequential pattern mining issue has formalised key ideas and elements.
Hiding the Data Using Hybrid of LSB and AES in Encrypted Image
Authors: Das Purushottam
Published: May 2023

Abstract

In this technique, which is known as "Reserving Room before Encryption," we first allotted some space for data embedding. Then, the picture was encrypted using the private key, which helps to safeguard the encryption process. Using a different private key, the data is then securely hidden in an encrypted image. Following points, we receive two private keys from the transmitter side, which increased the encryption and decryption processes' security. The data hiding the encrypted image and the two private keys are now sent to the receiver. Receivers can only retrieve data and images if they have access to the encryption key and the data concealing private key, respectively. Thus, the image and data were not easily recovered by the hackers.  Because of this, despite the AES algorithm's mathematical soundness, the primary underlying issues with it, like as speed and the use of subpar or tiny prime integers that have resulted in severe security breaches, may be resolved. A basic and commonly utilised technology worldwide is public key cryptography. The majority of public key algorithms, including AES, are based on modular arithmetic. As a result, performing public key encryption and decryption requires performing several modular multiplications on very large values. As a result, symmetric key algorithms are known to be faster than public key algorithms. It has recently become increasingly commonplace to use GPUs for general-purpose computing. The extremely parallel features have been used to significantly improve the performance of many computer problems.
Generating Document Summary using Data Mining and Clustering Techniques
Authors: Rana Deepak Singh
Published: May 2023

Abstract

This paper presents a novel approach to generating document summaries using data mining and clustering techniques, specifically K-means clustering and bisecting K-means clustering algorithms. With the exponential growth of textual data, there is an increasing need for efficient and accurate summarization techniques to aid users in understanding the key information within large collections of documents. This study explores the potential of data mining and clustering methods in extracting salient features from textual data and producing high-quality summaries. By applying K-means clustering and bisecting K-means clustering algorithms to the preprocessed textual data, the proposed approach groups similar sentences together and selects the most representative sentences from each cluster to form the final summary. The performance of the proposed method is evaluated using standard evaluation metrics, such as precision, recall, and F1-score, and compared with existing summarization techniques. The results demonstrate that the combination of data mining and clustering techniques provides a promising solution for generating accurate and concise document summaries, with potential applications in various domains, such as news aggregation, scientific literature summarization, and social media content analysis.
Machine Learning-Based Soil Classification
Authors: Aggarwal Kajal
Published: May 2023

A crucial component of agriculture is soil. There are several varieties of dirt. Different properties may be found in each kind of soil, and various crops can be grown on various types of soils. To understand which crops do better in different soil types, we need to be aware of their features and traits. In this situation, machine learning approaches may be useful. It has made significant development in recent years. In the realm of agricultural data analysis, machine learning is still a young and difficult study area. In this study, we provide a model that predicts soil series with regard to land type and, in accordance with prediction, suggests appropriate crops. For soil classification, a number of machine learning techniques are utilised, including Decision Tree (CART), Multilayer Perceptron (MLP) and support vector machines (SVM) using a gaussian kernel. The suggested SVM-based technique outperforms several current methods, according to experimental data. The support vector machine technique is used to examine and compare the classification outcomes of various training sample numbers. The purpose of this work is to evaluate the viability of land cover classification using small samples using SVM and, in accordance with the machine learning algorithm, to investigate novel techniques for quick, non-destructive, and precise land cover classification.
Development of a Smart Energy Monitoring System
Authors: Gupta Rajeev
Published: May 2023

The preservation of energy and the fight against global climate change are both benefited by computationally intelligent energy forecasting techniques for effective energy management at the consumer/producer side. Worldwide energy production and consumption are quite high, necessitating intelligent approaches with practicable implementation potential for effective energy management. The development of smart energy monitoring systems has become an increasingly important area of research in recent years. With the growing concern over climate change and the need to reduce greenhouse gas emissions, it is essential to find new ways to conserve energy and promote sustainable practices. One way to achieve this goal is by using smart energy monitoring systems that enable users to monitor and control their energy consumption. In this article, we will discuss the development of a smart energy monitoring system, its components, benefits, and challenges.
A Comprehensive Study of Printed Circuit Board (PCB) Design Techniques for High-Speed Digital Signal Transmission
Authors: Joshi Kamal Kant
Published: May 2023

