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Comparative Study for Optimizing Sales through Media : Big Data Analytics, department of advanced computing, St Joseph's University 1

Comparative Study for Optimizing Sales through Media : Big D...

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1st IEEE International Conference on Advances in Electronics, Communication, computing and Intelligent Information Systems, ICAECIS 2023

作者： Bhadra, Jayati Singh Khanna, Amandeep Rashmi, S. St. Joseph's University Department of Advanced Computing Bangalore India

ISBN: (纸本)9798350348057

Advertisement strategies play a significant role in the market battle, either in the uplifting or downfall of the products or services offered by the company. Media-Mix, with its backbone as the Market mix, can boost the company's financial strategy-making to a greater level. In this research paper, the focus has been to generalize this procedure so that any company, in general, can follow this methodology to decrease their costs on a greater level. Various advertising strategies, KPIs and NPS scores, Stock data, and climate have been considered before jumping into the results to optimize ROI efficiently (Return on Investment), ROAS (return on ad spend), and mROAS (marginal ROAS). The concentration on Ad stock and its carry-over effect has been deeply analyzed. This helps the companies feel confident enough to reach their customers efficiently with the right balance sheet on their hands. © 2023 IEEE.

关键词： Data Analytics

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Comparative Study for Optimizing Sales through Media : Big Data Analytics, department of advanced computing, St Joseph’s University

Comparative Study for Optimizing Sales through Media : Big D...

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Advances in Electronics, Communication, computing and Intelligent Information Systems (ICAECIS), International Conference on

作者： Jayati Bhadra Amandeep Singh Khanna Rashmi S Department of Advanced Computing St. Joseph’s University Bangalore

Advertisement strategies play a significant role in the market battle, either in the uplifting or downfall of the products or services offered by the company. Media-Mix, with its backbone as the Market mix, can boost the company’s financial strategy-making to a greater level. In this research paper, the focus has been to generalize this procedure so that any company, in general, can follow this methodology to decrease their costs on a greater level. Various advertising strategies, KPIs and NPS scores, Stock data, and climate have been considered before jumping into the results to optimize ROI efficiently (Return on Investment), ROAS (return on ad spend), and mROAS (marginal ROAS). The concentration on Ad stock and its carry-over effect has been deeply analyzed. This helps the companies feel confident enough to reach their customers efficiently with the right balance sheet on their hands.

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Analyzing the Attention Heads for Pronoun Disambiguation in Context-aware Machine Translation Models 31

Analyzing the Attention Heads for Pronoun Disambiguation in ...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Mąka, Pawel Semerci, Yusuf Can Scholtes, Jan Spanakis, Gerasimos Department of Advanced Computing Sciences Maastricht University Netherlands

ISBN: (纸本)9798891761964

In this paper, we investigate the role of attention heads in Context-aware Machine Translation models for pronoun disambiguation in the English-to-German and English-to-French language directions. We analyze their influence by both observing and modifying the attention scores corresponding to the plausible relations that could impact a pronoun prediction. Our findings reveal that while some heads do attend the relations of interest, not all of them influence the models' ability to disambiguate pronouns. We show that certain heads are underutilized by the models, suggesting that model performance could be improved if only the heads would attend one of the relations more strongly. Furthermore, we fine-tune the most promising heads and observe the increase in pronoun disambiguation accuracy of up to 5 percentage points which demonstrates that the improvements in performance can be solidified into the models' parameters. © 2025 Association for Computational Linguistics.

关键词： Machine translation

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Signup page using Face recognition authentication

Signup page using Face recognition authentication

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2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Modi, Akash Rayar, Prajwal V Kavya, A.G. Vaishnavi, R. Dravya, B.S. Tamilarisi, R. Alliance School of Advanced Computing Department of Computer Science Bengaluru India

ISBN: (纸本)9798331529833

Improvements in artificial intelligence and machine learning have led to the growing integration of face recognition technology for a wide range of applications, from security systems to social media. Three related uses of facial processing technology are examined in this abstract: face detection, face recognition, and authentication through a face-based signup page. Finding and differentiating human faces in an image frame requires the first step, face detection. It also recognizes the facial features and separates them from other items in the frame using computer vision algorithms. Histogram of Oriented Gradients (HOG), and the Haar Cascade, are the methods used for face recognition. Still, due to failure in extracting facial features in harsh conditions, we use the more recent deep learning models like Convolutional Neural Networks (CNNs) to do this project. Building on detection and face recognition compares the observed faces to known faces in a database to identify or to confirm a person's identity. To assess distinctive facial traits (embeddings) or features and increase recognition accuracy across a variety of circumstances, like varying lighting and angles, modern face recognition systems employ deep learning models, such as Face Net and Deep Face. The Facial Recognition The signup page serves as an example of how face recognition can be used in authentication systems for secure apps. A user registers for this program by having their face captured, and this information is safely kept and acts as a unique identification. The solution gives a hands-free and password-less authentication mechanism during login by comparing the captured face with the stored data in the database. © 2025 IEEE.

