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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Rajagopal, R. Senbagavalli, M. Debnath, Saswati Deepu, K. Darshan, K. Varun Tejas, K.S. Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350300857

Credit cards are an essential part of every person's daily life in the modern world. Customers use their credit cards and online transactions to make purchases for their requirements. To reduce the risk of defaulters, banks and other financial institutions consider customer credit applications. Credit risk is the increase in debt owed by a customer who repeatedly misses payments on their bills. The precision and recall measures received greater attention than the metrics for accuracy. Based on the False Negative value of the confusion metrics, logistic regression is the best model after comparison with the precision-recall curve. Additionally, a GUI (Graphical User Interface) was built after altering the logistic regression's threshold value and it successfully forecasted whether a customer was a defaulter or not. It is a problem to categorize clients' credit payment defaulters and non-defaulters. The basic objective of risk prediction is to evaluate each customer's credit risk and reduce it using information from financial statements, customer transactions, and repayment history. The damage and unpredictable nature. Different methods, including logistic regression and decision trees, are used to create risk prediction models. © 2023 IEEE.

关键词： Decision trees

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Bearing Error Diagnosis Using Deep Learning and Convolution Neural Network 2

Bearing Error Diagnosis Using Deep Learning and Convolution ...

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2nd International Conference for Innovation in Technology, INOCON 2023

作者： Martin, Richard Achary, Rathnakar Shelke, Chetan J Alliance University Alliance College of Engineering and Design Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350320923

Fault diagnostics and prognostics are essential issues. Industrial plants will be under a huge amount of pressure that maintains unpredictable interruption, system failures, and safety issues to a minimum, that necessitates identifying and eliminating potential issues as quickly as possible. Intelligent problem diagnosis is a promising technique because of its capacity to handle gathered signals quickly and effectively while also offering reliable diagnosis findings. Numerous authors have validated deep learning and machine learning approaches for identifying bearings failures, the findings have mostly been confined to tiny train and test datasets, with the input data modified to achieve high accuracy. In this article, original data of accelerometer sensor was loaded into unique periodic sequencing prediction algorithm that develops an edge fault diagnosis technique. We utilize identical frequency patterns as inputs to an innovative deep neural Long-Short-Term-Memory, Recurrent Neural Network to diagnosis bearings insufficiency at excellent accuracy inside the least time (CRNN). Without the use of database adjustment, the technique would acquire the maximum level of competence in the industry. The fault diagnostic method's efficacy and applicability are demonstrated by comparing the findings to those of other intelligent fault detection systems using two widely known benchmark real vibration datasets. © 2023 IEEE.

关键词： Recurrent neural networks

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Different Scenarios of File Sharing 2

Different Scenarios of File Sharing

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2nd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2023

作者： Bhalerao, Satyam Shravani, S. Halakude, Vishal Sajjan, Rajani MIT Art Design and Technology Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350333763

In the digital era, escalating data volumes necessitate secure and scalable file-sharing solutions. This research develops a private cloud architecture to address this need. It provides scalability through distributed servers, accommodating numerous users and data needs. Robust access controls, including authentication and encryption, ensure data security. The architecture supports diverse network configurations, enabling seamless file sharing. Synchronization and versioning facilitate collaboration, ensuring data integrity. In summary, our private cloud architecture offers secure, scalable, and efficient file sharing for organizations across various network scenarios. © 2023 IEEE.

关键词： Cloud computing architecture

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Investigation of Augmented Reality in the Agriculture Industry and Its Future 11th

Investigation of Augmented Reality in the Agriculture Indust...

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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Pavithra, K. Geetha, A. Department of Computer Science and Engineering Alliance University Bengaluru India Alliance College of Engineering and Design Alliance University Bengaluru India

ISBN: (纸本)9789819717231

The technology of augmented reality (AR) has the potential to boost overall productivity in the agricultural sector. It can be used with other technologies including robotic machinery, artificial intelligence (AI), the Internet of Things (IoT), predictive analytics, and precision algorithms. Along with the growing food demand, several microeconomic factors are anticipated to encourage the spread of AR in the agricultural sector. New farmers can now receive creative and safer teaching using augmented reality technology. Young farmers can learn about potential mishaps and mitigate future risks as a result of being electronically exposed to agricultural machinery. Given the facts, it is plausible to assume that augmented reality (AR) technologies are still in use. It has been reasonable to assume that AR technology is still being developed and is not yet sufficiently advanced for agricultural applications. However, there is a significant chance that it will be a big success. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Augmented reality

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Combining Deep Learning with Knowledge Graph for design Knowledge Acquisition in Conceptual Product design

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computer Modeling in engineering & sciences 2024年第1期138卷 167-200页

