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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...

引用

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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A 2-level cascaded deep learning with the adaptive novel feature extraction approach for sales forecasting framework

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International Journal of Computers and Applications 2025年第4期47卷 373-387页

作者： Selvaraj, Sivabalan Minu, R.I. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur India

This study presents a revolutionary deep-learning architecture that focuses on feature extraction, feature selection, and sales forecasting. The technique begins with a pre-processing step using median imputation and min–max. To extract features, the study uses the Word2Vec (CBOW) model to construct contextual word embeddings and TF-IDF to assess term relevance in texts. For numerical data, the Pearson Correlation Coefficient (PCC) detects linear associations, whereas the Adaptive Bhattacharyya Correlation refines characteristics using statistical metrics. The Tent Chaotic-based Puma Optimizer Algorithm (TC-POA) is used to pick important characteristics for the model effectively. The forecasting challenge employs the 2-level Cascaded Deep-Learning (2-LCDL) model. In this model, an Alexnet with Squeeze-and-Excitation is used along with the Bi-LSTM to capture sequential data dependencies and GRU for efficient long-term memory management. The work wraps up with a fully linked layer and SoftMax activation for sales prediction. This framework demonstrates substantial advances in deep-learning approaches, providing better-predicted accuracy and computational efficiency. The proposed model is evaluated for both 70% and 80% of training data for the metrics MSE of 0.0058, 0.0054, MAPE of 1.1968, 0.9979 and also the RMSE, G-Mean, MAE, MMAE, MNB, IoA have superior performance when compared to the other existing methods. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Sales

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Utilizing enhanced deep learning methods for residential burglary detection via analysis of human behavior patterns

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Multimedia Tools and Applications 2025年第10期84卷 7627-7646页

作者： Pavithra, S. Muruganantham, B. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

Residential burglary is a severe crime that affects millions of residents each year. It is critical to analyze patterns of human behavior in surveillance video data and discover suspicious actions to avoid and deter this crime. However, the complexity and diversity of human behavior make this undertaking challenging. In this research, we provide a unique system that uses advanced deep learning algorithms to detect abnormal actions that signal burglar attempts at time intervals. We use the state-of-the-art object identification method YOLOv7 to detect an object and its trajectories in video data. We additionally extract the time intervals between the object's movements and utilize them as a categorization feature. Based on the COCO dataset and three videos of distinct scenarios: abnormal activity, burglary attempt, and false alarm, we evaluate our system with earlier versions of YOLO and additional detection methods. On the COCO dataset, our method outperforms all other models with a precision of 93.9%, an f1 score of 94.42%, a recall of 94.71, and an accuracy of 95%. We also show that our algorithm can effectively discriminate between suspicious and normal behaviors based on the motion region's length, width, energy, and orientation variance, achieving an accuracy of 92% on average over three videos. Our system can provide practical domestic protection by automatically detecting and preventing burglary attempts and violent scenarios. Our method can also contribute to the advancement of knowledge in the fields of human behavior analysis and object recognition. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Crime

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AI-Enhanced Threat Intelligence in Remote Patient Monitoring Systems: A Survey on Recent Advances, Challenges and Future Research Directions

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IEEE Access 2025年 13卷 106465-106488页

作者： Trivedi, Jolly Tahir, Mohammad Isoaho, Jouni University of Turku Department of Computing Turku20540 Finland

The adoption of IoT devices for Remote Patient Monitoring (RPM) systems has emerged as a new attack surface and has resulted in an increased risk of cyber-attacks. Besides the traditional security mechanism for RPM systems, Artificial Intelligence (AI), has emerged as a crucial technology to address the security issues in IoT-enabled RPM systems. In this regard, this article provides a survey of the security challenges of RPM systems and discusses the role of AI in strengthening the security of RPM systems through advanced threat intelligence capabilities. The survey analyses 110 research papers from leading databases related to AI, the security of RPM systems, anomaly detection, architectural solutions, and existing RPMs. This review of AI-enhanced Threat Intelligence in RPM systems highlights the research gaps, which include the necessity of comprehensive end-to-end architectures for maintaining security and privacy in the RPM systems. The survey reveals traditional RPM systems are vulnerable to data breaches due to their centralized architecture. AI-powered threat intelligence enhances RPM security significantly by identifying anomalies in patient data, enabling continuous monitoring and early detection of threats. Federated Learning allows for decentralized training of AI models, providing both security and privacy. However, challenges like the explainability of AI models persist, which need continued innovation. This survey paper suggests integrating AI Enhanced Threat Detection as a Service (TDaas) that implements Federated Learning (FL) to transform the existing RPM security system and ultimately contribute to a secure and reliable Threat Detection system in Healthcare. This literature review lays out a roadmap for future research in the area of AI-driven threat intelligence security for RPM systems and offers insights for developing resilient healthcare infrastructures. © 2013 IEEE.