The purpose of this work is to provide a comprehensive review of PCB design techniques for the transfer of digital data at very high rates. PCB design for high-speed digital signal transmission requires a number of important considerations, including the selection of the appropriate stickup and layer count, the management of impedance, the reduction of crosstalk, and the optimization of power and ground planes. In order to guarantee the reliability of high-speed signal transmission, it is necessary to conduct an exhaustive analysis of signal integrity problems. In this study, we look at a variety of different design approaches and compare and contrast the advantages and disadvantages of each one. In addition to this, we provide an all-encompassing method for the development of printed circuit boards (PCBs) for the transmission of high-speed digital signals. We also discuss some of the challenges that are faced when developing printed circuit boards for the transmission of high-speed digital signals. Some of these challenges include reducing the amount of electromagnetic interference and maintaining control over the dissipation of heat. Because of this, we have arrived at the conclusion that the reliable and effective operation of the electronic system is dependent on PCBs that have been designed using an approach that is both systematic and comprehensive to high-speed digital signal transmission.
Development of a Real-Time Audio Signal Processing System for Speech Enhancement
Authors: Naithani Deeksha
Published: May 2023

The requirements of real-time signal processing dictate that the audio signal must be completely processed before the subsequent audio segment may be received. This is done in order to achieve those requirements. This highlights how important it is to create methods of signal processing that are not just quick but also accurate. I describe many ways for processing audio signals in real time within the scope of this thesis. The publications that are being presented cover a wide range of issues, including noise dosimetry, speech analysis, and network echo cancellation, to name a few. In this article, the process of constructing a system that uses audio signal processing to improve speech in real time is broken down and examined. Speech enhancement makes speech more audible and comprehensible in noisy surroundings, which is beneficial to individuals who have hearing loss as well as speech recognition and communication systems. Signal-to-noise ratio, power spectral efficiency ratio, and spectrum transfer entropy index are the metrics that are utilized to evaluate speech quality enhancement system development strategies, abbreviated as STOI. Because studies have shown that being exposed to loud noises over extended periods of time can have severe impacts on health, it is essential to have precise methods for detecting the levels of noise. The findings of a study that measured exposure to noise while also taking into account the impact of the speaker's own voice are presented in this article.
Identify the After-Sales Service Quality Factors, Influence the Repurchase Intentions of Selected White Goods
Authors: Kaur Rupinder
Published: May 2023

The expansion of the white goods industry is directly related to the level of consumer contentment, the quality of after-sale care, and the likelihood of future product purchases. To ensure customer happiness, businesses must do things like promptly replacing broken components throughout the product's warranty term and providing straightforward instructions for obtaining assistance. It is crucial for companies selling consumables like home items, pet food, cosmetic basics, and baby supplies to encourage consumers to renew their supply on a regular basis. This is the type of purchasing behavior that may increase ROI and guarantee long-term success. White-goods repurchase intent was analyzed in this research. Because of this, the Kendall's W test is used to determine whether or not there is a statistically significant difference between groups that otherwise seem the same.
Analyze the Perception and Satisfaction of Employees with Respect to Hrd Practices
Authors: Batola Megha
Published: May 2023

This study adopts a framework of social exchange theory and tries to better understand the effect of human resource development (HRD) approaches on job satisfaction and intent to leave. Human resource development (HRD) techniques are linked to higher levels of work satisfaction and lower rates of employee turnover if certain conditions are met. Previous studies have shown that HRD practices have an effect on workers' contentment at work and their motivation to quit, but the present study adds to the literature by empirically assessing the moderating role of POP in this relationship. A questionnaire is used to collect primary data from 40 respondents who are employed by government agencies. The results show that HRD practices significantly affect both job contentment and quit intentions, with the impression of organizational politics serving as a moderating factor. Using a quantitative methodology, this research surveyed employees to get their opinions on the HRD roles within their firm.
Awareness of Policyholder in Life Insurance Corporation Policies
Authors: Kaur Rupinder
Published: May 2023

When it comes to economic growth and development, the insurance business is often regarded as one of the most crucial sectors, especially in a country like India. Awareness-raising is a critical component of an integrated strategy for expanding the insurance market. There was a rising knowledge of the benefits and viability of life insurance in the 19th century, but many Indians still believed that purchasing such a policy meant intentionally courting death. Despite the fact that insurance provides invaluable protection for everyone, life insurance is frequently referred to as "Registration for Death" in the local languages of India. Identifying the demographics of the sample population and gauging policyholders' knowledge with LIC's services are the key objectives of this research. This study provides inspiration for raising brand awareness among consumers. Primary information is needed for the research. Three hundred policyholders were interviewed using a predetermined interview schedule and a basic random sample technique to collect primary data. The growth of the Indian economy relies heavily on the life insurance industry. Life insurance is a company that provides consumers with long-term financial solutions.
A Study on Design and Development of Geopolymer Concrete
Authors: Kumar Mayank
Published: May 2023

Ordinary Portland Cement (OPC), the major element of concrete, has a significant effect on the environment and is one of the most energy-intensive building materials. Being a relatively new building material, geopolymer concrete is one of the technologies that scientists have spent the better part of the previous two decades trying to replace with OPC. It has also been shown that geopolymer can replace OPC as a concrete binder. Further research into geopolymer concrete's characteristics utilizing alternative waste materials like RHA and GGBS, which are high in aluminous or silicate content, is required.
Hazardous Jarosite Waste's Potential for use as a Construction Material in Civil Engineering
Authors: Danu Ritiksha
Published: May 2023

