bluuebunny/crossref_metadata_2025 huggingface dataset:
USE OF NANO SILICA AND COIR FIBER TO STRENGTHEN THE ENGINEERING PROPERTIES OF CLAYEY SOIL
Authors:   PG student, Dept. of Civil Engineering, Sri Sai College of Engineering& Technology, Badhani, Punjab, India, Abass Qammar
Published: April 2024

One of the problematic soils is expansive soil, which has a strong tendency to shrink when dried and to inflate when its moisture level changes. In India, expansive soils made up of clays are referred to as black cotton soils. The alternating swell-shrink tendency of expansive soils results in foundation structures, including buildings and earth retaining wall pavements, to become distressed. For geotechnical engineers, comprehending the behavior of expansive soil and implementing the proper control measures has been a challenging challenge. A lot of study is being done to develop solutions for soils that produce black cotton. This review study uses the soil index and engineering features to examine the behavior of clayey soil stabilized with different proportions of Nano silica and Coir Fiber. Coir is a naturally occurring, biodegradable material that is widely accessible in several coastal and southern regions of India. Nano silica is a type of filler material that increases ductility without compromising strength. With less swelling potential, the generated nano silica functioned as a hydraulic binder. In expansive soils, coir fiber and nano silica are combined for the goal of sustainable development. As part of this comparison study, laboratory tests including Atterberg's limit, Compaction test, CBR test, and UCS test were performed for both modified and unmodified clayey soil. In this work, expansive soil was stabilized using Nano silica and Coir Fiber. Coir Fiber was fixed at 3.5%, after that Nano silica was changed to 2%, 4%, and 6%. Key Words: Compaction test, CBR, UCS, Coir Fiber, Nano Silica
Investigational Study on the Influence of Sisal Fiber and Rice Husk Ash in the Stabilisation of Clayey Soil
Authors:   PG student, Dept. of Civil Engineering, Sri Sai College of Engineering& Technology, Badhani, Punjab, India, Haq Sheikh Azhar Ul
Published: April 2024

The process of improving and enhancing the engineering qualities of soil so that it can support large loads without failing is known as "soil stabilization" in the realm of civil engineering. The behavior of the soil was examined in the current work at various proportions following the addition of Rice Husk Ash (fixed at 15%) and sisal fiber (varying at 1.5%, 2.0%, and 2.5%; length 40 mm). Various soil properties, such as OMC, MDD, CBR value, and UCS values, were then quantified. Utilizing Sisal Fiber and Rice Husk Ash together can enhance the diverse engineering features of soil, according to the conclusion of this experimental research project. Laboratory tests such Atterberg's Limits, Specific Gravity, Particle Size Distribution, Standard Proctor test, Unconfined Compressive Strength (UCS), and California Bearing Ratio (CBR) were carried out to examine the engineering qualities of the soil and its mixtures. The results showed that the engineering qualities of clayey soil were significantly enhanced by the addition of Sisal Fiber and Rice Husk Ash. As a result, Sisal Fiber and Rice Husk Ash may act as soil stabilizing agents, making clayey soil a better choice for building foundations. All testing were carried out in accordance with Indian standards regulations. Key Words: Compaction test, CBR, UCS, Rice Husk Ash, Sisal Fiber
Stock Market Analysis Using Machine Learning
Authors:   COMPUTER SCIENCE AND ENGINEERING   TIRUMALA ENGINEERING COLLEGE, N Dr. GOPALA KRISHNA
Published: April 2024

The stock market is a dynamic and complex financial system influenced by numerous factors, making accurate forecasting a challenging task. Traditional methods often fall short in capturing the intricate patterns within financial data. This research paper explores the application of machine learning techniques for stock market forecasting, aiming to provide a comprehensive overview of the current state of the field. The paper reviews relevant literature, discusses popular machine learning algorithms, and assesses their effectiveness in predicting stock market trends. Additionally, real-world applications and challenges in implementing machine learning for stock market forecasting are explored.
Enhancing Hospitality Through Geofencing
Authors:   Department of Computer Science and Engineering, D. Y. Patil College of Engineering and Technology, Kolhapur, Maharashtra, India, Gaikwad Mayur M.
Published: April 2024

Abstract: “Enhancing Hospitality Through Geofencing” provides a location-based offer notification system designed to enhance customer engagement and promote sales in retail environments. The system utilizes geofencing technology to deliver focused offers to customers based on their real – time location. Sellers can create and manage offers within predefined geographic areas using mobile application interface. When customer enters these geofenced areas, they receive notifications on their mobile devices about available offers from nearby sellers. Key words : Location-based services, Geofencing, offer notifications, mobile application, customer engagement
IMPACT OF LONG WORKING HOURS ON HOTEL EMPLOYEES
Authors:   VIVEKANANDA GLOBAL UNIVERSITY, JAIPUR (2023), SINGH RAVNEET
Published: April 2024