关键词： Remote patient monitoring

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Enhancing hurdles athletes’ performance analysis: A comparative study of cnn-based pose estimation frameworks

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Multimedia Tools and Applications 2025年 1-19页

作者： Jafarzadeh, Pouya Zelioli, Luca Virjonen, Petra Farahnakian, Fahimeh Nevalainen, Paavo Heikkonen, Jukka Department of Computing University of Turku Turku20014 Finland

Human pose estimation has gained significant attention in recent years for its potential to revolutionize athletic performance analysis, enhance understanding of player interactions, and optimize training regimes. Deep learning models, particularly Convolutional Neural Networks (CNNs), have outperformed traditional methods in pose estimation tasks. This study addresses a gap in sports analytics by applying two popular CNN-based frameworks, YOLO and DeepLabCut, to analyze pose estimation in hurdles athletes. Videos of a single female athlete during training sessions were used, and frames were manually annotated to capture three critical foot landmarks: ankle, heel, and big toe. The results highlight YOLOv8l’s superior accuracy, achieving a Percentage of Correct Keypoints (PCK) of 79% for these landmarks, while demonstrating the feasibility of a low-cost setup for practical applications. Visual comparisons further validate the model’s effectiveness in real-world scenarios. Additionally, YOLO predictions were utilized to analyze step progression in the time domain, providing actionable insights into athletic movement. This study underscores that even modest video equipment, combined with CNN-based methods, can equip coaches with powerful tools to analyze and optimize movements and techniques, paving the way for data-driven advancements in sports performance. © The Author(s) 2025.

关键词： Convolutional neural networks

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Transformer-Aided Deep Double Dueling Spatial-Temporal Q-Network for Spatial Crowdsourcing Analysis

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 893-909页

作者： Yu Li Mingxiao Li Dongyang Ou Junjie Guo Fangyuan Pan Department of Computing Hangzhou Dianzi UniversityHangzhou310018China

With the rapid development ofmobile Internet,spatial crowdsourcing has becomemore andmore *** crowdsourcing consists of many different types of applications,such as spatial crowd-sensing *** terms of spatial crowd-sensing,it collects and analyzes traffic sensing data from clients like vehicles and traffic lights to construct intelligent traffic prediction *** collecting sensing data,spatial crowdsourcing also includes spatial delivery services like DiDi and *** task assignment and worker selection dominate the service quality for spatial crowdsourcing *** research conducted task assignments via traditional matching approaches or using simple network ***,advanced mining methods are lacking to explore the relationship between workers,task publishers,and the spatio-temporal attributes in ***,in this paper,we propose a Deep Double Dueling Spatial-temporal Q Network(D3SQN)to adaptively learn the spatialtemporal relationship between task,task publishers,and workers in a dynamic environment to achieve optimal ***,D3SQNis revised through reinforcement learning by adding a spatial-temporal transformer that can estimate the expected state values and action advantages so as to improve the accuracy of task *** experiments are conducted over real data collected fromDiDi and ELM,and the simulation results verify the effectiveness of our proposed models.

