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Customer Spending Prediction on E-Commerce Website 15

Customer Spending Prediction on E-Commerce Website

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Salake, Sudha V. Patil, Pankaja Kunchur, Pavan Bangarashetti, Sadhana Managuli, Manjunath Patil, Sheetal B. Department of Computer Science and Engg KLS Gogte Institute of Technology Belagavi India Department of Information Science and Engg Basaweshwar Engineering College Bagalkot India Department of Electronics and Communication Engineering KLS Gogte Institute of Technology Belagavi India Department of Computer Science and Engg SJB Institute of Technology Belagavi India

ISBN: (纸本)9798331300579

In the ever-evolving world of e-commerce, understanding customer behavior and predicting their spending patterns is crucial for businesses to thrive. This work presents an innovative application designed to predict customer spending on an e-commerce website. By leveraging advanced machine learning techniques and utilizing comprehensive customer data, the application offers valuable insights that can be used to optimize sales strategies and enhance customer *** application employs a robust predictive model trained on historical customer data, including purchase history, browsing patterns, demographic information, and transactional *** application incorporates real-time data updates to ensure continuous improvement and adaptability to changing customer *** proposed application offers a powerful tool for e-commerce businesses to predict customer spending, optimize sales strategies, and deliver personalized experiences. By harnessing the power of machine learning and leveraging comprehensive customer data, businesses can stay competitive in the dynamic e-commerce landscape and foster long-term customer loyalty. © Grenze Scientific Society, 2024.

关键词： Marketplaces

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Domestic Violence Detection System Using Natural Language Processing

Domestic Violence Detection System Using Natural Language Pr...

引用

2023 International Conference on Innovations in Engineering and technology, ICIET 2023

作者： Fathima, K.S. Haroon, Rosna P. Fathima, Fitha Thajudeen, Thameesa Fudin, Muhammedshera Ilahia College of Engineering and Technology Department of Computer Science and Engineering Kerala Muvattupuzha India

ISBN: (纸本)9798350309980

Domestic violence is a widespread problem that leaves victims feeling helpless and trapped. It refers to a pattern of abusive behavior in an intimate relationship, where one partner seeks to exert power and control over the other. To address this issue, we proposed a system that incorporates several advanced technological aspects to address domestic violence and support victims. It includes a speech recognition module that identifies abusive behavior patterns through specific words and phrases associated with domestic violence. Natural language processing techniques analyze the context and tone of the language to understand the emotional state of the victim and detect instances of emotional abuse. A camera system with computer vision capabilities detects physical violence and abuse signs through object detection and tracking in video footage. The system is accessible through a mobile app, enabling victims to collect evidence of abuse and access necessary resources and support services. Machine learning algorithms continuously improve the system's accuracy and performance by learning from a large dataset of abusive language patterns and visual cues. When abusive behavior is detected, the system can automatically send alerts to law enforcement and emergency contacts, providing immediate assistance. Recorded voice and video footage, along with associated analysis results, are securely stored as evidence for legal proceedings. By integrating these technological aspects, the system aims to empower victims, improve evidence collection, and support efforts to combat domestic violence. © 2023 IEEE.

关键词： Object detection

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Fresh Products Application Information Management System 2nd

Fresh Products Application Information Management System

引用

2nd International Conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2022

作者： Yang, Xueyan College of Computer Science and Technology Huai Bei Normal University Huai Bei China

ISBN: (纸本)9789819945535

There has been a pronounced increase in online shopping since the start of the COVID-19 pandemic. Online supermarkets are one of the critical topics of research today. Today there is a wide range of online supermarket apps available nationally and internationally, and good progress has been made in transporting goods. However, there are still research gaps in managing food waste and fresh produce. Therefore, the research theme of this paper is to establish an information management system for a new production software. The methodology of this paper is as follows: firstly, the author analyses the functional requirements of the system, secondly the structural framework of the system is designed based on the requirements analysis, then the ER diagram is completed based on the understood data requirements, processing requirements, security and integrity requirements of the users and administrators, the database is created, tables are created, data is inserted, and finally the desired query functions are achieved through MySQL. Finally, the web design for the background management of product additions, user registration accounts, and login accounts was conducted by compiling HTML and CSS in VScode software. This paper has achieved the target query function. However, the query database steps are still slightly complicated, not convenient and fast enough, and the operational requirements of the administrator are also relatively high. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Database systems

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Human Safety and Vehicle Speed Controlling using Internet of Things (IoT) 2

Human Safety and Vehicle Speed Controlling using Internet of...