This article focuses on today’s scenario of working culture in the hospitality industry and the major steps taken care of by the human resource department for the well-being of hotel employees in the Indian hospitality sector. Guest will be happy when he finds all the services flawless, which he expected from the hotel but providing such overwhelming services to the guest is a task of the hotel employees who are working 24*7 in the hotel. Sometimes hotel employee has to work 12-15 hours or more than that in a single shift. Due to such long hours of shift timings employee surely face some physical and mental issues on his health. All these issues need to be taken care of by the human resource department of the hotel. They have to work on it strictly and minimize the time individual employees spend in the hotel.
RED WINE QUALITY PREDICTION USING MACHINE LEARNING
Authors:   Student, Department of Computer Science and Engineering, Tirumala Engineering College, Laasya Lakkaraju Datha Sri
Published: April 2024

Wine quality assessment is a critical task in the beverage industry, as it directly impacts consumer satisfaction and market competitiveness. Among various types of wine, red wine stands out for its complexity in flavor, aroma, and texture, making its quality prediction a challenging yet crucial endeavor. This project aims to develop a machine learning model for predicting the quality of red wine based on its physicochemical properties. The dataset utilized for this project consists of various attributes such as acidity, pH, alcohol content, and volatile acidity, among others, collected from red wine samples. These attributes serve as input features for the machine learning model, while the quality of wine, typically rated on a scale, serves as the target variable. The proposed approach involves several stages: data preprocessing, feature selection, model selection, and evaluation. During data preprocessing, techniques like normalization and handling missing values are employed to ensure data quality. Feature selection techniques such as correlation analysis and feature importance are utilized to identify the most relevant attributes for predicting wine quality. Several machine learning algorithms, including but not limited to, linear regression, decision trees, random forests, and support vector machines, are trained and evaluated to determine the most suitable model for the task. Performance metrics such as mean squared error, accuracy, and F1 score are utilized to assess the model's predictive capability. Key Words: Wine quality, market, aroma, texture, physicochemical, alcohol, acidity.
Credit Card Fraud Detection: A Machine Learning Approach
Authors:   Assistant Professor Cyber Security Engineering, Shah and Anchor Kutchhi Engineering College, Mumbai, Maharashtra, India, Pote Prajakta Nikhil
Published: April 2024

The proliferation of credit card transactions in the digital era has exacerbated the threat of fraudulent activities, necessitating robust detection mechanisms. Traditional rule-based systems often fail to keep pace with the evolving nature of fraud, prompting the adoption of machine learning techniques. This research paper explores the efficacy of machine learning algorithms in detecting credit card fraud, aiming to enhance the security of financial transactions. Leveraging a comprehensive dataset comprising legitimate and fraudulent transactions, various machine learning models are trained, tested, and evaluated. Through meticulous experimentation and analysis, key insights are unearthed regarding the performance, strengths, and limitations of different algorithms in detecting fraudulent patterns. Furthermore, feature engineering techniques and ensemble learning methods are employed to improve detection accuracy and efficiency. The findings underscore the significance of adopting a holistic machine learning approach for credit card fraud detection, offering valuable insights for financial institutions and stakeholders in fortifying transaction security. Keywords: Credit card transactions, digital era, fraudulent activities, detection mechanisms, rule-based systems, credit card fraud.
IMAGE CAPTION GENERATOR USING DEEP LEARNING
Authors:   School of Engineering – IIIrd year AI&ML-ZETA, Malla Reddy University, Kompally, Hyderabad, India, Khan Sufiyan Ali
Published: April 2024

Image Captioning is a task where each image must be understood properly and are able generate suitable caption with proper grammatical structure.Here it is a hybrid system which uses multilayer CNN (Convolutional Neural Network) for generating keywords which narrates given input images and Long Short Term Memory(LSTM) for precisely constructing the significant captions utilizing the obtained words .Convolution Neural Network (CNN) proven to be so effective that there is a way to get to any kind of estimating problem that includes image data as input. LSTM was developed to avoid the poor predictive problem which occurred while using traditional approaches. We used an encoder-decoder based model that is capable of generating grammatically correct captions for images. This model makes use of VGG16(Visual Geometry Group) as an encoder and LSTM as a decoder. The model will be trained like when an image is given model produces captions that almost describe the image. The efficiency is demonstrated for the given model using Flickr8K data sets which contains 8000 images and captions for each image but we use CNN and LSTM to capture dependencies and tell both the spatial relationships of images and contextual information of captions and generate contextually relevant captions. Keywords—CNN(Convolutional Neural Network),LSTM(Long Short Term Memory),VGG16(Visual Geometry Group),Deep Learning,Encoder-Decoder.
COMPARATIVE FINANCIAL ANALYSIS OF STATE BANK OF INDIA AND HDFC BANK
Authors:   Galgotias University, Singh, Aditi
Published: April 2024

