bluuebunny/crossref_metadata_2025 huggingface dataset:
From Pencil to Pixel: The Evolution of Animation Techniques in Cinema
Authors:   Amity University Uttar Pradesh, India, Saul, Nathan
Published: April 2024

This study delves into the rich history of animation, tracing its evolution from its early beginnings to its current status as a significant form of entertainment and artistic expression. Through an interdisciplinary approach, the study explores key milestones, influential figures, and technological advancements that have shaped the trajectory of animation over the years. By analysing notable works and examining their cultural impact, this research aims to provide insights into the transformative power of animation in shaping popular culture and fostering creativity worldwide.
TPO AND STUDENT CAMPUS DRIVE MANAGEMENT
Authors:   Shatabdi Institute of Engineering & Research At Post Agaskhind, Raut Pratik
Published: April 2024

The availability of information and the facility for the user to take action on the information collected have been revolutionized by the use of the Internet and the World Wide Web. The placement process can be managed using the internet which arises a need to develop a web-based placement management system specifically by the recruiters and the software engineers that can be used as a Recruitment system (Online TnP portal). This system can be used as an application for both candidates and recruiters. Advanced features for recruiters are available as they can shortlist candidates for further rounds according to their requirements on the basis of the probability obtained. The current recruitment system recruiters do not possess candidate information apart from his/her CV. This proposed system aims to analyze the candidate performance and recommend candidates fittest for the job using Random Forest Regressor algorithm that will help to maximize the placement probability of candidates easing the recruiter’s task. Random Forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. This system will provide ease and efficiency in recruitment process. Key Words: Blockchain, KYC,IPFS,DLT
The Musk Effect – How Personal branding Drives Success
Authors:   Marketing Professional, K Faiza Aafreen
Published: April 2024

Entrepreneurship has grown significantly in the last several years and is expected to continue increasing tremendously in the next ten years in the ever-growing world of technological disruptions and innovations. However, how can an entrepreneur differentiate themselves from the millions of others? This is the context in which personal branding is useful. Entrepreneurs can increase consumer trust and grow their enterprises rapidly in terms of profitability and brand equity by developing a strong personal brand. Using Elon Musk’s personal branding as a case study, this secondary research paper uses qualitative research methods to examine the significance of personal branding for entrepreneurs. The objective of this study is to provide entrepreneurs with an insightful knowledge of personal branding and how to use it to further the development, expansion of their businesses as well as create credibility among their customers and peers. Key Words: Branding, Personal Branding, Entrepreneurs, Elon Musk, Credibility.
A DATASET OF FIELD PLANT IMAGES FOR PLANT DISEASE DETECTION AND CLASSIFICATION WITH DEEP LEARNING
Authors:   Computer Science and Engineering  Tirumala Engineering College, Narasaraopeta, Krishana Dr.N.Gopal
Published: April 2024

The United Nations Food and Agriculture Organization proposes a 70% increase in food production by 2050 to meet global population demands, recognizing that one-third of all food is lost due to plant diseases. To address this challenge, researchers have developed deep learning models to aid farmers in detecting crop illnesses. While these models are typically trained on databases like Plant Village or Plant Doc, which contain laboratory photos with consistent backgrounds, they perform poorly when applied to field images with complex backgrounds and multiple leaves per image. To remedy this, a new dataset called Field Plant has been introduced, comprising 5,170 plant disease images collected directly from plantations and manually annotated by pathologists. This dataset focuses on diseases affecting crops like corn, cassava, and tomato in tropical regions. Evaluation of state-of-the-art models on Field Plant shows superior performance compared to other datasets. Additionally, recommendations on crop cultivation are made based on factors such as soil quality, weather conditions, humidity, and rainfall, aimed at increasing agricultural output. This system benefits farmers, nations, and helps to stabilize food costs. Keywords - Boosting food production,Deep learning models,Plant diseases,Field Plant dataset,Soil, weather, humidity, rainfall,Agricultural output.
Real Time Object Detection System Using Yolov
Authors:   Department of Computer Science Engineering  Dayananda Sagar College of Engineering, Bangalore, M.S , Nidhishree
Published: April 2024

Improving object detection models in computer vision is a crucial area of focus. Object detection represents a more sophisticated approach to image classification, where a neural network identifies objects within an image and delineates them using bounding boxes. YOLO (You Only Look Once) revolutionized object detection by introducing an end-to-end neural network architecture that simultaneously predicts bounding boxes and class probabilities. Unlike earlier methods that adapted classifiers for detection, YOLO's approach streamlines the process by integrating classification and localization tasks, accurately delineating objects with bounding boxes. The project mainly focuses on augmenting YOLOv4, a state-of-the-art object detection architecture, to address the limitations like small object detection, adaptive lighting handling, occlusion challenges, detecting objects at different angles, and specialized object features. Additionally, we aim to improve the model's ability to recognize specialized object features for superior classification. Furthermore, by incorporating functionalities like license plate recognition using Tesseract OCR, object counting (total and class-wise), and detection cropping, this project extends the model's applicability to real-world scenarios. These functionalities make the model valuable in areas like autonomous vehicles, traffic monitoring, and industrial automation. The augmented model's improvements aim to overcome drawbacks identified in existing research, enhancing its efficacy in various scenarios.
Handwritten Signature Verification System Using User-dependent Approach: A Comparative Study
Authors:   Chandigarh University, Sharma, Manan
Published: April 2024