The rate of industrialization is a good indicator of a country's progress. Large quantities of trash and by-products are produced as a result of several operations, including those that are industrial, mining, municipal, agricultural, and many others. In addition, these materials are being explored for potential use as filler in embankment design, as well as sub-base or base layers in road building. Use of potentially dangerous jarosite waste mixed with steel industry waste is advocated in the present research.
A Study on Numerical Analysis of Gol-Gumbaz's Dynamic Behavior
Authors: Negi Ankit
Published: May 2023

Historically significant masonry buildings, particularly those in seismically active zones, have often been destroyed by earthquakes. High inertial mass, weakening of strength and stiffness of structural materials with time, loose connections between load-bearing walls and floors, and pliability of floors are the primary causes of their exceptional sensitivity to earthquakes ence, it's crucial to evaluate the seismic behavior of these old stone buildings and estimate their capacity so that the design foundation for strengthening or retrofitting techniques and interventions may be established. On the premise of a fixed base, this work presents the results of a numerical analysis of GolGumbaz using the method.
Effect of Higher Modes on the Design of Structures that Resist Earthquakes
Authors: Shah Mahesh Chandra
Published: May 2023

Seismic waves carry the earthquake's kinetic energy to the structure, where it is then transferred into vibrational motion through the foundations. The reaction acceleration for extremely short time period (high frequency) structures will be close to the maximum acceleration felt by the ground. Long-lasting buildings can take use of the system's adaptability; the mass won't budge no matter how much the earth shifts underneath it, and the relative deformation will be just as large. Examine the impact of higher modes on earthquake-proof building design in this research.
A Study on Behavior of Headed Bars Made of Fiber-Reinforced Polymers in Rcc Exterior Beam Column Joints
Authors: Danu Ritiksha
Published: May 2023

Seismic pressures on a reinforced concrete moment-resisting structure are known to create critical and essential regions at the reinforced beam-column joints. Beam-column junctions are a microcosm of the whole structure, therefore brittle behavior at these points predicts brittle action. Ductile behavior at beam-column joints predicts ductile action. Under the influence of seismic forces, the joints between RC beams and columns are exposed to high shear stresses. Learn more about the performance of fiber-reinforced polymer headed bars in rcc external beam column joints in this publication.
A Study on Energy Dissipation in Rcc Beam Column Joints Using Semi Rigid Connection
Authors: Negi Ankit
Published: May 2023

Beam column joint failure has been linked to the partial or whole collapse of reinforced structures, according to information gathered during seismic reconnaissance. Under seismic stress, the failure of a single beam has only a limited impact, but the failure of a beam-column junction might lead to the collapse of the whole structure. The resulting economic damage and human casualties are substantial. Hence, keeping the beam and column from coming apart is crucial. Recent earthquakes have caused significant damage to important structures, and this is mostly attributable to a lack of ductility in the beam-column junction. The ductility performance was enhanced by the design of a specific moment resistant frame. There is less moment transmission and greater yielding under lateral load in steel and pre-fabricated constructions that make use of semi-rigid connections. In this project, we use an analytical approach to examine the impact of semi-rigid connections in the RCC columns and beams of reinforced concrete high-rises.
A Study on Properties of Hybrid Fiber Reinforced Concrete's Strength
Authors: Bahuguna Anoop
Published: May 2023

Improvements in concrete's tensile strength, post-cracking resistance, and brittleness are perennial goals in the field of civil engineering and construction. Strong and adaptable hybrid fiber reinforced concrete is being developed using new techniques. Hybrid fiber reinforced concrete (HFRC) is a concrete matrix containing hybrid fibers randomly scattered throughout. The hybrid fibers utilized in the current study of fiber-reinforced concrete were a mixture of steel fiber and polyvinyl alcohol (PVA) fiber. Workability, compressive, split tensile strength, and flexural strength of the HFRC matrix were all measured as part of an extensive experimental program aimed to examine the impacts of the aforementioned hybrid fiber.
Smart Attendance: An Automated Attendance Management System Using Machine Learning Techniques
Authors: Rana Deepak Singh
Published: May 2023