作者： Yuexin Huang Suihuai Yu Jianjie Chu Zhaojing Su Yangfan Cong Hanyu Wang Hao Fan Key Laboratory of Industrial Design and Ergonomics Ministry of Industry and Information TechnologyNorthwestern Polytechnical UniversityXi’an710072China School of Industrial Design Engineering Delft University of TechnologyDelft2628 CEThe Netherlands Department of Industrial Design College of ArtsShandong University of Science and TechnologyTsingtao266590China College of Computer Science and Technology Zhejiang UniversityHangzhou310027China

The acquisition of valuable design knowledge from massive fragmentary data is challenging for designers in conceptual product *** study proposes a novel method for acquiring design knowledge by combining deep learning with knowledge ***,the design knowledge acquisition method utilises the knowledge extraction model to extract design-related entities and relations from fragmentary data,and further constructs the knowledge graph to support design knowledge acquisition for conceptual product ***,the knowledge extraction model introduces ALBERT to solve memory limitation and communication overhead in the entity extraction module,and uses multi-granularity information to overcome segmentation errors and polysemy ambiguity in the relation extraction *** comparison verified the effectiveness and accuracy of the proposed knowledge extraction *** case study demonstrated the feasibility of the knowledge graph construction with real fragmentary porcelain data and showed the capability to provide designers with interconnected and visualised design knowledge.

关键词： Conceptual product design design knowledge acquisition knowledge graph entity extraction relation extraction

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Unveiling Consumer Online Reviews: An In-Depth Analysis of Uses and Gratificatications 4

Unveiling Consumer Online Reviews: An In-Depth Analysis of U...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Titus, Ray Rajesh Sharma, R. Chinnaiyan, R. Sungheetha, Akey Babu, Tina Nair, Rekha R Alliance University Bengaluru India Alliance University Alliance School of Engineering and Design Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350307757

This research scrutinizes the uses and gratifica- tions linked with consumer online reviewing, recognizing the motivations driving individuals to share feedback in digital spaces. Recognizing the constraints of time, money, and expertise, as well as potential limitations in sample selection, the study acknowledges the possibility of a small sample size hindering generalizability to the broader population of online consumers. Despite these limits, the study intends to investigate several forms of online evaluations (positive, negative, and neutral) in order to uncover the underlying motivations for customers' participation in digital mechanisms for feedback. Furthermore, the research endeavors to explore the repercussions businesses may encounter when providing incentives for customer feedback. While not considering the influence of different platforms or the specific stage of the purchasing process most affected, this study forms a foundational exploration into the multifaceted landscape of consumer online reviewing. By identifying and quantifying the components that impact consumer purchasing decisions, the research aims to shed light on the complex interplay between consumer motivations, online reviews, and business outcomes. This nuanced examination contributes to the existing body of knowledge in understanding the dynamics of online consumer behavior and provides valuable insights for businesses navigating the challenges and opportunities of the digital marketplace. © 2024 IEEE.

关键词： Motivation

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Feature Extraction and Similarity-Based Movie Recommendation Using Bag-of-Words Model

Feature Extraction and Similarity-Based Movie Recommendation...

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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Raj, Swati Dutta, Jhalak Bandopadhyay, Saikat Prasad, Rajesh Department of Computer Science and Engineering Christ University Bengaluru India Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of Artificial Intelligence and Machine Learning Netaji Subhash Engineering College Kolkata India Department of Computer Science and Engineering MIT Art Design and Technology University Pune India

ISBN: (纸本)9789819613472

Recommender systems have become pervasive in guiding users through a lot of choices available in today’s digital landscape. This paper presents a content-based recommender system focused specifically for movie recommendations, offering a viable alternative to collaborative filtering methods. Unlike collaborative filtering, which relies on user ratings and demographic data, the proposed system operates without such prerequisites, making it particularly advantageous in scenarios with limited user engagement. By leveraging content attributes such as title, genre, cast, and crew, the system identifies similarities between movies and recommends those akin to a user’s preferences. Through innovative preprocessing techniques and feature extraction methodologies, the system transforms textual descriptions into numerical vectors, enabling efficient recommendation generation. The proposed model contributes to addressing critical challenges inherent in collaborative filtering, including the cold start problem, data sparsity, and popularity bias. Furthermore, it underscores the broad applicability of recommender systems across various domains and emphasizes the continuous evolution of interface designs and algorithms within the research community. Overall, the proposed content-based recommender system offers a promising solution for delivering personalized movie recommendations while advancing the state of the art in recommendation technology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bag-of-words Cosine similarity Filtering system Recommender system TF-IDF

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BIG FIVE (O.C.E.A.N): A New Way to Recruit 1

BIG FIVE (O.C.E.A.N): A New Way to Recruit

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1st International Conference on Cognitive Computing and engineering Education, ICCCEE 2023

作者： Agarwal, Raj Mukherjee, Roshni More, Nitin Pawar, Suvarna Design and Technology University Computer Science Engineering MIT Arts Pune India