Handwritten signature verification is a critical aspect of personal authentication in numerous sectors, including banking, legal, and access control systems. This paper provides a comprehensive investigation into writer-dependent signature verification techniques, spanning from traditional approaches such as Histogram of Oriented Gradients (HOG) and Scale-Invariant Feature Transform (SIFT) to cutting-edge deep learning architectures like Siamese and triplet networks. Through extensive experimentation and meticulous analysis conducted on benchmark datasets, we rigorously compare and evaluate the efficacy of these approaches. Our study reveals intriguing insights into the performance characteristics of different methodologies, shedding light on their strengths, weaknesses, and areas for improvement. Notably, we observe that while traditional feature-based methods excel in capturing discriminative information from signature images, deep learning architectures offer enhanced flexibility and adaptability, particularly in scenarios with large and diverse datasets. By combining the strengths of both paradigms, we demonstrate the potential for building robust and reliable signature verification systems capable of handling real-world challenges and variations. We highlight the importance of carefully curated datasets and well-designed network architectures in achieving optimal results, thereby providing valuable guidelines for practitioners and researchers in the field. In conclusion, this study contributes significant insights to the advancement of handwritten signature verification systems, emphasizing the importance of leveraging both traditional methodologies and modern deep learning techniques for building secure and dependable authentication mechanisms. By addressing key challenges and exploring novel approaches, our research aims to foster innovation and enhance the security measures associated with handwritten signature authentication in diverse applications. Keywords—Handwritten signature verification, Writer-dependent approach, Feature-based methods, Deep learning, Siamese networks, Triplet networks, Benchmark datasets, Performance evaluation, Authentication mechanisms, Security measures.
Beyond Wiretapping: The Power and Perils of Capturing Network Communication
Authors:   Apex Institude of Technology  Chandigarh University  Gharuan, Punjab, Kundu Vinit
Published: April 2024

The rise of wireless networks has made life easier and more mobile, but it has also made it easier for people to secretly listen in on conversations, known as "tapping." This study looks at how tapping into wireless networks is done, showing how it can be risky. By closely examining how wireless tapping works, particularly through techniques like monitor mode or promiscuous mode in network interface cards (NICs), we explore how sensitive information can be revealed and put at risk. Additionally, we look into why data leaks might happen, studying the detailed flow of network activity using tools like Wireshark and TCP dump analysis. We aim to identify vulnerabilities that could lead to harmful activities like unauthorized access, data theft, or disruptions to network services. In summary, this research aims to emphasize the risks associated with wireless network tapping and provide advice on how to prevent it. By understanding the tactics employed by attackers, both organizations and individuals can implement measures to safeguard their wireless communication and decrease the likelihood of unauthorized access to critical data. Keywords— tapping, sniffing, intercept, network security.
Python Expense Tracker Application
Authors:   Trinity Academy of Engineering, Pune, Tummod Shiva
Published: April 2024

It's a Python Expense Tracker that helps you keep track of your savings and spending. Plus, it alerts you if you go over your set limit. An expense tracker is like a digital notebook that helps you keep track of all your money transactions. It also gives you reports about your financial activities. Nowadays, life is very busy and people want to make more money, so they rush around a lot. But sometimes, they forget to keep track of their money properly. That's where an expense tracker comes in handy. It's a great tool that lets you manage and keep track of your expenses easily. Daily based expense tracking guides us saving more money. That system makes you to be in control and win your intention. Keywords: Python, Flask, Docker, 8GB RAM, OS- Windows/Linux/MAC, Laptop/Desktop
FATIGUE DETECTION OF DRIVERS USING MACHINE LEARNING
Authors:   Department of Computer Science and Engineering  Tirumala Engineering College, NAIK, Dr.R.LALU
Published: April 2024

In this project, the aim is to develop a prototype drowsiness detection system to address driver fatigue, a major cause of accidents worldwide. The system detects driver drowsiness by monitoring eye blinks and yawning. If the driver's eyes remain closed for an extended period or if yawning is detected, an alarm is sounded. Programming for this system is conducted in OpenCV, utilizing the Haar cascade library for face detection and other machine learning libraries to detect facial features. The system operates in real-time and is nonintrusive, prioritizing driver safety without being obtrusive. By effectively monitoring driver alertness and issuing timely warnings, this system contributes significantly to enhancing road safety. Keywords - Driver fatigue, drowsiness detection, eye blink, yawn detection, OpenCV, Haar cascade, machine learning, facial features.
A Blockchain-Powered E-Voting System with SHA-256 Encryption
Authors:   “Department of Computer science and Engineering”  Sipna college of Engineering and Technology, Amravati, Maharashtra, Landge Mr. Sarang
Published: April 2024