India's banking sector has come under scrutiny. The cost of finances for banks has increased significantly as the Reserve Bank of India( RBI) raised interest rates to help the falling Indian currency, the Rupee. This may show up as a rise innon-performing means( NPAs) and a drop in profitability. Internal and external factors have an effect on bank profitability. The end of this paper is to compare and estimate the fiscal performance of India's largest public and private sector banks and also to understand their trends of NPAs through secondary data analysis. Both banks' earnings were guided by bank-specific criteria and threat factors. influence fiscal rates of SBI were set up at advanced dereliction threat than HDFC. Productivity measures were the crucial motorists of gains at India's largest private sector bank HDFC but not at SBI. Asset operation effectiveness measures were nearly same of both the banks. NPA rates were much advanced at India's largest public sector bank SBI. The single most important determinant of SBI proved to be the liquidity rates. Two sample T test assuming equal dissonances was also conducted to check if there was significant difference between the NPA rate of SBI and HDFC bank over the last 3 times.
REGULATORY FRAMEWORK AND COMPLIANCE CHALLENGES IN DIGITAL PAYMENT:A CASE STUDY ON PAYTM
Authors:   Galgotias University, Vershney Utkarsh
Published: April 2024

India is not only the world's largest independent democracy, but it is also a rapidly growing economic powerhouse. No country can have a stable economy without a sound and efficient banking system. Banks play a critical role in a country's economic growth. They collect people's unused savings and make them eligible for investment. They're in the process of granting loans and purchasing investment securities, new demand deposits are also established. Accepting and discounting bills of exchange allows for trade both within and outside the country. Banks also help to improve capital mobility. India's banking system has a long list of notable accomplishments over the last three decades. It is no longer limited to the cities, but has spread to even the most remote parts of the world. This is one of the factors behind India's development. The banking industry is now one of India's most important service industries. The availability of high-quality services is critical to the economy's success. Banks' attention has turned away from customer acquisition to customer retention. The introduction of Information Technology into the banking sector has changed the way people work. The banking sector's policy has undergone radical transformations, various customer-oriented products, such as internet banking, are available. Customer’s workload has been reduced mainly because of ATM providers, telebanking, and electronic payments. The internet's convenience Banking allows a customer to access and manage his bank account without 7 having to go to the bank. 'The Customer's options have been revolutionized by the availability of ATMs and credit/debit cards.
Car Price Prediction Using Regression Techniques of Machine Learning
Authors:   Department Of Computer Science and Engineering  Bachelor Of Technology Tirumala Engineering College, P Ms. Jasmine
Published: April 2024

A car price prediction has been a high-interest research area, as it requires noticeable effort and knowledge of the field expert. Considerable number of distinct attributes are examined for the reliable and accurate prediction. To build a model for predicting the price of used cars in Bosnia and Herzegovina, we applied three machine learning techniques (Artificial Neural Network, Support Vector Machine and Random Forest). However, the mentioned techniques were applied to work as an ensemble. The data used for the prediction was collected from the web portal autopijaca.ba using web scraper that was written in PHP programming language. Respective performances of different algorithms were then compared to find one that best suits the available data set. The final prediction model was integrated into Java application. Furthermore, the model was evaluated using test data and the accuracy of 91% was obtained.
USE OF WASTE PLASTIC WITH BACTERIAL COATING AS A SUSTAINABLE BUILDING MATERIAL IN CONCRETE
Authors:   DR. A. D. SHINDE COLLEGE OF ENGINEERING. Bhadgaon, Gadhinglaj. Dist: Kolhapur Pin: 416502  Department of Civil Engineering, V. S. Prof. PATIL
Published: April 2024

The use of plastic is increasing day by day, although steps were taken to reduce its consumption. The suitability of recycled plastics shredded as coarse aggregate in concrete and its advantages are discussed here. Tests were conducted to determine the properties of plastic aggregate density, specific gravity and aggregate crushing value. As 100% replacement of natural coarse aggregate (NCA) with shredded plastic is not feasible, partial replacement at various percentage were examined. Higher compressive strength was found with 5% NCA replaced concrete.
Image Forgery Detection Using Machine Learning
Authors:   Department Of Computer Science And Engineering ,  Bachelor Of Technology  Tirumala Engineering College, Y Harshitha,
Published: April 2024