ISBN: (纸本)9798350332803

The developed system offers several advantages for HR Managers in large organizations who have to deal with a high volume of job applicants for limited vacancies. With the help of natural language processing and text mining technology-based artificial intelligence, the system can efficiently and effectively shortlist the best candidate from a large pool of applicants. One of the key features of the system is the use of a ranking algorithm that helps HR Managers to identify the most suitable candidate based on their qualifications, expertise, and other relevant factors. The ranking algorithm has been trained on a large dataset of job vacancies and job applicants to ensure that it selects the most suitable candidate for the position. The system fetches all relevant information from the uploaded resume, including the candidate's work experience, educational qualifications, personal information, and other relevant details. The system then conducts personal competitive analysis and personality trait analysis, which is recorded in a database for admin analysis. This approach ensures that HR Managers have access to all relevant information and can select the best candidate for the position, providing equal opportunities to all applicants without any discrimination. The system also provides recommendations to the applicants, which can help them improve their resumes and increase their chances of getting shortlisted. The recommendations include skills that can be added to their resume, job roles that are suitable for their expertise level, course and certificate recommendations, resume tips and ideas, and overall resume score. Additionally, the system also provides Resume Interview Tips through YouTube videos. The system's outcome has demonstrated that it is capable of meeting the realistic demands of recruitment for candidates. By leveraging the power of natural language processing and text mining technology-based artificial intelligence, this system has greatly simplified

关键词： Natural language processing systems

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Joint Rain Streaks & Haze Removal Network for Object Detection

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computers, Materials & Continua 2024年第6期79卷 4683-4702页

作者： Ragini Thatikonda Prakash Kodali Ramalingaswamy Cheruku Eswaramoorthy K.V Department of Electronics and Communication Engineering National Institute of TechnologyWarangal506004India Department of Computer Science and Engineering National Institute of TechnologyWarangal506004India Department of Electronics and Communication Engineering Indian Institute of Information TechnologyDesign and ManufacturingKurnool518008India

In the realm of low-level vision tasks,such as image deraining and dehazing,restoring images distorted by adverse weather conditions remains a significant *** emergence of abundant computational resources has driven the dominance of deep Convolutional Neural Networks(CNNs),supplanting traditional methods reliant on prior ***,the evolution of CNN architectures has tended towards increasing complexity,utilizing intricate structures to enhance performance,often at the expense of computational *** response,we propose the Selective Kernel Dense Residual M-shaped Network(SKDRMNet),a flexible solution adept at balancing computational efficiency with network accuracy.A key innovation is the incorporation of an M-shaped hierarchical structure,derived from the U-Net framework as M-Network(M-Net),within which the Selective Kernel Dense Residual Module(SDRM)is introduced to reinforce multi-scale semantic feature *** methodology employs two sampling techniques-bilinear and pixel unshuffled and utilizes a multi-scale feature fusion approach to distil more robust spatial feature map *** the reconstruction phase,feature maps of varying resolutions are seamlessly integrated,and the extracted features are effectively merged using the Selective Kernel Fusion Module(SKFM).Empirical results demonstrate the comprehensive superiority of SKDRMNet across both synthetic and real rain and haze datasets.

关键词： Image deraining Selective Dense Residual Module(SDRM) Selective Kernel Fusion Module(SKFM) Selective KernelDense Residual M-Shaped Network(SKDRMNet)

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I $^{2}$ RNN: An Incremental and Interpretable Recurrent Neural Network for Encrypted Traffic Classification

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IEEE Transactions on Dependable and Secure Computing 2023年 1-14页

作者： Song, Zhuoxue Zhao, Ziming Zhang, Fan Xiong, Gang Cheng, Guang Zhao, Xinjie Guo, Shize Chen, Binbin College of Computer Science and Technology Zhejiang University Hangzhou China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Science and Engineering Southeast University Nanjing China Information Systems Technology and Design Singapore University of Technology and Design Singapore

Traffic classification occupies a significant role in cybersecurity and network management. The widespread of encryption transmission protocols such as SSL/TLS has led to the dominance of deep learning based approaches. In cybersecurity, strong adversaries often complicate their strategies by constantly developing emerging attacks. Meanwhile, security practitioners desire to grasp the reasons for inference results. However, existing deep learning approaches lack efficient adaptation for incremental traffic types and often have less interpretability. In this paper, we propose I $^{2}$ RNN, an Incremental and Interpretable Recurrent Neural Network for encrypted traffic classification. The I $^{2}$ RNN proposes a novel propagation process to extract the sequence fingerprints from sessions with local robustness. Meanwhile, this proposal provides interpretability including time-series feature attribution and inter-class similarity portrait. Moreover, we design I $^{2}$ RNN in an incremental manner to adapt to emerging traffic types. The I $^{2}$ RNN only needs to train an additional set of parameters for the newly added traffic type rather than retraining the whole model with the entire dataset. Extensive experimental results show that our I $^{2}$ RNN can achieve remarkable performance in traffic classification, incremental learning, and model interpretability. Compared with other local interpretability methods, our I $^{2}$ RNN exhibits excellent stability, robustness, and effectiveness in the interpretation of network traffic data. IEEE

关键词： Cryptography

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