E-voting systems hold promise for revolutionizing the electoral process by offering efficiency, accessibility, and transparency. However, concerns about security and integrity have hindered their widespread adoption. In this research paper, we propose a blockchain-based e-voting system leveraging the SHA-256 algorithm to address these challenges. Our system utilizes blockchain technology to create a tamper-proof and immutable ledger of votes, ensuring transparency and accountability throughout the voting process. The SHA-256 algorithm is employed to secure data integrity, providing robust protection against unauthorized modifications. This paper presents the system architecture, implementation details, and security analysis, demonstrating its resistance to tampering and vulnerabilities. Furthermore, we evaluate the system's performance and usability, highlighting its potential to enhance the electoral process. By leveraging blockchain technology and the SHA-256 algorithm, our e-voting system offers a secure, transparent, and efficient platform for conducting elections, fostering trust and confidence among voters. Keywords: Blockchain-based e-voting system, SHA-256 Algorithm
Impact of Social Media Marketing on Consumer Behaviour
Authors:   Galgotias University, Uttar Pradesh – 203201, Mishra Divyanshu
Published: April 2024

The behavior of consumers is intricate, sophisticated, hard to modify, and occasionally confusing. Learning about consumer behavior is an interesting but important field of research. because of its complexity, diversity, unpredictable nature, and ubiquity. One of the most significant facets of consumer behavior and a hot topic for consumer science research is consumer engagement and purchase intention. Scholars and marketers have been trying to understand how consumers behave in a range of social, cultural, and other contexts. Understanding the various mental and physical actions that the customer takes prior to, during, and following the purchase is the main focus. The virtual world has emerged as a result of the introduction, development, and use of information and communication technologies by the general public and corporate organizations. Using the web, a system that uses the internet to facilitate business communications and transactions, new forms of value creation have been created in this digital age, dismantling traditional business models (Kalakota and Robinson, 2000). The demand for social media has increased due to developments in digital services and infrastructure brought about by government regulatory frameworks and other initiatives, as well as the availability of low-cost, high-speed data, appropriate content, and mobile apps. Additionally, mobile devices have become more affordable and sophisticated (Duhan and Singh, 2013). In addition, social media has given users new and innovative virtual platforms to interact, communicate, socialize, discover new information, and stay up to date on concepts, ideas, and products that may be of interest to them when making decisions about what to buy. Consumer decision-making has changed in the social web era due to the introduction of new influences such as brand community discussions and online brand endorsements (Constantinides and Fountain, 2008; Edelman, 2010). Social networks assist consumers in making decisions and sharing information (Milovic, 2018). In recent times, there has been an unparalleled surge in consumer interactions due to the availability of various forms of user-generated content such as text, photographs, and videos, as well as the chance to interact with friends, make new ones, and discover emerging trends (Giannakos et al., 2013). Mostly, people use these kinds of media to browse through information available on online forums and stay up to date on the latest developments in companies and goods.
Solar Powered Car Optimization Through Machine Learning
Authors:   UG student, Dept. of Electronics and Computers Engineering, Sreenidhi Institute of Science and Technologies, Telangana, India, Jashwanth Bolleddu
Published: April 2024

The main goal of the project is to predict the solar power output of the solar panel depending on prevailing weather conditions using some Machine Learning algorithms for the purpose of optimizing and adapting the power consumption so as to strike a good balance between solar power supply and demand. For predicting the power output, the following parameters are measured with the help of an Arduino-based sensor unit -Temperature, Pressure, Light intensity and Relative humidity. Apart from them as well, cloud coverage, visibility, and dew point, and we add wind speed is also taken into consideration which we collect from the weather forecast that help a lot. In order in an attempt to train the Machine Learning algorithms, a dataset using the above parameters will be formed hopefully. As renewable energy is gaining a lot of ground rapidly, it becomes necessary for us interestingly to make solar systems reliable kind of. By predicting the power output of the panel we can plan a Necessary alternative accordingly as well, making the system more reliable indubitably. Solar energy prediction is significant in enhancing the competitiveness of solar power plants in the energy market indeed, and decreasing reliance on fossil fuels in socio-economic development largely. Our work is aiming to accurately predict the solar energy. IndexTerms–MachineLearning, SolarPower, Arduino Uno, PythonJupyterNotebook, Sensors.
Online Voting System using Blockchain Technology
Authors:   Department of Information Science and Engineering,  Jain (Deemed-to-be University)  Bengaluru, India,, Chaudhary Pushpraj
Published: April 2024