Digital Image Forgery can be done by deceiving the digital image to mask some meaningful or important data of the image. It is usually difficult to spot out the manipulated region of the original image. To sustain the uprightness and legitimacy of the image, the detection of forgery in the image is mandating. Acclimation of the modern way of life and advancement in photography gadgetry has made exploitation of digital image easy with the help of image editing software. Therefore, it is crucial to detect such image forgery operations in the images. The image forgery detection can be done based on object removal, object addition, unusual size modifications in the image. Images are one of the powerful media for communication. In this project we have used algorithms such as Copy move, Canny Edge Defection, Structure Similarity index, Hierarchical Agglomerate Clustering and Neural Network algorithms with improved accuracy rate.
"Assessment of Water Quality in the Mula-Mutha River: Challenges, Solutions, and Analysis
Authors:   Assistant Professor, Dept. of Civil Engineering, VIIT, Pune, Maharashtra, India, Sawalkar Nitin
Published: April 2024

Mula and Mutha River being the major rivers provide adequate supply of water for the various needs. While, Mula-Mutha River in pune (India) is one of the most vulnerable water bodies to pollution because of their role in carrying municipal and industrial wastes and run-offs from agricultural lands in their vast drainage basins. Despite of the various standards and laws made by government many industries were discharging their waste directly into the river making its quality poor day by day. As this water is further used for drinking and other purposes at the downstream of the rivers, its quality has much importance. The water treatment plants in Pune have been under constant development ensuring to accomplish the need of water. The constant increase in this pollution of water has brought down the efficiency of water treatment plants. Thus, there is a need for analysis of water to be done prior to usage. This is a study for assessment of the river water quality specifically Mula-Mutha river, to find its current suitability, condition and further analysis. This study also includes study of enacted solutions to river treatment and evaluation of the drawbacks they include. Key Words: Municipal and Industrial Wastes, Run-offs, Quality of water, Water Treatment
Enhancing Transparency and Accountability in Predictive Maintenance with Explainable AI
Authors:   Department of Computer Science and  Engineering  Chandigarh University  Mohali, India, soni, Rajat
Published: April 2024

Predictive maintenance is a critical aspect of industrial operations, enabling proactive identification and mitigation of potential failures in machinery and equipment. However, the widespread adoption of AI-driven predictive maintenance solutions has been hindered by the opaque nature of many machines learning models, raising concerns about transparency, accountability, and trust. This research aims to address these challenges by developing explainable AI techniques for predictive maintenance in industrial systems. By integrating interpretability methods with advanced predictive models, we seek to enhance the transparency and interpretability of AI-driven maintenance decisions. Our proposed methodology combines state-of-the-art machine learning algorithms with local and global explainability techniques, such as LIME, SHAP, and feature importance analysis. Through extensive experiments on real-world industrial data, we evaluate the performance of our explainable AI models and demonstrate their ability to provide insightful explanations, enabling domain experts to understand the underlying reasoning and critical factors contributing to maintenance predictions. Furthermore, we explore the impact of explainable AI on improving trust, accountability, and adoption of AI systems in industrial predictive maintenance scenarios. Keywords— Predictive Maintenance, Explainable AI (XAI), Machine Learning, Interpretability, LIME, SHAP, Feature Importance, Industrial Systems, Trust in AI, Accountability.
Assessing the Impact of Word-of- Mouth Marketing in Local Markets
Authors:   Galgotias University, Greater Noida, India, Kaushik Ashu
Published: April 2024

Word-of-mouth marketing (WOMM) is a dynamic and impactful phenomenon that has sparked substantial interest among researchers and practitioners alike. Within local markets distinguished by close-knit communities and interpersonal interactions, WOMM occupies a unique role as a powerful influencer of consumer behavior. This study investigates the multiple features of WOMM in local contexts, with the goal of fully assessing its impact on consumer decision-making and brand perception The study takes a mixed-methods approach, combining qualitative and quantitative tools to capture the intricacies of WOMM dynamics. Through surveys and in-depth interviews with consumers and business owners, the study investigates the underlying mechanisms and drivers that contribute to the effectiveness of WOMM initiatives in local markets. By Additionally, the study looks into how word-of-mouth communication is changing in the digital era, with social media and online forums acting as amplifiers for these talks. Through an examination of the interactions between offline and online platforms, the research illuminates the ways in which technology influences the spread and influence of word-of-mouth communications in local marketplace. The research's conclusions offer significant insights to the academic and industrial communities. Businesses can obtain a better grasp of how to use community networks and interpersonal relationships to improve their marketing strategy by learning about the nuances of word-of-mouth marketing (WOMM) in local markets. These data can also be used by marketers and legislators to create more focused and efficient methods of interacting with customers in local contexts, strengthening brand-consumer bonds and boosting sales in the process.
CLIMATE CHANGE AND IT’s EFFECTS ON MANUFACTURING SECTOR
Authors:   Galgotias University, Greater Noida, India, Khan Raguel
Published: April 2024