引用

2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Kavitha, S. Safreen Padmasree Niskriyaa Keerthana Velammal College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Madurai India

ISBN: (纸本)9798350347579

People require transportation to move from one place to another place nowadays. As the population expands, so does transportation across the country. Despite the high demand for vehicles, the number of accidents are increasing due to the lack of safety precautions. Moreover, major accidents occur for two-wheeler drivers because two-wheelers lack safety precautions that four-wheelers have. Many research studies that are conducted recently to determine the cause of accidents concluded that driving recklessly in crowded areas causes road accidents. The major goal of this research work is to minimize the road accidents and prevent road accidents while ensuring driver safety. The proposed model enhances human safety while driving is by employing Arduino UNO and IoT. The vehicle will only start if the driver wears helmet and not intoxicated. To track the vehicle's current location, GPS module is used. To avoid accidents due to vehicle overspeed, the speed of vehicle is continuously monitored. In a particular zone, the driver will be alerted to reduce the speed. © 2023 IEEE.

关键词： Roads and streets

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Injecting Visual Features into Whisper for Parameter-Efficient Noise-Robust Audio-Visual Speech Recognition

Injecting Visual Features into Whisper for Parameter-Efficie...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yang, Zhao Yeo, Yue Heng Jiang, Rui Fu, Xiao Chen, Weiguang Xi, Wei Zhao, Jizhong School of Computer Science and Technology Xi'an Jiaotong University China School of Computer Science and Engineering Nanyang Technological University Singapore College of Computer Science and Electronic Engineering Hunan University China

ISBN: (纸本)9798350368741

Audio-visual speech recognition (AVSR) aims to enhance the robustness of an automatic speech recognition (ASR) systems by incorporating visual information from lip movements, especially in challenging noisy environments. Nevertheless, most current approaches either involve training from scratch or fully finetuning a pre-trained model, both of which incur significant computational costs and are often impractical for large-scale speech foundation models. This gap highlights the need for more efficient methods to leverage visual and acoustic information in AVSR tasks. To address this challenge, we propose AVWhisper, a parameter-efficient model that integrates visual and acoustic representations by injecting visual features from the AV-HuBERT encoder into the pre-trained Whisper model. Our approach leverages the existing attention mechanisms in Whisper to facilitate cross-modal interaction and integrates auxiliary visual information through lightweight adapters based on Low-Rank Adaptation (LoRA) and prompt-based techniques. Furthermore, a two-phase training strategy is adopted to effectively handle cross-domain differences and visual information injection problems respectively. Extensive experiments on the LRS3-TED dataset demonstrate that AVWhisper consistently outperforms state-of-the-art methods across various noise conditions, offering a more efficient and scalable solution for audio-visual speech recognition. © 2025 IEEE.

关键词： Audio-Visual Speech Recognition LoRA Noise-Robust Parameter-Efficient Prompt Whisper

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Multi-granularity Dynamic Identity Aggregation for Text-based Person Re-identification 11

Multi-granularity Dynamic Identity Aggregation for Text-base...

引用

11th Joint International Information technology and Artificial Intelligence Conference, ITAIC 2023

作者： Zhu, Hanyue Du, Guodong College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing21000 China

ISBN: (纸本)9798350333664

Text-based person re-identification(ReID) is a text-to-image retrieval task that searches images of people across non-overlapping cameras with textual descriptions. Due to significant intra-class variations and cross-modality disparities, along with substantial sample noise, it becomes challenging to learn discriminative features. Existing text-based ReID methods tend to focus on learning global representations, which possess limited discriminability and weak robustness to noisy samples. In this paper, we propose a novel Multi-granularity Dynamic Identity Aggregation(MuDIA) learning approach that simultaneously explores intra-modal fine-grained and cross-modal coarse-grained contextual cues for text-based ReID. MuDIA consists of two primary components: Weighted Parts Aggregation for Global Feature (WPAG) and Multi-Granularity Refinement of Local Feature (MGRL). WPAG aims to learn discriminative features by aggregating parts and simultaneously exploring the contextual relations among body parts within each modality. MGRL aligns the features of the two modalities at multi-granularity, eliminating the modality gap at cascade-style levels and gradually reducing the feature gap between the visual domain and the textual domain. Extensive experiments are carried out to evaluate the proposed framework on two text-based ReID datasets, i.e., CUHK-PEDES and ICFG-PEDES. © 2023 IEEE.

关键词： Cross-modality Person re-identification Text-to-image alignments

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Cirrhosis Prediction in Chronic Liver Disease Patients Using Machine Learning Techniques 3

Cirrhosis Prediction in Chronic Liver Disease Patients Using...

引用

3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Athish, V.P. Sree Nandha, S.S. Rajeswari, D. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Dept. of Data Science and Business Systems Tamilnadu Kattankulathur603203 India

ISBN: (纸本)9798350322842

Liver failure is a serious medical condition that can have life-threatening consequences. The liver is responsible for filtering blood and performing many other important functions. With the rise of machine learning in healthcare, there is an opportunity to use past datasets to predict liver failure early on. The main focus of this research work is to explore the application of supervised machine learning algorithms in predicting liver failure. The study involved pre-processing techniques such as univariate and bivariate analysis, and data visualization to gain better insights into the features of the dataset. Using these techniques, a multi-class classification model was built with machine learning algorithms, and the performance metrics of accuracy, F1 score, and recall were used to compare the algorithms. The results show that machine learning algorithms can accurately predict liver failure and can be an effective tool in healthcare. This study demonstrates the potential of machine learning in predicting liver failure and can contribute to the development of future models in the field. The results of this research study indicates that the proposed algorithm achieved an accuracy of 94.48%. The proposed work will be beneficial to researchers in this field and inspire them to build more advanced and accurate models. © 2023 IEEE.