Voting stands as a cornerstone of democracy in any nation, empowering citizens to select future leaders and express their views within their communities. It fosters an understanding of civic duty and citizenship significance. Online voting systems, digital platforms designed to conduct elections securely, streamline the voting process by eliminating traditional paper ballots and in-person gatherings. These systems safeguard the integrity of each vote by preventing multiple submissions. Contrasting paper-based methods, electronic voting, or e-voting, offers inherent advantages such as heightened efficiency and reduced errors. Such systems promote broader participation by enabling individuals to vote from any location with internet access. Blockchain technology, an emerging decentralized innovation with robust cryptographic underpinnings, holds promise for enhancing various industries. Integrating e-voting with blockchain could address existing e-voting concerns. Here, we propose a blockchain-based voting system to curb fraud and enhance the simplicity, security, and efficiency of the voting process. Keywords: Blockchain, Ethereum, Smart-contracts, Online-voting, Privacy, Trust, Security
EYE DISEASE RECOGNITION SYSTEM
Authors:   Information Technology, VPPCOE, Mumbai University, India ,, Katkar Aniruddha
Published: April 2024

This paper presents an innovative system for detecting eye diseases utilizing advanced machine learning techniques. Given the increasing prevalence of eye disorders, early detection and intervention are of utmost importance. The proposed system integrates a diverse dataset comprising medical images and patient information. Deep learning algorithms are employed to extract intricate features from the dataset. These features are then input into a predictive model, facilitating accurate identification of potential eye diseases. Rigorous testing and validation demonstrate the system's performance and its ability to provide reliable predictions. The early diagnosis enabled by this system has the potential to significantly impact patient outcomes and contribute to the advancement of ophthalmic healthcare. The Eye Disease Detection System serves as a valuable tool for the early detection and management of various eye conditions. Through the integration of advanced technologies such as machine learning and medical imaging, this system enhances the accuracy and efficiency of the diagnostic process. Index Terms : Vision disorders, Glaucoma, Macular degeneration, Eye diseases, Ophthalmology, Corneal diseases.
AUTOMATED PNEUMONIA IDENTIFICATION THROUGH CNN-BASED ANALYSIS
Authors:   ECE, RVRJCCE, INDIA, S Aadarsh
Published: April 2024

Pneumonia, a condition caused by infectious agents such as bacteria, viruses, fungi, or parasites, results in lung infections and the accumulation of pus in affected tissues. Incorrect diagnosis or improper treatment can significantly impact a patient's quality of life. This project focuses on the precise identification and categorization of pneumonia-afflicted patients through the analysis of their chest X-rays. Leveraging advancements in deep learning, healthcare experts can make more accurate diagnostic decisions for various illnesses. This study presents an innovative approach utilizing Convolutional Neural Networks (CNN) to predict and distinguish between patients affected by pneumonia and those who are not, based on chest X-ray images. The methodology involves a series of convolutional and max pooling layers activated using the ReLU activation function. Subsequently, the processed data is passed through dense layers, culminating in the activation of the output neuron through the sigmoidal function. During the training process, the model exhibits increasing accuracy and decreasing loss, demonstrating its effectiveness. To prevent overfitting, data augmentation techniques are applied before model fitting. These strategies collectively enhance the robustness and reliability of the deep learning models, resulting in accurate and persuasive detection of pneumonia from chest X-ray images. Keywords: Pneumonia, CNN, VGG-16, VGG-19, Chest X-rays, Classification, Deeep learning, Data augmenatation, ReLU, softmax.
ASSESSMENT & PRIORITIZATION OF THIRD-PARTY LOGISTICS
Authors:   Bachelor of Business Administration  In Logistics And Supply Chain Management  School of Business  Galgotias University, G . Arshia
Published: April 2024

This research investigates the risks Third-party logistics go through. Through a comprehensive literature review, we identified different types of risk manual handling, machinery operation, transportation activities, and warehouse working conditions emerge as prominent areas of concern. By preparing a questionnaire and doing a survey (ratings) from 5 people from third-party logistics. Failure Mode And Effect Analysis (FMEA) we evaluate the results. Results show that Capacity constraints and Supply chain risk disruptions are extreme risk levels and failure of contract and Lack of warehousing tools and techniques are low-level risks. By enhancing awareness and providing practical strategies for risk assessment and prioritization, this research contributes to the advancement of practices within the 3PL sector, ultimately promoting safer working environments for employees and stakeholders alike. KEYWORDS Third-party logistics (3PL), Risk assessment, FMEA (failure mode and effect analysis), Risk Mitigation
PROFIT EFFICIENCY OF INDIAN BANKS: POST-INTERNET ERA
Authors:   Department Of Economics  Madras Christian College  Chennai – 600059, G Ms. Hannah Jebamalar
Published: April 2024