This research examines the complex link between the industrial sector and climate change, evaluating the effects of extreme weather events and rising global temperatures. It seeks to understand the many ways that environmental changes impact industrial operations, with a focus on how resource constraints, weather patterns, and environmental laws affect production sustainability and efficiency. By employing case studies, statistical analysis, and sophisticated modelling, the study pinpoints the industries, regions, and production processes that are susceptible to disruptions caused by climate change. Potential disadvantages are discussed along with chances for innovation and broad adoption of green technology. The research highlights the need for collaboration among politicians, business stakeholders, and environmental activists in achieving significant change. The paper advocates for collaborative efforts to enhance manufacturing sustainability and resilience by emphasizing the links between environmental effects and industrial innovation. The intention is to give policymakers and business leaders a road map for creating strategies that reduce risks and promote long-term development. This research offers comprehensive insights into possibilities, problems, and strategies related to manufacturing's sustainable growth and adaptation to climate change, which makes a substantial contribution to the worldwide discourse on these topics. To support educated decision-making and positive change for an environmentally conscious industrial landscape, it offers legislators, business partners, and environmentalists an invaluable resource for understanding, navigating, and addressing the profound effects of climate change on the manufacturing sector.
Blockchain-Based Management for Organ Donation and Transplantation
Authors:   Student : MGIT, Divya, Dr. N. Sree
Published: April 2024

Today’s organ donation and transplantation systems pose different requirements and challenges in terms of registration, donor-recipient matching, organ removal, organ delivery, and transplantation with legal, clinical, ethical, and technical constraints. Therefore, an end to-end organ donation and transplantation system is required to guarantee a fair and efficient process to enhance patient experience and trust. We propose a private Ethereum blockchain based solution to enable organ donation and transplantation management in a manner that is fully secure, traceable, auditable, private, and trustworthy. We develop smart contracts that ensure the data provenance by recording events automatically. We present algorithms with their implementation, testing, and validation details. We evaluate the performance of the proposed solution by performing privacy, security, and confidentiality analyses as well as comparing our solution with the existing solutions. Key Words: organ, donation, private, confidentiality
IMPACT OF SOCIAL MEDIA ON STUDENTS
Authors:   Galgotias University, Jha Raj Ranjan
Published: April 2024

In the contemporary digital age, social media has become an integral part of daily life, profoundly influencing various aspects of society, including education. This research paper delves into the multifaceted impact of social media on students, encompassing both the positive and negative ramifications. The positive effects of social media on students include enhanced communication and collaboration, access to vast educational resources, and opportunities for self-expression and creativity. Additionally, social media platforms can facilitate peer support networks, fostering a sense of belonging and community among students. Moreover, social media can serve as a platform for academic and career networking, enabling students to connect with mentors, professionals, and potential employers. However, the pervasive use of social media also poses significant challenges and risks for students. Excessive screen time and engagement with social media have been linked to decreased academic performance, attention deficits, and sleep disturbances. Furthermore, social media platforms often expose students to cyberbullying, online harassment, and negative social comparison, leading to psychological distress and decreased self-esteem. Moreover, the dissemination of misinformation and fake news on social media can hinder students' critical thinking skills and undermine their ability to discern credible sources of information. This research paper synthesizes existing literature to provide a comprehensive understanding of the impact of social media on students, highlighting the complexities and nuances of this phenomenon. By elucidating the various dimensions of social media's influence on students, this paper aims to inform educators, parents, policymakers, and students themselves about the opportunities and challenges inherent in navigating the digital landscape. Additionally, the paper proposes recommendations for promoting responsible social media usage and mitigating its adverse effects on students' well-being and academic success.
The impact of data visualization technique on decision making in business analytics Vaibhav,
Authors:   Galgotias University, Bisht Prof Diksha
Published: April 2024

Data visualization plays a crucial role in the field of business analytics, significantly influencing decision-making processes. By presenting complex data in a visually appealing and easily understandable format, data visualization helps stakeholders and decision-makers to identify patterns, trends, and insights that would otherwise go unnoticed. This, in turn, aids in making informed decisions, improving business performance, and achieving strategic goals. Data visualization has emerged as a critical tool in the realm of business analytics, revolutionizing the way organizations interpret, analyse, and make decisions based on data. In an era inundated with vast amounts of data, the ability to transform complex datasets into meaningful visual representations has become indispensable for businesses seeking to gain actionable insights. This introduction will delve into the profound impact of data visualization on decision-making in business analytics, exploring its role in enhancing comprehension, facilitating communication, and driving strategic outcomes. Enhancing Comprehension: One of the primary advantages of data visualization is Its ability to distil intricate datasets into intuitive visuals that are easily understandable to stakeholders across various levels of an organization. Unlike traditional tabular formats or raw data sets, visual representations such as charts, graphs, and dashboards offer a more accessible means of interpreting complex information. By leveraging graphical elements such as colour, shape, and size, data visualization enables users to discern patterns, trends, and correlations that might otherwise remain obscured within the data. This enhanced comprehension empowers decision-makers to grasp key insights swiftly and make informed judgments based on a comprehensive understanding of the underlying data. Facilitating Communication: Effective communication of insights derived from data analysis is essential for driving alignment and informed decision-making within organizations. Data visualization serves as a powerful medium for conveying complex findings in a concise and compelling manner. Visualizations not only provide a common language through which diverse stakeholders can interpret and discuss data but also foster collaboration by facilitating the sharing of insights across departments and teams. By presenting information in visually engaging formats, such as interactive dashboards or infographics, data visualization promotes clarity and transparency in communication, enabling decision-makers to convey key messages more effectively and garner consensus around strategic initiatives.
Energy Consumption and Economic Growth Nexus in India
Authors:   Amity School Of Economics, Uttar Pradesh, Khanna Anchal
Published: April 2024

