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Deep Learning for Enhanced Consumer Behavior Analysis and Predictive Accuracy

Deep Learning for Enhanced Consumer Behavior Analysis and Pr...

引用

2024 International Conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Bharti, Jigyasa Dongre, Snehlata G H Raisoni College of Engineering Dept. of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350319019

The accelerating digital transformation in retail necessitates advanced analytical approaches to effectively comprehend and predict consumer behavior. Existing methodologies in retail data mining often fall short in handling the complexity and heterogeneity of data, resulting in suboptimal predictive accuracy and operational efficiency. This work addresses these limitations by introducing a novel deep learning framework that synergizes the capabilities of Graph Neural Networks (GNNs) with Q Learning and VARMAx models. The proposed model adept.y fuses data from diverse sources such as Twitter, Amazon, Facebook, and Flipkart, creating a comprehensive analysis platform. By leveraging the relational data structure processing power of GNNs and the dynamic decision-making proficiency of Q Learning, along with the time series predictive strength of VARMAx models, this approach offers a more holistic understanding of consumer trends. The efficacy of this model is demonstrated through extensive testing on varied data samples encompassing social media, e-commerce platforms, and tweets. The results are compelling: an 8.3% increase in accuracy, 8.5% enhancement in precision, 6.5% improvement in recall, and a significant 10.4% reduction in delay compared to existing classification methods. The impact of this work is far-reaching, offering retailers a robust tool for strategic decision-making, enhanced customer insight, and a competitive edge in the rapidly evolving digital marketplace. This approach not only propels the retail industry forward but also sets a new benchmark in the application of deep learning for consumer behavior analysis. © 2024 IEEE.

关键词： Data mining

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Identifying Hacking Failures in the IaaS, PaaS, and SaaS Networks to Secure Cloud Applications 6

Identifying Hacking Failures in the IaaS, PaaS, and SaaS Net...

引用

6th Parul International Conference on engineering and Technology, PICET 2024

作者： Bhavsar, Rakesh Thakar, Vishvjit Dept. of Computer Science Engineering School of Engineering Indrashil University Gujarat Mahesana India

ISBN: (纸本)9798350369748

From small businesses to major corporations, Cloud computing is quickly taking over as the preferred platform. due to the obligation to guarantee that cloud apps are safe and can shield data from unauthorized attackers. According to the joint responsibility model of Cloud Security, organizations who utilize these services are also responsible for maintaining their degree of security while service providers are responsible for making sure that their services are safe. A shared responsibility Paradigm for identity and access management transfers part of the client's responsibility for application security. Setting up procedures for identity management and access control as well as carrying out ethical hacking and penetration testing are required to guarantee the greatest degree of data security. Since Cloud Security has developed into a shared duty between the user and the service provider, clients are now required to take accountability for the security of their applications in the cloud. IaaS and PaaS services are the major beneficiaries of this. Advanced security procedures like ethical hacking and penetration testing are necessary to provide a safe virtual environment. Organizations will be able to avoid harmful assaults and data leaks thanks to these efforts. Raising risk visibility, identifying vulnerabilities, minimizing risks, and gathering crucial telemetry data are the greatest ways to enhance security. The primary goal of this study article is to assess the techniques for designing a framework or checklist to identify associated risks & vulnerabilities while at the same time attacking cloud-based applications. As a consequence, malicious actors will be kept out of cloud-based programs. © 2024 IEEE.

关键词： Cloud security

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Implementation of human-following robot system applying virtual delay to posture of a leader 3

Implementation of human-following robot system applying virt...

引用

3rd International Conference on Image Processing and Robotics, ICIPRoB 2024

作者： Takemori, Fumiaki Tottori University Faculty of Engineering Dept. of Electrical Engineering and Computer Science Tottori Japan

ISBN: (纸本)9798350374766

This paper develops an implementation of a measurement and control system in which a vehicle follows its predecessor while maintaining a certain distance. First, we construct a model that virtually delays the reference coordinates of the leading target backwards, and propose a control law that follows the steering angle of the following vehicle to that virtual delay point. Next, a range sensor is used to detect the position and orientation of the leader, and the position and orientation is estimated using a template matching method based on the person's body contour point group measured by the sensor. Finally, the effectiveness (including robustness) of the proposed system is demonstrated in some simulations and human tracking experiments. © 2024 IEEE.