The present study is mainly concerned with the analysis of profitability factors that influence the performance of banks after the introduction of internet banking. This study examines the trends in important banking indicators for 10 years period from 2013 to 2022. The study actually concentrates more on NPA and taken into consideration of profit efficiency variables in relation to internet banking of scheduled commercial banks. Therefore, this study focus on the detailed analysis of profit efficiency by incorporating all variable while keeping NPA’s ratios as central for study. The study have concluded the decline in NPA (Non-Performing Assets). Further, Reserves & Surplus is also analysed intensively to know the future prospects of scheduled commercial banks. Keywords: Profitability, Non-Performing Assets, Scheduled Commercial Bank and Internet Banking
Noise Robust VAD Technique and its Performance Evaluation
Authors:   Department of Electronics and Communication Engineering Vidyavardhaka College of Engineering Mysuru, India, A S ABHISHEK
Published: April 2024

In this real world of audio and speech signals, the ability to detect the voice activity and remove the background voice is important. This project presents that to remove noise robust Voice Activity Detection (VAD) technique that combines voice source and vocal tract system information using a zero-frequency filtering (ZFF) approach. As mentioned earlier ZFF technique is employed to combine and to compute a composite signal that encapsulate essential parameters such as fundamental frequency and formats, enabling robust VAD in time domain.This methodology offers a significant advantage in terms of computational efficiency compared to other methods, making it an attractive choice for real-time applications. By applying dynamic thresholding after spectral entropy-based weighting, this approach exhibits resilience or minimum across a range of Signal-to-Noise Ratios (SNRs), further enhancing its ability.This project provides comprehensive utility for audio segmentation, which offers the capability to process individual audio files or entire directories. It also includes the extraction of a composite signal, which is a critical component in enhancing voice quality and reducing background noise. With this, the project is open to use build further and use it for the big industries purpose and for communication purpose. Keywords— Voice Activity Detection (VAD), Zero-Frequency Filtering (ZFF), Signal Noise Ratio (SNR).
Krypt Wallet Cryptocurrency Payment Interface
Authors:   Department of Information Technology Vidyalankar Institute of Technology Mumbai, Maharashtra, India, More Vrushabh
Published: April 2024

This paper explores how KRYPT uses smart contracts and the Ethereum Blockchain to facilitate transfers of Ethereum in a novel way. Through the use of blockchain technology, KRYPT guarantees asset transactions that are safe, transparent, and effective. By using private keys and encryption, users may benefit from increased security, which allays worries about theft or hacking. The removal of middlemen also simplifies the transfer procedure, and real-time tracking guarantees safe and timely delivery of payments. By giving users on the Ethereum Blockchain more security, effectiveness, and control, KRYPT is a major development in Ethereum transactions. Keywords— Blockchain, Smart-Contract, Ethereum, EVM, ERC20 Token, Ethereum Blockchain.
3PL Fire and Safety Hazard Assessment and Mitigation
Authors:   MASTER OF BUSINESS ADMINISTRATION  School of Business  Galgotias University, kumar Sanjeev
Published: April 2024

3PL facilities, handling high volumes of goods, face unique fire and safety risks. This abstract explores assessing and mitigating these hazards. Common fire hazards include flammable materials, electrical issues, and improper storage. Safety hazards encompass slips, falls, and misuse of machinery. Assessment techniques involve inspections, reviewing safety data sheets, and analyzing incidents. Mitigation strategies focus on fire safety plans, proper storage, electrical safety, and employee training. The abstract emphasizes preventative maintenance and a culture of safety for a safer 3PL environment.
A STUDY ON DIGITAL MARKETING OF FLIPKART
Authors: RAO ARYAN
Published: April 2024

Efficient digital marketing tactics are crucial for companies to succeed in the ever-changing world of e-commerce. Flipkart is one of the most prominent online marketplaces in India, and this study examines their digital marketing strategies. Examining Flipkart's strategy across different digital channels and how they affect the company's brand visibility, consumer engagement, and sales is the main objective of this research. In order to better understand how Flipkart draws in and keeps customers, as well as how to increase engagement and conversion rates, this study analyzes the company's website design and user experience. In addition, it delves into Flipkart's SEO tactics, namely its attempts to rise in the SERPs and target certain keywords. Content marketing campaigns run by Flipkart, such as blog articles, product descriptions, videos, and social media postings, are examined in the research. It delves into the ways Flipkart uses content to captivate its audience and boost sales. The research also examines Flipkart's approach to social media marketing, looking closely at the company's content and interaction tactics across sites like Facebook, Instagram, and Twitter. This study delves into Flipkart's email marketing initiatives, specifically looking at their subscriber segmentation tactics and the content of the emails they send out. Also covered is an examination of Flipkart's paid advertising campaigns, including display, search engine, and social media ads, as well as an analysis of the company's channel allocation strategies. In this case study, we take a look at how Flipkart uses mobile marketing and influencer marketing to boost engagement and sales through its app and its partnerships with influential users. It also checks Flipkart's analytics, which include how well the company uses data to monitor the efficacy of its digital advertising activities. Keywords: Flipkart, digital marketing, e-commerce, online marketplace, SEO, content marketing, social media marketing, email marketing, paid advertising, influencer marketing, mobile marketing, analytics, user experience, customer engagement, sales optimization, India.
EFFECT OF ELECTRODE ON JOINING TWO DISSIMILAR METALS (SS & MS
Authors:   Assistant professor, Department of Mechanical Engineering, GNIT, Hyderabad, Telangana, P Varalakshmi
Published: April 2024