The Smart Attendance system is a machine learning-based system that aims to improve the management and tracking of student attendance. It can be utilized in educational institutions to keep track of their students' attendance. The system uses radio frequency identification tags to record information in real time, which helps the system to provide updated and accurate records. The system uses machine learning techniques to analyze the data collected by it to identify trends and patterns in student attendance. This method can then be used to identify individuals who are habitually late or have high absenteeism rates. It can help educators implement effective measures to improve student attendance. The performance of various machine learning algorithms was evaluated in this study. The four algorithms were randomly forest, decision tree, SVM, and naive bayes. They were tested against conditions such as students who frequently skip certain classes, habitual latecomers, and individuals with high absenteeism rates. The results of the study revealed that the SVM and random forest algorithms performed well and had the highest accuracy rates. These findings show that these algorithms can help educators identify patterns in student behavior and implement effective measures to boost attendance. The Smart Attendance system has demonstrated the potential of artificial intelligence to help improve the management of student attendance in educational institutions. Through the use of machine learning and RFID tags, the system can provide up-to date and accurate records, as well as insights into the behavior of students. This information can then be utilized to implement effective measures to reduce absenteeism and improve student outcomes.
Design and Implementation of an AI Chatbot for Customer Service
Authors: Harbola Aditya
Published: May 2023

Abstract

A company's customer service is very important to its success. It can help boost revenue and retain customers. As digital technology has increased the demand for 24-hour support, businesses are now turning to AI chatbots to provide better and more personalized service. Artificial intelligence (AI) chatbots can help businesses improve the customer experience and reduce the workload of their customer service agents. The paper presents the development and implementation of a chatbot utilizing NLP and AI techniques. It aims to provide efficient and personalized responses to customers' inquiries. The research process involved gathering and analyzing data, developing the chatbot's framework, and carrying out the study. Its architecture and framework were built with the help of NLP and AI. This feature allows the chatbot to respond to users' natural language queries. Its features were also designed to help customers navigate through various tasks and provide recommendations. The chatbot was well-received by its users and was able to provide effective and efficient customer service. The findings of the study indicate that the potential of AI and NLP in enhancing the experience of customers is immense.
Enhancing MNIST Digit Recognition with Ensemble Learning Techniques
Authors: Kapil Divya
Published: May 2023

Abstract

The classification task known as MNIST digit recognition involves identifying handwritten numbers into their corresponding values. Although there are numerous approaches proposed for this type of task, they typically face issues in achieving high accuracy. One method that can improve single models' performance is through ensemble learning. The goal of this study is to explore the use of various learning techniques, such as boosting and bagging, in combination with random forest models and decision trees, to improve the performance of MNIST digit recognition with regard to accuracy. We then perform evaluations on these methods using various metrics, such as recall, precision, accuracy, and F1. The findings of this study provide valuable insight into the various advantages of ensemble methods for the MNIST digit recognition task. It also highlights the need to explore these techniques in the context of machine learning. The objective of this study is to investigate the use of ensembles in improving the accuracy of MNIST digit recognition. We performed evaluations on two popular methods, namely boosting and bagging, with random forest and decision tree models. The evaluation parameters included F1 score, recall, accuracy, and precision. The results of the evaluations revealed that both boosting and bagging methods performed well in terms of their evaluation metrics. In most cases, the decision tree performed better than the random forest. However, the random forest method was able to achieve the highest accuracy, which is 99 percent. The findings of the evaluation revealed that ensembles can help improve single models' accuracy in MNIST digit recognition. On the other hand, the random forest method is a promising option for this task. The exact results of the evaluations will vary depending on the evaluation and implementation metrics. More research is needed to confirm their generalizability. The study emphasizes the value of exploring ensembles in machine learning systems, as well as the potential advantages of performing MNIST digit recognition using them.
Iot Based Precision Farming and Agriculture - Aspects and Technologies
Authors: Arya Chandrakala
Published: May 2023

The emergence of Internet of Things (IoT) technologies has presented novel prospects for precision farming and agriculture. This paper presents a comprehensive analysis of recent research studies pertaining to Internet of Things (IoT) applications in the agricultural sector. The objective of this review is to furnish a thorough examination of the diverse facets and technologies linked with precision farming based on the Internet of Things (IoT).

 The aforementioned review presents a comprehensive overview of the primary discoveries and perspectives derived from said investigations. This study investigates the utilisation of sensor networks and data collection methodologies to facilitate the contemporaneous monitoring of soil moisture, temperature, humidity, and crop health. The utilisation of remote sensing methods, such as the utilisation of satellite imagery and drones, was examined as a means of monitoring crops and estimating yield.

The study investigated the efficacy of resource optimisation and automation tactics, including intelligent irrigation systems, in the domains of resource conservation and productivity enhancement. The convergence of Internet of Things (IoT) technologies and Decision Support Systems (DSS) was a key area of focus, examining the creation of data-centric insights and suggestions for agricultural practitioners. The successful implementation of IoT-based agricultural systems was found to be influenced by critical factors such as connectivity and communication infrastructure, as well as security and privacy concerns.

The present review article offers significant perspectives on the progress and constraints of precision farming and agriculture based on the Internet of Things (IoT), underscoring the significance of additional research and development endeavours in this swiftly developing domain.