An organic branding is one of the most unique processes to build a brand. Most of the companies are trying to build their brand using paid branding through some well-known platforms like Google, Facebook, etc. Sometimes this method may work, but it may not have a far-reaching effect. Creating some hype is easy with paid branding. Creating mass-trust is not possible with paid branding. Moreover, it is expensive. Every company want a lot of organic traffic. The high volume of traffic will increase the orders for a company. For example, search for any online services such as software service providing website or e-commerce online stores or job boards or any other goods, the main revenue growth depends on the amount of traffic routed to their website.

https://www.researchgate.net/publication/382955327_Product_Colour_Variation_Management_with_Artificial_Intelligencehttps://journals.e-palli.com/home/index.php/ajet/article/view/3213This research focuses on the topic of using AI in color variant management in products to enhance the appeal and performance of the products in the marketplace by incorporating artificial intelligence, deep learning, and neural network systems. Real-time consumer and product information, preferences, buying history, and sales history; I created an AI model to predict and change product colors in real-time. The complete workflow used comprises data gathering, processing, and feature extraction, model training, integration of the color adjustment software tools, and finally, testing and validation. The efficiency of such AI-driven interventions was analyzed through the consumer satisfaction indices, the sales results, and the engagement data based on the consumption of digital platforms. This study demonstrates valuable potential of AI to improve product design application and development while providing valuable suggestions for Businesses adapting and improving market outcomes according to the changing consumer trends. Such an application of AI implements a new best practice in ways of enhancing futuristic consumer-oriented marketing approaches. The paper was first completed in 2021 and later I have modified the paper with latest updates till date 2024.

https://journals.e-palli.com/home/index.php/ajet/article/view/3212/1687This article discusses the use of classification of web visitors and determination of branding quality using AI to redesign digital marketing. AI in its various forms, such as machine learning, natural language processing, and computer vision, helps businesses to better understand their users’ behavior, better understand the context of supplied content, and improve user experience. The application of AI in the management of websites offers features such as Real-time monitoring, automated content tuning, and Analytics for predictions. Automated tools can analyze who is visiting the site, what kind of work they are doing in terms of SEO, and how to assist with creating high-quality content. Also, AI helps with mass and targeted promotions like recommended products and services, variable rates of prices, etc. AI can help unlock significant benefits for businesses, across the board and lead to enhanced engagement in the digital space and thus gives a competitive advantage in the market. The article also discusses investment in training and education for consumers to ensure them remain relevant with emerging technologies in AI and the market. By way of illustration and analysis, the article clearly outlines how the incorporation of AI in organizational functions can bring about operational efficiencies and cost savings, as well as result in remarkable improvements in branding and marketing strategies. The article was first completed in 2021 and later I have modified the article with latest updates till date 2024.  https://www.researchgate.net/publication/383177336_Website_Visitor_Analysis_Branding_Quality_Measurement_Using_Artificial_Intelligence

     How the power of machine -machine learning, data science and NLP can be used to prevent spoofing and reduce financial risksDOI: 10.30574/gjeta.2024.20.2.0149Sasibhushan Rao Chanthatihttps://gjeta.com/content/how-power-machine-%E2%80%93-machine-learning-data-science-and-nlp-can-be-used-prevent-spoofing-andhttps://gjeta.com/sites/default/files/GJETA-2024-0149.pdf Abstract:  This paper discusses the potential of machine learning, data science, and natural language processing (NLP) in mitigating the incidence of spoofing and financial risks hinged on cyber threats. Another one is spoofing; it is the act of impersonating legitimate entities to gain unauthorized information, and it is indeed a threat to the public and companies to some extent. The research introduces two primary methodologies to combat spoofing: an email filtering system using a machine learning algorithm and an encryption and decryption system using a Caesar Cipher and Python programming language. It distinguishes between approved domains and unapproved domains by using machine learning and successfully filters out phishing emails from reaching the intended clients. This study also illustrates how to conduct email domain verification using MongoDB Atlas, which a database is containing approved vendors’ domains, to reduce spoofing. Specifically, incorporating NLP helps the system analyze raw data to categorize it and identify patterns potentially leading to a spoofing attempt, enhancing the spoofing detection and prevention of the system. The paper also presents arguments that require awareness and integration of new technologies in the security frameworks. Hence, incorporating machine learning, data science, and NLP presents robust, versatile, and cost-effective solutions to enhance cybersecurity and ultimately protect vital information and organizations’ monetary loss due to cybercrimes. The paper was first completed in 2021 and later I modified the article with latest updates till date 2024. Keywords: Machine Learning; NLP; Financial Risks; Python programming; MongoDB Atlas; Spoofing; Cyber Security

Second Version on A Centralized Approach to Reducing Burnouts in the IT industry Using Work Pattern Monitoring Using Artificial Intelligence using MongoDB Atlas and Pythonhttps://www.researchgate.net/publication/381879374_Second_Version_on_A_Centralized_Approach_to_Reducing_Burnouts_in_the_IT_industry_Using_Work_Pattern_Monitoring_Using_Artificial_Intelligence_using_MongoDB_Atlas_and_PythonApril 2021DOI: 10.13140/RG.2.2.12232.74249https://rgdoi.net/10.13140/RG.2.2.12232.74249 Chanthati, Sasibhushan Rao. (2021). Second Version on A Centralized Approach to Reducing Burnouts in the IT industry Using Work Pattern Monitoring Using Artificial Intelligence using MongoDB Atlas and Python. 10.13140/RG.2.2.12232.74249. Industry burnout is interlinked with cultural, individual, physical, or emotional exhaustion, and social factors, the resolution of which requires the technology-driven trends in the workplace and the technologies such as work pattern monitoring and Artificial Intelligence that can deal with large amounts of data. Industries face a gigantic problem i.e., employee burnout which can charge a firm loss in numerous hours and thousands of dollars every year. The more advanced companies use work pattern monitoring using Artificial Intelligence to make their employees work more professionally. In this research my attempts to understand the development and leadership, on the effects of work pattern monitoring using Artificial Intelligence technology on information technology organizations (Sasibhushan Rao Chanthati, 2022). In this updated second version, the data of the employees will be stored on a cloud server with governance & compliances. The study discussed the development of methods which are configured as two different system interfaces, which are of minimum valuable product (MVP) and the results obtained from the two approaches. The system will provide work pattern monitoring via the ‘Real-Time Database – MongoDB Atlas’ which will synchronize the employee burnout data to improve the employee experience. This research also illustrates the advantages and disadvantages of the proposed solutions. “Burnout Detection Mechanism” that will help Industry management and Human Resource Management to manage the emotional state of the employees, understanding their real state. The study conducted a self-survey, and the outputs of the surveys are explained in this paper. The sample data we are using is mainly focused on information technology employment perception. https://scholar.google.com/citations?view_op=view_citation&hl=en&user=t6JwIkoAAAAJ&citation_for_view=t6JwIkoAAAAJ:_FxGoFyzp5QC&gmla=AETOMgFnWqgvCSaJPZ5dvcz6rBRTF88XAvCJSomyBsmMlXdq338DJRDh6Cm6wuiph8d6n4Sj8LLEOPZUL8IAEbmR