This study examines the challenges associated with joining dissimilar metals, specifically stainless steel and mild steel, and investigates the impact of electrode selection on joint quality and characteristics. Various welding processes, including shielded metal arc welding (SMAW), is explored. Experimental findings indicate that electrode choice significantly influences the formation of intermetallic compounds, fusion zone morphology, and mechanical strength of the joint. Alloying elements in electrodes are found to enhance weldability and performance by facilitating metallurgical bonding and reducing the formation of brittle phases. Furthermore, the study explores variations in welding parameters to optimize the process for desirable joint properties. By deepening our understanding of electrode roles in welding dissimilar metals, this research offers insights crucial for selecting appropriate techniques and parameters, ensuring reliable joints. These insights have broad applicability across industries such as automotive, aerospace, construction, and manufacturing, where dissimilar metal joining is common. Key Words: dissimilar metals, intermetallic compounds, Weldability, morphology, mechanical strength.
IMPORTANCE OF ARTIFICIAL INTELLIGENCE IN EDUCATION
Authors:   Galgotias University, Greater Noida, India, NAGAR NITESH
Published: April 2024

A growing number of individuals are coming to the realization that the incorporation of Artificial Intelligence (AI) into educational frameworks has a significant impact on the learning environment of this notion. Explores the many uses and possible advantages of AI in the educational setting. The ability of AI to personalize the way information is presented to learners is one of its strongest points. Artificial intelligence (AI) encourages a personalized learning strategy that takes into consideration different learning styles and paces via the use of data analytics and adaptive algorithms. As a result, student engagement and academic achievement are enhanced. As a cognitive enhancement tool, artificial intelligence (AI) can also assist educators in creating and disseminating interesting information for diverse student populations. The use of AI in the classroom has the potential to usher in a new era of student engagement and knowledge exchange through the development of dynamic, collaborative learning spaces. With the support of AI-driven tools, educators have the potential to cultivate classrooms that are more interactive and cooperative. Traditional boundaries of the classroom. It is unclear how AI systems will affect cultural norms, standards, and the student-teacher interaction, despite the fact that AI has many positive uses. Students' happiness and success are greatly affected by online learning. In the learner-instructor relationship, which is defined by engagement, presence, support, and communication. If we want to know what kinds of problems are holding AI systems back from reaching their full potential and putting kids' and instructors' safety at risk, we need to know how people see the effects of these systems on their interactions. Having said that, we must carefully examine the ethical issues that emerge from the widespread use of AI in the classroom. Concerns about privacy, algorithmic biases, and the digital divide need our full attention. While AI is changing the educational landscape in profound ways, this abstract offers a more nuanced view of the subject. Since AI is a catalyst for innovation, researchers, teachers, and policymakers should carefully consider how to incorporate it to ensure future inclusive, equitable, and morally acceptable education. Keywords:- AI , Education,
What Is the Impact of ChatGPT on Education?
Authors:   Department of Computer Applications  Chandigarh School of Business  Chandigarh Group of Colleges, Jhanjeri, Mohali, India, NAGAR Lavisha
Published: April 2024

n artificial intelligence-based chatbot, it is invented by open AI it is one of the fascinating and amazing application and that is free of cost any one can access this; people have to just login your details. It is launched in November 30 2022. It is based on the GPT-3 language model, which is trained on a massive dataset of text and code. ChatGPT can generate human-like text in response to a wide range of prompts and questions, such as writing poems, answering questions, and creating stories. It is beginning in 2018 This AI is informative and comprehensive. its work like a human body . Although human design this chatgpt but still its power is more than a manual men. It generate the significant information it gives human like responses in which any one can input and it generates the output . How this chatGpt can use in education, what kinds of issues arises in initials. (i.e., December 2022 to February 2023).I search of the relevant databases and Google Scholar yielded 50 articles for content analysis (i.e., open coding, axial coding, and selective coding). ChatGPT has been used for a variety of purposes, including customer service, education, and entertainment. It has also been used to generate creative content, such as poems, stories, and scripts. ChatGPT is still under development, but it has the potential to revolutionize the way we interact with computers. The service is available 24x7 it helps in their assignments , academic exams and many more .
AMPLIFYING AQUATIC VISUALS
Authors:   MGIT, kumari, CH.Lakshmi
Published: April 2024