关键词： Template matching

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Diffusion Models for Automatic Music Mixing

Diffusion Models for Automatic Music Mixing

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Wu, Xinyang Horner, Andrew Hong Kong University of Science and Technology Dept. Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350362480

Music mixing is a process that involves fine-tuning the levels, dynamics, and frequency content of musical elements to ensure clarity and harmony in the final music production. In this paper, we present an automatic mixing system based on diffusion models to correct imbalances in music mixes. We manipulate the well mixed stems' short-time Fourier transform randomly to simulate the frequency and level imbalances commonly encountered in real-world scenarios. The difference between the imbalanced mix and the original mix is treated as noise for the diffusion model to predict, enabling the reverse denoising process to generate an automated mix. We evaluate our model's performance by calculating the signal-to-distortion ratio between the original and predicted mixes. These results are compared with baseline automatic mixing models, demonstrating significant improvements. Test set results in audio: https://***/diffmusicmix/ © 2024 IEEE.

关键词： Signal denoising

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Integration of Swarm Intelligence with KNN for Optimal Nearest Neighbors Value Prediction 9

Integration of Swarm Intelligence with KNN for Optimal Neare...

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9th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2023

作者： Anchalia, Aniketh Kakati, Anirudh Patil, Ambika Bhavana, B.N. Paudel, Ankit Chaudhari, Shilpa Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350334395

K-Nearest Neighbors (KNN), a simple and widely used algorithm, is extremely valuable in the field of machine learning models. Finding an optimal value of the nearest neighbor parameter in the KNN algorithm has been a problem that has existed for quite some time. In recent years, nature inspired algorithms have been widely emerging, with scope of being utilized in various engineering problems. This work proposes integrating a swarm intelligence optimization technique, the Sparrow Search Algorithm (SSA), for optimizing the process of finding K. The proposed hybrid model has been tested against four datasets of varying sizes and the results depict improvement in efficiency. The improvement is much larger as the size of the dataset increases, with around a 99% improvement in efficiency for the largest dataset tested on. KNN is also used to preprocess two of the datasets which have missing values, which result in improved accuracies of prediction. © 2023 IEEE.

关键词： Swarm intelligence

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ML Assisted Web Application Firewall 12

ML Assisted Web Application Firewall

引用

12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Kalariya, Pranav Jethva, Manthan Alginahi, Yasser University of Windsor Electrical and Computer Engineering Dept. Windsor Canada Computer Science Dept. Adrian College MI United States

ISBN: (纸本)9798350330366

This study presents the development and implementation of a sophisticated Web Application Firewall (WAF) empowered by machine learning techniques to bolster cybersecurity measures. Traditional WAFs primarily rely on rule-based systems, which may struggle to adapt to the evolving nature of web-based threats. In contrast, our proposed solution leverages machine learning algorithms to dynamically analyze and respond to emerging cyber threats, providing a more proactive and adaptive defense mechanism. The core functionality of the system involves the continuous monitoring of incoming web traffic, extracting relevant features, and utilizing a machine learning model to classify the traffic as either benign or malicious. The model is trained on historical data to recognize patterns and behaviors indicative of various cyber threats, including SQL injection, cross-site scripting, and other common attack vectors. Through this learning process, the system becomes adept.at discerning malicious activities and adapting its defense strategies accordingly. The proposed model helps achieve higher precision in identifying the threat requests from normal requests. © 2024 IEEE.

关键词： Learning algorithms

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Advancements in User Security: Enhancing Usability with Graphical Password Authentication 2

Advancements in User Security: Enhancing Usability with Grap...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Harisha Naik, Sandhya Ramesh Vasudeva, Shettigar Sarvani Shrilakshmi, K. Kothwal, Vaishnavi Sahyadri College of Engineering & Management Dept. of Computer Science & Engineering Mangalore Karnataka India

ISBN: (纸本)9798350327533

Text-based password authentication is a common method used to verify the identity of users who are trying to access a secure system or service. In order to use this authentication method, the user must input a password or other secret phrase that is then compared to a server-side copy of the same password. Access is given if the password typed matches the one saved. Graphical password authentication is a type of user authentication that involves using images or visual elements instead of alphanumeric characters to verify the identity of the user. Unlike traditional text-based passwords, graphical passwords offer an intuitive and user-friendly way of authentication, as they rely on the user's ability to remember pictures, shapes, and patterns. This technology has been developed to address the limitations of traditional text-based passwords, such as the difficulty of creating and remembering complex passwords, and the vulnerability to brute-force attacks. Compared to conventional text-based passwords, graphical password authentication has a number of benefits, including better usability and higher security. © 2024 IEEE.