This research investigates the intricate relationship between energy consumption and economic growth in India, aiming to understand how changes in energy usage impact economic development. The study delves into the causal link between renewable energy consumption and economic growth, considering factors such as capital and technology. Additionally, it explores the impact of energy consumption on agricultural economic growth, emphasizing the vital role of energy in the agricultural sector. The examination extends to assessing the relationship between electricity consumption and overall economic growth, along with sectoral growth across major Indian states, providing valuable regional insights for tailored energy strategies. Furthermore, the research extends its purview to unravel the nuanced impact of energy consumption on agricultural economic growth. Recognizing the indispensable role of energy in the agricultural sector, the study accentuates the pivotal contribution of energy dynamics to the overall economic landscape. This emphasis underscores the significance of aligning energy policies with the unique needs and challenges of the agricultural domain, crucial for fostering balanced and sustainable economic growth.
5G SMART DIABETES TOWARDS PERSONALIZED DIABETES DIAGNOSIS WITH HEALTH CARE BIGDATA CLOUD
Authors:   Associate Professor, Department of Computer Science and Engineering, Tirumala Engineering College, Y Mrs.Swathi
Published: April 2024

Recent advances in wireless networking and big data technologies, such as 5G networks, medical big data analytics, and the Internet of Things, along with recent developments in wearable computing and artificial intelligence, are enabling the development and implementation of innovative diabetes monitoring systems and applications. Due to the life-long and systematic harm suffered by diabetes patients, it is critical to design effective methods for the diagnosis and treatment of diabetes. Based on our comprehensive investigation, this article classifies those methods into Diabetes 1.0 and Diabetes 2.0, which exhibit deficiencies in terms of networking and intelligence. Thus, our goal is to design a sustainable, cost-effective, and intelligent diabetes diagnosis solution with personalized treatment. In this article, we first propose the 5G-Smart Diabetes system, which combines the state-of-the-art technologies such as wearable 2.0, machine learning, and big data to generate comprehensive sensing and analysis for patients suffering from diabetes. Then we present the data sharing mechanism and personalized data analysis model for 5G-Smart Diabetes. Finally, we build a 5G-Smart Diabetes testbed that includes smart clothing, smartphone, and big data clouds. The experimental results show that our system can effectively provide personalized diagnosis and treatment suggestions to patients. Key Words: Monitoring, Diabetes, Wearable, Big Data, Smart Systems, Machine Learning
A STUDY ON PROMOTIONAL STRATEGY AND ITS IMPACT ON CUSTOMERS SATISFACTION TOWARDS E LEARNING AT EDIGLOBE
Authors: A VIGNESH
Published: April 2024

Education is the cornerstone of societal advancement, serving as a catalyst for economic growth, social mobility, and individual empowerment. However, access to quality education has historically been uneven, particularly in emerging markets where challenges such as limited infrastructure, socioeconomic disparities, and cultural barriers impede progress. In recent years, the intersection of education and technology has ushered in a new era of innovation and opportunity, giving rise to a dynamic sector known as Educational Technology, or EdTech. This introduction sets the stage for an exploration of EdTech startups and businesses in emerging markets, tracing their historical evolution, assessing their current impact, and envisioning their future potential. Through targeted advertising campaigns, Edi Globe aims to reach prospective learners across diverse demographics and geographical locations. Additionally, the company offers promotional discounts, free trials, and referral programs to incentivize enrollment and foster customer engagement. Customer Satisfaction in E-Learning: Customer satisfaction in e-learning hinges on several factors including course content quality, platform usability, instructor expertise, and technical support. Edi globe recognizes the significance of these elements and endeavors to provide a seamless learning experience for its users. By regularly updating course materials, enhancing platform functionality, and offering prompt customer support, Edi globe aims to exceed customer expectations and cultivate loyalty. Impact of Promotional Strategy on Customer Satisfaction: The effectiveness of Edi Globe's promotional strategy in bolstering customer satisfaction is evident in several ways. Firstly, promotional discounts and free trials allow prospective learners to experience the platform firsthand, thereby mitigating apprehensions and increasing enrollment rates. Moreover, targeted advertising campaigns ensure that EdiGlobe reaches individuals genuinely interested in e-learning, leading to higher conversion rates and customer satisfaction. Additionally, referral programs not only attract new customers but also foster a sense of community among existing users, thereby enhancing satisfaction and retention.
SMART WIRELESS BATTERY CHARGER WITH CHARGING MONITER
Authors:   Department of  ENTC  Trinity Academy of Engineering Pune, India, Chavan Pooja
Published: April 2024