Our proposed fusion-based approach aims to combat the degradation of underwater images, addressing issues such as diminished colors and indistinguishable objects. Leveraging contrast stretching and Auto White Balance, the technique significantly improves contrast and color, offering a straightforward yet effective solution to enhance visibility in aquatic images, crucial for applications like video surveillance in outdoor computer vision systems. This straightforward approach plays a pivotal role in enhancing image visibility, contributing significantly to applications like video surveillance and other outdoor computer vision systems. Our dehazing process builds upon two essential statistical observations related to haze-free images and haze. By applying dark channel prior and guided filter to the decomposed image, we effectively estimate atmosphere light, resulting in a dehazed output. This method tackles the intricacies of haze, contributing to the generation of high-quality images with improved details and clarity. Beyond image enhancement, our approach showcases versatility by extending into object classification, demonstrating the broader impact and potential of our method in the realm of outdoor computer vision systems.
Investigating the impact of employee empowerment and autonomy on job satisfaction and organizational commitment Shubhangi Roy
Authors:   MBA Dual Specialization  MASTER OF BUSINESS ADMINISTRATION  School of Business  Galgotias University, Roy Shubhangi
Published: April 2024

The potential of employee empowerment and autonomy to improve job satisfaction and organizational commitment has drawn attention from scholars and practitioners alike, leading to a focus on these topics in current organizational management literature. To methodically investigate the relationship between employee empowerment, autonomy, work happiness, and organizational commitment, this meta-analysis synthesizes findings from previous empirical investigations. This study aims to offer insights into the degree to which giving employees autonomy and empowerment affects their job satisfaction and commitment to the company through an extensive analysis of pertinent literature.The meta-analysis incorporates a broad spectrum of research that spans different industries, organizational sizes, and geographic regions. With the use of rigorous statistical methods such as moderator analyses and effect size calculations, this study seeks to clarify the complex dynamics that underlie the relationship between work satisfaction, employee autonomy, empowerment, and organizational commitment. To further understand their influence on the observed connections, potential modifiers including personality differences, leadership styles, and corporate culture will be investigated. It is anticipated that the results of this meta-analysis will have significant ramifications for theory and practice. This research aims to provide a thorough knowledge of the mechanisms via which employee autonomy and empowerment affect work satisfaction and organizational commitment by synthesizing empirical evidence from several studies. These perceptions have the potential to promote employee well-being and organizational effectiveness by informing management strategies and policies that strive to develop a more dedicated and engaged workforce. The results indicate that a substantial correlation exists between increased levels of job satisfaction and organizational commitment and employee empowerment, which is defined as the transfer of authority, discretion in decision-making, and access to resources. Similarly, autonomy—that is, giving workers the freedom and flexibility to carry out duties and make decisions—correlates favourably with organizational commitment and job satisfaction. Moreover, the examination pinpoints mediating factors including job engagement, psychological empowerment, and perceived control that elucidate the connections between employee outcomes, autonomy, and empowerment. The significance of cultivating a culture of trust, communication, and support for employee empowerment initiatives is one of the implications for organizational practice. Supervisors must acknowledge the significance of autonomy in augmenting worker motivation, contentment, and dedication, while simultaneously taking into account the situational elements that impact the efficacy of employee empowerment tactics. upcoming
Predicting Air Quality
Authors:   Computer Science Engineering  Chandigarh University,  Mohali, India, Kumar Abhishek
Published: April 2024

A list for revealing air quality is known as the air quality file (AQI). It measures the short-term effects of air pollution on a person's health. The motivation behind the AQI is to instruct general society on the negative well-being impacts of neighbourhood air contamination. How much air contamination in Indian urban communities has altogether expanded? The air quality index can be calculated mathematically in several different ways. Various examinations have found a connection between air contamination openness and unfavourable well-being influences in the populace. Data mining is one of the most intriguing methods for predicting and analysing AQI. This paper aims to determine the most efficient means by which AQI prediction can aid in climate control. The best technique can be enhanced to track down the most ideal arrangement. As a result, the work that is done in this paper requires extensive research as well as the addition of novel methods to guarantee that the best solution to the issue of poor air quality will be found. Another significant objective is to exhibit and show the specific measurements engaged with our work so that it is instructive and clever and subsequently gives legitimate examinations and helps future scientists. Keywords – Air Quality Index (AQI), Air pollution, Short-term effects, Health impacts, AQI prediction, Climate control, Novel methods, Extensive research, Comparative analysis, Future research.
Phishing Email Detection using Machine Learning
Authors:   Assistant Professor, Computer Science and Engineering, Raghu Engineering College, Visakhapatnam, Kumar A V Santhosh
Published: April 2024

