Article isn't published yet.

Once Upon A Brand : Crafting Connections through Storytelling in Modern Marketing

Publication Date : 27/05/2024


Author(s) :

Sukriti Verma.


Volume/Issue :
Volume 10
,
Issue 5
(05 - 2024)



Abstract :

The power of “Once upon a time” is as powerful as ever in the field of marketing. By weaving creative and compelling narratives that resonate with the audience on a relatable level, brands can build lasting relationships with their consumers. In a world filled with millions of advertisements, the immemorial art of storytelling emerges as a light of authenticity and connection. This research paper dives into the transformative role of storytelling in modern marketing and crafting valuable connections. The primary purpose of this research is to analyze the mechanisms through which storytelling enhances brand perception and loyalty. This paper aims to uncover the elements that make brand stories compelling and the impact these stories have on consumer behavior. Additionally, this research paper seeks to provide various insights for marketers on integrating storytelling into their strategies effectively. The findings of this paper reveal that storytelling in marketing significantly boosts brand engagement and loyalty as well as does its part in crafting connections between the sellers and the buyers. Personalization, video storytelling, interactive features and user-generated content are storytelling techniques that drive sales and increase customer engagement.


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0


NEXT-GEN NETWORK ATTACK DETECTION WITH MACHINE LEARNING AND DEEP LEARNING TECHNIQUES

Publication Date : 05/06/2024


Author(s) :

Dr.M.Deepa, M.P.Venkat Vijay, S. SriRanjani , V. Sowmiya, V. Ramya.


Volume/Issue :
Volume 10
,
Issue 6
(06 - 2024)



Abstract :

Systems for detecting network intrusions that are based on anomalies are very important. This research proposes robust machine learning and deep learning models for classifying different forms of network intrusions and attacks. The 49-feature UNSW-NB15 dataset has been used in experiments by suggested models for nine distinct assault samples. Among the ensemble models, the Decision Tree classifier yielded the highest accuracy of 99.05%, followed by Random forest (98.96%), Adaboost (97.87%), and XGBoost (98.08%).The K-Nearest Neighbour classifier was trained for a range of K values, with K=7 yielding the best results and an accuracy of 95.58%. For binary classification, a Deep Learning model with two dense layers activated by ReLU and a third dense layer activated by Sigmoid was created. It yielded good accuracy of 98.44% when used with the ADAM optimizer and an 80:20 Train-Test Split Ratio. XGBoost detects network attack exploits with 95% accuracy, Random Forest detects fuzzer attacks with 90% accuracy, Random Forest detects generic assaults with 99% accuracy, and Decision Trees detects reconnaissance attacks with 79% accuracy. Detecting network attacks requires no feature selection because all features are powerful and important.


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0


Artificial Intelligence (Ai) And Authorship: Who Owns Ai Generated Creations?

Publication Date : 05/06/2024


Author(s) :

ISHAANVI MAVI.


Volume/Issue :
Volume 10
,
Issue 6
(06 - 2024)



Abstract :

Artificial intelligence (AI) is rapidly transforming creative landscapes, with machines now capable of producing original works of art, music, and literature. However, this innovation presents a significant legal challenge: who owns the copyright of these AI-generated creations? Current copyright law is built on the concept of human authorship, leaving a gaping hole when applied to AI. This paper delves into this legal uncertainty, exploring the applicability of existing copyright frameworks to AI authorship. By analyzing landmark cases and the evolving role of AI in the creative process, this research aims to identify potential solutions and pave the way for a future where both human and machine creativity are fostered within a clear legal framework.


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0


Article isn't published yet.

Empowering Deep Learning for Images: A Comparative Analysis of Regularization Techniques in CNNs

Publication Date : 18/05/2024


Author(s) :

Sultan Khaibar Safi.


Volume/Issue :
Volume 10
,
Issue 5
(05 - 2024)



Abstract :

- The remarkable success of Convolutional Neural Networks (CNNs) in image recognition and related tasks has been hampered by the ever-present challenge of overfitting and the pursuit of robust generalization performance. This article meticulously dissects and compares various regularization techniques specifically designed to empower deep learning for image tasks within the context of CNN architectures. We embark on a rigorous exploration of fundamental techniques like L1 and L2 regularization, delving into their theoretical foundations. We further unveil the intricacies of advanced methods such as Dropout, Data Augmentation, Early Stopping, and the synergistic approaches of Elastic Net and Group Lasso regularization. Through a meticulous examination, we unveil the theoretical underpinnings of these techniques, illuminate effective strategies for hyperparameter selection, and elucidate their profound impact on model complexity, weight sparsity, and ultimately, the network's ability to generalize effectively. To empirically validate these insights and solidify our comparative analysis, we conduct controlled experiments utilizing benchmark image datasets. This empirical validation process sheds light on the efficacy of each technique. By meticulously analyzing the trade-offs inherent in these diverse regularization approaches and their suitability for specific image data characteristics and CNN architectures, this article empowers researchers with a comprehensive understanding of these techniques. Armed with this knowledge, researchers can make informed decisions to optimize performance in deep learning tasks involving images, ultimately propelling the field towards ever-greater advancements.