关键词： Authentication graphical passwords security

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Authentication and Authorization in Zero Trust IoT: A Survey 35

Authentication and Authorization in Zero Trust IoT: A Survey

引用

35th Irish Systems and Signals Conference, ISSC 2024

作者： James, Meha Newe, Thomas O'Shea, Donna O'Mahony, George D. Munster Technological University Dept. Computer Science Cork Ireland University of Limerick Dept. Electronic and Computer Engineering Limerick Ireland

ISBN: (纸本)9798350352986

A critical challenge in implementing Zero trust (ZT) in Internet of Things (IoT) environments is ensuring secure authentication and authorization mechanisms. The work in this article delves into the intricate realm of ZT by critically examining the state-of-the-art authentication and authorization processes within the ZT framework for IoT. Despite the wealth of work on ZT and Zero Trust Architecture (ZTA), notable research gaps persist, specifically in identity authentication and fine-grained access control. Given the vast and diverse ecosystem of IoT devices, device identification complexities arise from a lack of unique hardware identifiers, rendering traditional methods susceptible to cyber-attacks, such as, spoofing. For dynamic IoT environments, achieving the optimal implementation in access policies poses a significant challenge. Numerous ZTA methodologies, integrating access controls, blockchain frameworks, and advancements in Artificial Intelligence (AI) and Machine Learning (ML), have been implemented to tackle these challenges. However, research gaps remain, particularly in understanding the behavioral aspects within these frameworks. As a result, this article focuses on identifying how device behavioral aspects can improve authentication and authorization in ZT for IoT. Finally, the article concludes by highlighting how these challenges can serve as future research directions. © 2024 IEEE.

关键词： Authorization

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Neural Harvest : Advancements in Crop Yield Forecasting 7

Neural Harvest : Advancements in Crop Yield Forecasting

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Seelam, Srinivasa Reddy Thivari, Charitha Sai Parabattina, Bhagath Narendra Kumar, G. Sumanth, K. Lakireddy Bali Reddy College of Engineering Dept. of Computer Science And Engineering Mylavaram India

ISBN: (纸本)9798350359299

Crop yield forecasting involves predicting the amount of crops that a farmer can expect from their field. This prediction is based on several factors, including soil type and environmental conditions. It is a crucial issue for the farming industry, which is the backbone of any nation's economy. Thanks to advancements in Artificial Intelligence, farmers can benefit from accurate crop yield estimations. This research study presents a Feed Forward Neural Network architecture that enables farmers to get accurate crop yield estimates and finally discusses about the implementation of the procedure and its outcome. © 2024 IEEE.

关键词： Feedforward neural networks

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NextGen Dynamic Video Generator using AI 9

NextGen Dynamic Video Generator using AI

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9th IEEE International Conference on Smart Structures and Systems, ICSSS 2023

作者： Anitha, R. Kishore, N. Vijay Anand, M. Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384208

In an era marked by remarkable technological advancements, the way we create and share information has undergone a profound transformation. This paradigm shift is epitomized by the NextGen Dynamic Video Generator using AI, a cutting-edge tool that seamlessly integrates artificial intelligence with content creation. What sets this tool apart is the customizability, which allows the users to fine-tune every aspect of the tutorials. Users could tune creative elements like humor, the dept. of explanation, character appearance, and voice to tailor tutorials to precise specifications. The core functionality of the tool revolves around script generation, the Cohere's language, laying the groundwork for the tutorial's content. Furthermore, seamless integration with Edge TTS ensures that the generated scripts are delivered with utmost clarity and engagement, enhancing the overall learning experience. Character animation is powered by SadTalker, adding a dynamic and captivating dimension to these tutorials. This animated character serves as a relatable guide, facilitating a deeper connection between the content and the audience. The tool also seamlessly integrates relevant and eye-catching images from Google, which are incorporated into the presentation slides. The workflow is a well-orchestrated process involving script generation, audio dialogue creation, image retrieval, video generation, and the seamless fusion of character animations and slides. The resulting video tutorials are not only comprehensive but also engaging and ready to be shared as valuable educational resources. © 2023 IEEE.

关键词： Image retrieval

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