The increasing sophistication of phishing attacks poses a significant threat to cybersecurity, demanding advanced and adaptive detection mechanisms. This project focuses on the development and implementation of a phishing detection system leveraging machine learning algorithms. The objective is to enhance the security measures for users by employing a robust and intelligent system capable of identifying and thwarting phishing attempts. The project involves the collection and curation of diverse datasets containing examples of phishing and legitimate communication. Supervised learning techniques will be employed to train the machine learning model, utilizing features such as email content, sender information, and website attributes. The model's training will be continuously refined to adapt to emerging phishing tactics and enhance its accuracy. Additionally, unsupervised learning methods will be explored to detect novel phishing patterns and anomalies that may not be present in the training dataset. Clustering algorithms will assist in grouping similar instances, aiding the identification of previously unseen phishing techniques. The implementation will be evaluated through extensive testing using real-world datasets, measuring the system's effectiveness in detecting phishing attempts with a low false-positive rate. The project aims to contribute to the field of cybersecurity by providing a proactive and adaptive solution to counter the evolving nature of phishing threats. The successful implementation of this phishing detection system will enhance user confidence in online interactions and contribute to the overall resilience of digital ecosystems. Key Words: Phishing attacks, Cybersecurity, Detection mechanisms, Machine learning algorithms, Supervised learning, Unsupervised learning, Email content analysis, Sender information, Website attributes, Training dataset, Real-world datasets, False-positive rate, Proactive cybersecurity, Adaptive solutions, Digital ecosystems.
Emergency Vehicle Detection and Traffic Prevention Using Open CV
Authors:   Assistant Professor, Computer Science and Engineering, Raghu Engineering College, Visakhapatnam, G .Bhargavi
Published: April 2024

The proposed project focuses on the development of an innovative system for "Emergency Vehicle Detection and Traffic Prevention using OpenCV." The project aims to enhance road safety and expedite emergency response by leveraging computer vision techniques implemented through the OpenCV framework. The primary objective is to design a robust algorithm that can accurately identify emergency vehicles in real-time based on distinct visual features such as color, shape, and motion patterns. OpenCV, a powerful open-source computer vision library, will serve as the foundation for image processing and analysis. Upon successful detection of an emergency vehicle, the system will employ dynamic traffic prevention measures. This includes the activation of intelligent traffic signal control to ensure the smooth and rapid passage of the emergency vehicle through intersections. The system may also interface with connected infrastructure, such as smart traffic lights, to optimize overall traffic flow and minimize delays. The project aims to contribute to public safety and emergency response efficiency by providing a reliable and automated solution for prioritizing emergency vehicles on the road. Through the integration of OpenCV, the system will offer a versatile and scalable approach to emergency vehicle detection and traffic prevention, making it a valuable contribution to the field of intelligent transportation systems. Index terms Emergency Vehicle Detection, Traffic Prevention, OpenCV, Computer Vision Techniques, Real-time Vehicle Identification, Intelligent Traffic Signal Control, Dynamic Traffic Management, Road Safety Enhancement, Emergency Response Efficiency, Public Safety, Automated Traffic Prioritization, Intelligent Transportation Systems, Visual Feature Analysis, Smart Traffic Lights Integration, Automated Traffic Flow Optimization
CNN BASED REALTIME AIRCRAFT DETECTION
Authors:   Department of Computer Science & Engineering, Raghu Engineering College, Visakhapatnam., N Seetayya
Published: April 2024

Deep learning techniques such as Convolutional Neural Networks (CNN) and Transfer Learning are being used to detect and identify fighter aircraft or jets in a dataset consisting of 21 different aircraft with 20,000 images. The principle of "pooling" in Convolutional Neural Networks (CNN) involves progressively reducing the spatial size of the model to decrease the number of parameters and computations in the network. These techniques have been applied to various aspects of aircraft recognition, including object detection and engine defect detection. Convolutional Neural Networks (CNNs) are widely employed in various domains, including defence, agriculture, business, and face recognition technology, for image detection tasks. Transfer learning is a machine learning method that involves using a pre-trained model as the initial point for a new task, allowing for faster training and improved performance. This technique is particularly useful in deep learning, where large amounts of data are required for training complex models. The dataset is processed using Python libraries such as pandas, seaborn, and sci-kit-learn to find pre-trained patterns and insights. The data is then split into training and testing datasets, with 80% and 20% of the total data, respectively. A model is built using the TensorFlow library for CNN, and the metric used is "accuracy". Additionally, a transfer learning model is built to compare the accuracy results and adopt the best-fitting one. Keywords: Convolutional Neural Network, Deep Learning, Artificial Neural Network, Support Vector Machines.