Smart wearables for health monitoring are new technologies that enable customers to monitor and track their health. However, as the number of sensors in a wearable device grows, along with continuous monitoring and wireless communications, battery lifetime and charge length become a big concern. This issue is especially relevant for health monitoring of workers in hazardous environments such as emergency response responders, mining, and construction. In this research, we describe a wireless charging system for a health monitoring smart-wear application for workers, taking into account the power consumption needs of sensory hardware, communication technology, and realistic charging scenarios. We also show a functional prototype of a 5-watt wireless charging device utilized in our health monitoring wearable. Our paper describes the design, analysis of the needs, and important components of a 'wireless charging bin' that we are currently prototyping for our application.
Design and Development of Four Directional Fire Protection System
Authors:   Department of Mechanical Engineering , KDK College Of Engineering, Nagpur,   Maharashtra India, P.G Dr. . Mehar
Published: April 2024

The creation of an autonomous Four Directional fire protection robot is long overdue. Thanks to the creation of such a device, people and property can be rescued at a much higher rate with only minimal fire damage. It hwas our responsibility as engineers to develop and evaluate a prototype fire detection and suppression system. For this project, we created a solar-powered feature for uninterruptible power supply. mobile robot with wireless control. The robot can move around a model building, find a fire, and then use a water spray to extinguish it. the demand Long overdue is the development of an autonomous fire extinguisher robot. People and property can be saved at a considerably greater rate with only little fire damage thanks to the development of such a device. As engineers, it was our job to create and test a prototype fire detection and suppression system. For uninterruptible power supply, we designed a solar-powered feature for this project. Wirelessly operated mobile robot. The robot is the one that can go through a model building, locate a fire, and then put it out with the aid of a water spray. Keywords: Arduino uno Controller, LCD display, BT Module, Automatic Fire detection, 360° fire protection, Buzzer etc.
CURRICULUM VITAE ANALYSIS USING MACHINE LEARNING ALGORITHM
Authors:   Department of Computer Science and Engineering,Tirumala Engineering College, Anuhya, Moram
Published: April 2024

We describe a system called CV analysis machine, that processes a set of given free-form textual resumes (in English for IT domain), creates a standardized profile for each candidate, and for a given job description, identifies a ranked shortlist of k candidates, along with a matching score for each. The resume scoring function is hand-crafted, hierarchical, and uses domain-knowledge from recruitment experts. We describe a simple neural- network system that automatically learns some weights used in the scoring function, based on feedback about whether the candidate has their grades on each portion and calculate the overall grade of each candidate and find all grades at a time. Companies often receive thousands of resumes for each job posting and employ dedicated screeners to short list qualified applicants. In this paper, we present PROSPECT, a decision support tool to help these screeners shortlist resumes efficiently. Prospect mines resumes to extract salient aspects of candidate profiles like skills, experience in each skill, education details and experience. Extracted information is presented in the form of facets to aid recruiters in the task of screening. We also employ Information Retrieval techniques to rank all applicants for a given job opening. In our experiments we show that extracted information improves our ranking by 30% there by making screening tasks simpler and more efficient. Keywords--e-recruitment,aptitude,personality prediction,credentials,Big five model of Personality
A STUDY ON IMPACT OF ARTIFICIAL INTELLIGENCE ON HR RECRUITMENT PROCESS IN LEADTEQZ
Authors:   Department of Mechanical Engineering , KDK College Of Engineering, Nagpur,   Maharashtra India, B Thirukumaran
Published: April 2024