关键词： Historical behavior analysis spatial crowdsourcing deep double dueling Q-networks

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Develop a novel, faster mask region-based convolutional neural network model with leave-one-subject-out to predict freezing of gait abnormalities of Parkinson’s disease

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Neural computing and Applications 2025年第7期37卷 5441-5457页

作者： Ezhilarasi, J. Senthil Kumar, T. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chengalpattu603203 India

A common symptom of severe Parkinson’s disease (PD) is Freezing of Gait (FoG), a gait disorder that causes sudden difficulty in initiating or maintaining walking. FoG frequently leads to falls and has a detrimental impact on a patient’s regular life. Real-time detection algorithms identify FoG occurrences using wearable sensors. Anticipating FoG in advance allows for pre-emptive cueing, which may prevent the episodes or reduce their severity and duration. This research proposes a Faster Mask Region-based Convolutional Neural Network (FMRCNN) signal processing approach for FoG identification. The model captured gyroscope, magnetometer, and tri-axial accelerometer signals using an inertial measurement device on the left side of the abdomen. The experimental results demonstrate a reduction in the equal error rate to 1.9% in the Leave-One-Subject-Out (LOSO) Cross-Validation (CV) with Long Short Term Memory (LSTM) assessment. Additionally, the tenfold CV evaluation enhances specificity and sensitivity by 0.045 and 0.017, respectively, compared to previous best results. It takes only 0.52 ms to detect a 256-data section. The proposed work uses the LOSO-CV-LSTM to evaluate various machine learning (ML) and deep learning (DL) techniques for FoG detection. The proposed system not only detects FoG but also enhances the automation of PD detection and therapy at an earlier stage. The results demonstrate that the proposed system improves performance measures compared to existing systems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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Precision Agriculture System Using Big Data Analytics in Predicting Yield by Controlling Plant Disease and Irrigation Scheduling 1. ***, Research Scholar, department of Data Science and Business System, School of computing, Srm Institute of Science and Technology,Ktr

SSRN

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SSRN 2022年

作者： Vasudevan, N. Karthick, T. Department of Data Science and Business System School of Computing SRM Institute of Science and Technology Chennai India

The agricultural sector is one of India's most important and major endeavors, and it is also critical to the country's economic development. Agriculture is one of the most important things that contributes to humanity's pleasure and welfare, as well as serving as a deterministic element in a nation's economic well-being. Smart agriculture, or the application of information technology in agriculture, attempts to provide precise disease management, irrigation and yield prediction in crop production. We have implemented Big Data Analytics and Internet of Things in the agricultural field, which is nothing but Precision Agriculture. This will extensively impact on the crop yield positively. Precision agriculture is utilizing IoT and Big Data to enhance crop quality, therefore increasing total output rates. In this paper we have implemented the N stage – CNN (Convolutional Neural Networks) using the Smart Agriculture dataset, using parameters such as temperature, humidity, rainfall, pH and solar irradiance. In this paper, irrigation scheduling is performed using machine learning techniques and plant disease detection using DoubleGAN method. As we have taken into consideration almost all required parameters for plant growth with irrigation scheduling and plant disease detection, the yield prediction is precise. The principle aim of this work is to come up with N stage CNN model that will increase the yield irrespective of the type of crop and thereby largely contributing to the agricultural sector, which will in turn increase food production in the coming years. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Communication with factorized policy gradients in multi-agent deep reinforcement learning

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Neural computing and Applications 2025年 1-24页

作者： Zhu, Changxi Dastani, Mehdi Wang, Shihan Department of Information and Computing Sciences Utrecht University Utrecht Netherlands

In multi-agent deep reinforcement learning (MADRL), agents can learn to communicate to broaden their view and understanding of the environment and their teammates. Previous works on communication in MADRL mainly rely on centralized or independent value functions for learning communication, which cannot differentiate how communicating agents individually contribute to the overall learning process. Moreover, continuous environments that incorporate continuous state/action spaces have received limited attention in previous research. In this paper, we propose a novel architecture for communicating agents and apply centralized but factorized value functions to differentiate how each agent contributes to learning during communication, along with gradient backpropagation. Additionally, to address the complexity introduced by communication, we investigate the use of an attention mechanism that aggregates messages, enabling policies to maintain a fixed input length. We then present a new policy gradient method termed communication with factorized policy gradients (CFPG), featuring full backpropagation from factorized value functions to communicating agents’ architecture. We demonstrate that CFPG can enhance performance and accelerate learning in continuous predator–prey scenarios and multi-agent MuJoCo, when compared to other learning communication methods. © The Author(s) 2025.

关键词： Signaling

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