关键词： Convolutional neural networks

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Yield Prediction of Rabi Crops in India using XGBoost Regressor 3

Yield Prediction of Rabi Crops in India using XGBoost Regres...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Yashwanth, Y.G. Nanditha, G. Joseph, Saniya Bhadra, Jayati Department of Advanced Computing St. Joseph’s University Bengaluru India

ISBN: (纸本)9798331527549

Agriculture is at the heart of India's economy, with two key cropping seasons: Kharif, grown in the summer, and Rabi, grown in the winter. Rabi crops like wheat, gram, and mustard are cultivated from October to April, particularly in Northwest India. Accurately forecasting the yield of these crops is vital for efficient planning, resource management, and ensuring food security, especially given the unpredictability of climate patterns. In this study, we predict yield based on rainfall and various climatic factors which impact Rabi crop. Using data from different states on crop production and rainfall, we built predictive models to estimate yields. After processing and refining the data, we utilized advanced machine learning methods like Random Forest, XGBoost, and LightGBM to assess how rainfall affects crop performance. Additionally, we improved the accuracy of the Random Forest model through hyperparameter tuning. The models' accuracy was evaluated using metrics like Mean Squared Error (MSE) and R-squared (R2), with cross-validation ensuring consistent results. Of the models tested, Random Forest, XGBoost, and LightGBM performed best, with the latter two offering the most precise predictions. The results show that XGBoost achieved the best accuracy with a maximum of 96% and after hyperparameter tuning showed a better result of 97%. In this paper we highlight how integrating cutting-edge machine learning algorithms can improve the precision of crop yield forecasts, aiding agricultural planning and helping to secure food supplies for the future. © 2025 IEEE.

关键词： Crop Yield Prediction LightGBM Machine Learning Algorithms Precision Agriculture Rabi season Random Forest XGBoost

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Predictive Modeling of Formula 1 Constructor Performance: Discovering the Top Team 3

Predictive Modeling of Formula 1 Constructor Performance: Di...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Vidhya Shree, M. Taniya, S.L. Amal, N. Bhadra, Jayati Department of Advanced Computing St. Joseph’s University Bengaluru India

ISBN: (纸本)9798331527549

Formula One motor-sport is a sport that attracts millions of people worldwide, and performance is influenced by a synergy of drivers, constructors, and engines. The identification of the best-performing constructors is complex because of these interdependencies. While existing studies have focused on isolated factors such as driver performance or engine metrics, they lack a holistic model that integrates these multiple dimensions. This research fills the gap by creating a holistic framework that evaluates driver performance, engine efficiency, and constructor dynamics over time. We utilize machine learning models such as Random Forest, Decision Tree, Logistic Regression, and LLaMA to predict the top five constructors in Formula 1 races. Of these, the Random Forest model had a higher accuracy level for the ranking of constructors, and hence the output could be of more practical value to stakeholders. This research work, therefore, constitutes an important milestone in further developing the predictive analysis of performance for Formula 1 using various factors that describe performance and advanced machine learning methods. © 2025 IEEE.

关键词： Random forests

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Stock Price Prediction of Nifty 50 in India Using LSTM and ARIMA During Black Swan Events

Stock Price Prediction of Nifty 50 in India Using LSTM and A...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Dsouza, Laranya Ankitha, M.G. Bhogle, Srinivas St. Joseph's University Department of Advanced Computing Karnataka Bengaluru India

ISBN: (纸本)9798331509828

Stock price prediction plays a crucial role in financial analysis, particularly in emerging markets like India. The Nifty 50 index, representing the top 50 companies listed on the National Stock Exchange, serves as a benchmark for the Indian equity market. Two prominent methodologies for stock price prediction: Long Short-Term Memory (LSTM) networks and Autoregressive Integrated Moving Average (ARIMA) models. Furthermore, the performance of these models is analyzed during Black Swan events - unexpected and significant market disruptions that can lead to drastic price movements. The objective is to find the optimal method for financial forecasting by evaluating which model yields the most accurate predictions for actual stock data. © 2025 IEEE.