The emergence of generative AI as a revolutionary force is destined to reshape the landscape of human resource management. This transformative technology is poised to automate mundane tasks, drive operational efficiency, and elevate the overall employee experience within HR recruitment processes. Delving into specific use cases of generative AI within HR contexts, this research aims to spotlight how its integration transcends traditional HR practices, facilitating data-driven decision-making and amplifying the recruitment experience. By automating resume screening, personalizing candidate interactions, and curating tailored training materials, generative AI exemplifies an organizational commitment to leverage transformative technologies, cultivating a sophisticated, data-driven, and candidate-centric recruitment ecosystem. While the integration of generative AI in HR recruitment processes promises a paradigm shift towards efficiency and individualized candidate engagement, it also necessitates addressing potential challenges and ethical considerations. Rigorous validation, monitoring, and transparent communication are essential to ensure a fair, inclusive, and compliant recruitment process. As a comprehensive exploration, this research paper provides a comprehensive blueprint for organizations seeking to harness the transformative potential of generative By embracing AI tools for automation, data analysis, and candidate matching, Leadteqz has experienced efficiency gains, reduced time-to-hire, and improved recruitment decision-making processes. This evolution is crucial for organizations to adapt to future trends, enhance recruitment outcomes, and maintain a competitive edge in the dynamic landscape of talent acquisition.
Threat Detection and Tracking System
Authors:   Department of Electronics and Communication Engineering  Buddha Institute of Technology, Gida, Gorakhpur, Uttar Pradesh, India, Madheshiya Ritik Kumar
Published: April 2024

This device provides a platform to ensure user safety by generating the user’s location and alerting authorities regarding user’s location. In today’s era safety is a matter of prime concern as the crimes are increasing and it has become dangerous to roam freely alone especially in deserted streets and thus, we have designed this project which work on principle of a person’s safety by taking into account their location monitored by fingerprint sensor also there is a GPS module that determines the location coordinates and GSM module that is use to send emergency message in pre-stored contacts. Detection of the input from the user is performed with the help of a fingerprint sensor at every minute, a distress signal is sent to authorized numbers when detection does not occur within time. The GPS track the location of user and send that information to authorized number and GSM is responsible for alerting authorized personnel by sending SMS on the mobile number with the location information of the user. Key word: Threat Detection, Tracking, Safety, GPS module, GSM module, etc.
A Neuro-Driven Mobile Robot Enhanced by LoRa and EEG Technology
Authors:   Assistant Professor, Department of Computer Science and Engineering, Dhanalakshmi Srinivasan Engineering College, Perambalur, K Aswin
Published: April 2024

The Cognitive Rover project presents an imaginative way to deal with portable mechanical technology, coordinating high level neuro-innovations with long-range correspondence capacities to make a flexible and versatile mechanical stage. The essential target of the task is to foster a versatile robot fit for independent route and connection with its current circumstance, directed by brain signals got through electroencephalography (EEG) innovation. Utilizing the LoRa (Long Range) correspondence convention, the robot lays out strong network overstretched distances, empowering far off activity and information transmission in different situations, including search and salvage missions, perilous climate investigation, and far off review errands. The proposed framework design comprises of two fundamental parts: a neuro-driven control framework and a correspondence module. The neuro-driven control framework uses EEG signals gathered from a MindWave headset to decipher client orders and natural boosts. Through AI calculations and continuous sign handling strategies, the framework makes an interpretation of EEG signals into noteworthy orders for independent route, obstruction evasion, and undertaking execution. The correspondence module incorporates LoRa innovation to work with consistent information trade between the robot and far off administrators, empowering continuous observing, control, and video real time over significant distances. Key benefits of the Cognitive Rover incorporate its versatility to assorted conditions, instinctive client association through cerebrum PC interface innovation, and vigorous long-range correspondence abilities. Additionally, the task adds to progressions in mechanical technology, human-robot cooperation, and assistive advancements, with expected applications in misfortune reaction, ecological checking, and foundation review. Key Words: Electroencephalography (EEG), LoRa (Long Range), Cognitive Rover (CR), Brain Computer Interface (BCI), Bluetooth Low Energy (BLE), Transmitter (Tx)
The Influence of social media platforms on the formation and spread of consumer trends
Authors:   Galgotias University, Greater Noida, India, Chauhan Lavish
Published: April 2024

Social media platforms are becoming essential for determining customer trends and behaviour in a variety of businesses. The impact of social media on the development and propagation of consumer trends is examined in this study paper. This article attempts to shed light on the dynamics of contemporary consumerism and its interaction with digital media by investigating the ways in which social media platforms affect consumer behaviour, as well as the function of influencers and algorithms. This study examines the mechanisms via which social media influences consumer behaviour, identifies important elements driving trend development, and evaluates the impact of social media on various industries by utilizing a mixed-methods approach that combines qualitative and quantitative analysis. This study attempts to give a thorough grasp of how social media shapes modern consumer trends through case studies and surveys. This study examines the complex effects of social media on consumer behaviour and trend creation using a mixed-methods methodology. While quantitative research looks at the statistical relevance of these elements in influencing consumption patterns and purchasing decisions, qualitative analysis investigates the impact of social influence, content virality, and user involvement in driving trends. The research's conclusions provide insightful information to companies, marketers, and legislators that want to use social media to better understand and influence customer behaviour. This thesis adds to a better understanding of the modern consumer landscape by thoroughly examining the impact of social media on trend formation and spread. It also offers helpful suggestions for modifying marketing tactics to accommodate changing consumer preferences in the digital era.