关键词： Support vector machines

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AI-Driven Assessment and Evaluation: Enhancing Sustainability in Educational Practices 3

AI-Driven Assessment and Evaluation: Enhancing Sustainabilit...

引用

3rd International Conference on Automation, Computing and Renewable systems, ICACRS 2024

作者： Chinnasamy, P. Pushpalatha, M. Nirmala, V. Vidhya, V. Ayyasamy, Ramesh Kumar Priya, Divya Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Computer Science and Technology Karpagam College of Engineering Coimbatore India Department of MCA Annamacharya PG College of Computer Studies Rajampet Kadapa India India Kampar Perak Malaysia Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

ISBN: (纸本)9798331532420

AI-powered educational technologies are emerging as transformative forces in the quickly changing field of education, where innovation is essential to keeping ahead of the competition. Assessments are one area that is undergoing revolutionary transformation. Artificial Intelligence (AI) is redefining the way we evaluate learning outcomes as traditional evaluation approaches struggle to keep up with the demands of the digital age. The field of artificial intelligence-powered learning assessments has a lot of promising future developments ahead of it. This research proposes novel technique in assessment and evaluation based on online education system using Artificial intelligence in machine learning techniques. Here, the input has been analysed as student performance data based on online education and processed for removing of missing values with noise removal. Then this data has been analysed using spatio LSTM convolutional fuzzy neural network for classification of evaluation and assessment model. the experimental analysis has been carried out for various student performance analysis dataset in terms of training accuracy, average precision, specificity, F-measure and recall. Proposed method training accuracy 97%, Average precision 94%, F-measure 90%, RECALL 93%, SPECIFICITY 96%. © 2024 IEEE.

关键词： Adversarial machine learning

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Smart Language Checker: A Machine Learning Solution for Offensive Language detection in Social Media

Smart Language Checker: A Machine Learning Solution for Offe...

引用

2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Kavitha, S. Anchitaalagammai, J.V. Murali, S. Deepalakshmi, R. Himal, L.R. Suryakanth, M.S. Velammal College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350348910

Social media serves as a prominent platform for individuals to express their thoughts, often encompassing both positive and offensive language. Less resource-rich languages benefits from automated offensive language detection systems. This article addresses this gap by proposing a model tailored for the low-resource language. Leveraging a manually labeled dataset of 60,000 comments from the dataset collected from various social media sources, the proposed model explores three feature extraction techniques: bag-of-words (BoW), Vectorization-Term frequency-inverse document frequency (TF-IDF), and Tokenization methods. Employing both traditional classifiers and a deep sequence model, the study reveals that the random forest classifier excels, achieving a testing accuracy of 94.07% when considering unigrams, bigrams, and trigrams. Furthermore, TF-IDF yields a maximum accuracy of 93.90%.The corpus generated in this endeavor is made accessible to researchers in this field, facilitating advancements in offensive language detection for English. © 2023 IEEE.

关键词： Feature extraction

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Design Thinking and Innovative Tool Use in Business Model

Design Thinking and Innovative Tool Use in Business Model

引用

2024 International Conference on Intelligent systems for Cybersecurity, ISCS 2024

作者： Singhi, Nitika Verma, Ankush Agrawal, Atul Khan, Irfan Computer Science Engineering Shri Vaishnav Vidhyapeeth Vishwavidyalava Indore India Data Analytics and Information System Prestige Institute of Management and Research Indore India Computer Science Engineering Sushila Devi Bansal College of Technology Indore India Computer Science Engineering Bansal Institute of Science and Technology Bhopal India

ISBN: (纸本)9798350375237

In past few year designs thinking has worked on several new effective problem and has generated innovative solution for new business model. For the scenario of today's demand, to better understand how design thinking can work on different perspectives in business models and how it can bring new innovative solutions for any problem is essential However, studies of existing literature explore the application, potential, and challenges of Design Thinking towards the comprehensive aim of this paper, which regards business model innovation as a fundamental subject of Design Thinking. The paper provides practical tools for exploring Design Thinking along with various critical aspects for the researchers to explore how Design Thinking can be applied to innovative business models. The main focus of this paper is to explain the basics of Design Thinking, how to frame the mind through different techniques used, the Design Techniques used for a business model, the methodology of a design thinker, and different tools like BPM, Blue Work Live, Mind Mapping, Brainstorming, and RICPS. © 2024 IEEE.

关键词： Mapping

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