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0


Law Of Environment In India: Problems And Challenges In Its Enforcement

Publication Date : 06/05/2024


Author(s) :

PRASHANT SEHRAWAT.


Volume/Issue :
Volume 10
,
Issue 5
(05 - 2024)



Abstract :

While India boasts a plethora of environmental protection legislation, their enforcement has been notably lacking. There is a pressing need for the effective implementation of these laws, as mandated by the Constitution and other environmental statutes. The judiciary, alongside the National Green Tribunal (NGT), has played a commendable role in addressing this issue through creative and innovative interventions. Public Interest Litigations filed in the Supreme Court against industries and Pollution Control Boards have highlighted the necessity for stringent pollution control measures. To ensure the proper execution of these laws, it is imperative to establish adjudicatory bodies comprising both legal and technical experts in each state. Environmental stewardship is not solely the responsibility of the government; it is a collective duty encompassing individuals, associations,societies, industries, and corporations. Upholding environmental integrity aligns with the nation's sustainable development goals and is ingrained as a fundamental duty in Article 51-A(g) of the Indian Constitution.


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0


DESIGN AND DEVELOPMENT OF 3D PRINTED ROBOTIC ARM FOR PAINTING APPLICATION

Publication Date : 19/04/2024


Author(s) :

Prof. Sunil Madhukar Mahajan, 2 Ghrushnesh Aniruddha Bhandari, 3 Rushabh Vijay Ghatborikar, 4 Saurabh Pandey, 5 Prathamesh Balasaheb Aher, .


Volume/Issue :
Volume 10
,
Issue 4
(04 - 2024)



Abstract :

Abstract: In the current scenario machines and robots are playing an important role in the automation industry. This paper is presenting the process through which 3D Printed robotic arm is made with the help of Arduino and Potentiometer for controlling and coordinating the industrial processes. Here, we realize that the 3D Printed robotic arm has the ability to move in four directions with the help of servo motors. The movement of servo motor, UNO is responsible which converts the analog signal into the digital signal which is further received by servo motor. This project discuss about the technical imputation, the issue related with the implications and application of 3D Printed robotic arm in the field of automation of industries. It can be used as a artificial arm for a human who have lost his hand in any accident.


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0


A Study on Financial Statement Analysis with reference to GVK Power & Infrastructure Pvt Ltd

Publication Date : 23/05/2024


Author(s) :

Mr. Fasi Ur Rehman , Ms. Kasoju Lahari .


Volume/Issue :
Volume 10
,
Issue 5
(05 - 2024)



Abstract :

This study aims to analyse GVK Power & Infrastructure Pvt Ltd's financial statements in order to provide light on the company's previous performance, stability, and future prospects. Management, investors, and stakeholders all rely on financial statement analysis to assess the company's financial health, identify trends, and make well-informed decisions. The financial statements covering a certain time period will be reviewed for this analysis. Many financial metrics and statistics, including the company's liquidity, profitability, solvency, and efficiency, may be found in these statements. The study also uses industry standards and comparative analysis to put GVK Power & Infrastructure's financial performance in context. In order to have a better understanding of the firm's SWOT (strengths, weaknesses, opportunities, and threats) in its competitive market environment, this comparison analysis might be useful. Additionally, the research explores the factors that influence GVK Power & Infrastructure's financial performance, such as market dynamics, regulatory environment, operational efficiency, and strategic objectives. Understanding these factors is critical for analysing the company's financial risks and opportunities.


No. of Downloads :

0


Real-time Evaluation of Descriptive Answer Using NLP and Machine Learning

Publication Date : 01/06/2023


Author(s) :

Chetan Bapu Sonawane.


Volume/Issue :
Volume 10
,
Issue 4
(06 - 2023)



Abstract :

The process of evaluating descriptive answers to gauge student performance poses significant challenges. Typically, this assessment is conducted manually, which can introduce subjective biases. The outcome may be influenced by factors such as the authority's nature, mood, and the student-teacher relationship. Furthermore, manual analysis is time-consuming and demanding. To address these issues, this proposed system aims to automate the analysis process by leveraging technologies such as Machine Learning, Natural Language Processing (NLP), and Deep Linguistic Analysis (DLA). By employing these techniques, we can extract pertinent information from textual answers and measure the similarities between the extracted summaries and the correct answers.


No. of Downloads :

12