关键词： Prediction models

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Improvising Language Conversation - Hindi & Urdu

Improvising Language Conversation - Hindi & Urdu

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2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Agarwal, Sanskriti Livingstone Arthur Raj, M. Dutta, S. St. Joseph's University Department of Advanced Computing Karnataka Bengaluru India

ISBN: (纸本)9798331529833

In a diverse country such as India, natural language processing (NLP) plays an even greater role in resolving the complexities of languages. In this article, we discuss Urdu terminology used by Hindi literature and explain the problem for readers that are not familiar with these phrases. We devised an automated manner to replace Urdu-based words in Hindi sentences with synonymous terms used commonly in conversational or literary Hindi. This study supports current attempts at standardization and simplification that have been underway for Hindi, encouraging linguistic diversification in sectors like the media or education. While there are challenges to solve for, such as handling homonyms and a fixed list of synonyms available, we empirically show strong improvements towards more inclusive language. Overall, the research presents a starting point for future multilingual application-based work and highlights that NLP holds exciting promise to support reliable communication processes within diverse linguistic domains. © 2025 IEEE.

关键词： Natural language processing systems

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Evolution Analysis of Network Attack and Defense Situation Based on Game Theory

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Computers, Materials & Continua 2025年第4期83卷 1475-1494页

作者： Haiyan Sun Chenglong Shao Jianwei Zhang Kun Wang Wanwei Huang College of Software Engineering Zhengzhou University of Light IndustryZhengzhou450007China Intelligent Computing Power Research Department Zhengzhou Xinda Advanced Technology Research InstituteZhengzhou450007China

To address the problem that existing studies lack analysis of the relationship between attack-defense game behaviors and situation evolution from the game perspective after constructing an attack-defense model,this paper proposes a network attack-defense game model(ADGM).Firstly,based on the assumption of incomplete information between the two sides of the game,the ADGM model is established,and methods of payoff quantification,equilibrium solution,and determination of strategy confrontation results are ***,drawing on infectious disease dynamics,the network attack-defense situation is defined based on the density of nodes in various security states,and the transition paths of network node security states are ***,the network zero-day virus attack-defense behaviors are analyzed,and comparative experiments on the attack-defense evolution trends under the scenarios of different strategy combinations,interference methods,and initial numbers are conducted using the NetLogo simulation *** experimental results indicate that this model can effectively analyze the evolution of the macro-level network attack-defense situation from the micro-level attack-defense *** instance,in the strategy selection experiment,when the attack success rate decreases from 0.49 to 0.29,the network destruction rate drops by 11.3%,in the active defense experiment,when the interference coefficient is reduced from 1 to 0.7,the network destruction rate decreases by 7%,and in the initial node number experiment,when the number of initially infected nodes increases from 10 to 30,the network destruction rate rises by 3%.

关键词： Network attack-defense situation evolution zero-day virus NetLogo

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Detection of Brain Tumor using Transfer Learning using Conventional Autoencoder with Long Short Term Memory Method 6

Detection of Brain Tumor using Transfer Learning using Conve...

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6th International Conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Agalya, D. Kamalakkannan, S. Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India

ISBN: (纸本)9798331522667

Brain tumor (BT) has generate a significant health challenge by putting pressure on healthy brain parts or spreading into other areas as well as blocking the flow of fluid around the brain. BT diagnosis is an extensive and time consumption process that depends primarily on radiologists experience and interpretive capabilities. For the area of medical imaging, automatic segmentation of images by means of Deep Learning (DL) approaches. The DL approaches assist in transforming the sector, resulted in improving precision and effectiveness of investigations. However, there are various feature extraction mechanisms available. Therefore, the Recurrent Neural Network (RNN) is utilized for feature extraction and image classification. In model execution, the data have trained in transforming the test image as well as data features for minimizing the domain shift is calculated through the Convolutional Autoencoder (CAE) for reconstruction loss. This research has concentrated in building a model with VGG16 as a single test that subjected at inference and existing method is adopted as neural networks for AE as Transfer Learning (TL) that performs an image analysis task such as segmentation and even set as an adopter for pre training the model. The AE used to train from the source dataset and perform as the adaptors in optimizing during testing using a test subject for effective computation. Moreover, the Long Short Term Memory (LSTM) is utilized as RNN model with CAE for providing improved detection of BT in healthcare industries. Hence, the proposed CAE with LSTM is compared with AE with Convolutional Neural Network (CNN) for evaluating BT detection using MRI dataset with various BT type classifications. © 2025 IEEE.

关键词： Image segmentation

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