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3rd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2025

作者： Rani, Chadalavada Ester Raju, V. Purushothama Dept. of Computer Science & Engineering A.P Bhimavaram India

ISBN: (纸本)9798331543358

The Unified Payments Interface has become increasingly popular as a convenient and user-friendly digital payment platform. However, the surge in Digital Transaction has also resulted in a rise in fraudulent activities, presenting a significant security challenge. This paper introduces a novel approach to detecting fraudulent Digital Transaction by leveraging convolutional neural networks (CNN). By incorporating the CNN model, this study aims to offer a cost-effective solution for identifying Digital Financial fraud. The CNN's ability to analyse patterns in data makes it particularly suitable for fraud detection in Digital Financial Transactions. We evaluate our proposed methodology using a comprehensive dataset that includes both fraudulent and genuine transactions. Our results demonstrate a significant improvement in fraud detection accuracy and efficiency compared to existing methods. This research contributes to the advancement of secure digital payment systems by presenting an innovative approach to detect fraud transactions. Financial institutions and payment service providers can utilize this methodology to enhance the security of Digital Financial Transactions and protect users from fraudulent activities. © 2025 IEEE.

关键词： Convolutional neural networks

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Generating Summaries of Customer Reviews: A Hybrid Model Approach

Generating Summaries of Customer Reviews: A Hybrid Model App...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Upadhyay, Akshat Sharma, Jeevika Jha, Sumit Kumar Minocha, Sachin Amity University Dept. of Computer Science Noida India

ISBN: (纸本)9798350375237

Online customer reviews provide researchers with the knowledge of what people think of products and services. It quickly becomes evident that it would be tedious to manually summarize all the reviews since the data is inherently unstructured. The paper suggests an automatic review sum-marization system framework through TensorFlow, leveraging advanced deep learning. The technique uses two models: RNN-LSTM(LSTM) and CopyCat. LSTM is used widely for long reviews because of its ability to grasp the idea and the attitude of the writer, ensuring that summaries convey a sentiment genuinely felt by the reviewer. Then, the task is passed to CopyCat, which utilizes several informative words for the person's annotation. Thus, the results benefit from additional context words or facts. By leveraging the strengths of these models, the system can provide concise yet meaningful summaries that reflect the true sentiments expressed in the original reviews. Additionally, this work trains the models separately on a handpicked dataset of Amazon reviews. By doing this, they learned the specific language and words used when discussing these products. This custom training enabled the models to create summaries that accurately reflect customer opinions about the items. © 2024 IEEE.

关键词： Sales

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Multispectral Satellite Data Classification Using Rank Correlation Similarity Index and Automatic Labeling Technique

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SN computer science 2024年第8期5卷 1073页

作者： Ain, Arindom Gogoi, Minakshi Chutia, Dibyajyoti Dept. of Computer Science and Engineering GIMT Under Assam Science and Technology University Assam Guwahati India Dept. of Computer Science and Engineering Girijananda Chowdhury University Assam Guwahati India NESAC Umium Shillong India

Multispectral satellite image classification is crucial in remote sensing and image analysis, serving various applications like water area extraction, urban region classification, and vegetation monitoring. Model-based classification, particularly supervised learning approaches, heavily relies on manually annotated training samples, which are labor-intensive and subject to human bias. However, solely depending on features derived from individual pixels may fail to capture spatial contextual information effectively and manage mixed pixels and various types of noise, resulting in classification inaccuracies. To address these challenges, this research proposes a novel unsupervised learning approach and automatic labeling technique (ALT). This approach utilizes collective information from groups of pixels instead of relying solely on individual pixels for identification and extraction of meaningful unique patterns which serves as the most significant factor for satellite image classification. A new similarity index, the patch similarity index (PSI), is generated by incorporating the rank correlation similarity index (RCSI). The PSI is used to determine the similarity factor both between the patches and within the patch. Subsequently, a clustering algorithm is employed to group pixels into four distinct classes: vegetation, water, built-ups, and open land. Additionally, ALT streamlines the post-classification process by reducing the need for manual intervention in mapping clusters to the actual class label. The proposed method offers a more accurate and reliable unsupervised classification for multispectral satellite imagery, promising enhanced efficiency and reduced manual labor. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Automatic labeling technique Frequent masking Multispectral Patch similarity index Rank Correlation

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Object detection and tracking for Personal Protective Equipment

Object detection and tracking for Personal Protective Equipm...

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Harinath Reddy, C. Debnath, Saswati Tharani, G. Saketh Reddy, S. Dept. Computer Science Alliance University Bengaluru India

ISBN: (纸本)9798331510299

This paper focuses on real-time detection and tracking techniques for Personal Protective Equipment (PPE) in workplace scenarios. Based on the critical characteristics of efficiency, the potential solution addresses the identification and tracking of PPE for safety and anti-injury purposes by leveraging advanced underlying technologies. Therefore, this paper presents a real-world applicability, efficiency, and reliability in PPE monitoring using YOLOv8 (You Only Look Once version 8). The overall approach of the system includes data cleaning, model learning, and deployment. The efficiency of the proposed model is evaluated by performance indicators precision, recall, etc. The findings thus support the applicability of YOLOv8 in real-scenario settings with reliable PPE identification and monitoring. The accuracy achieved with the proposed model is 94%. © 2024 IEEE.

关键词： Efficiency

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ICARE: the principles of Explainable AI in a Context-aware Recommendation APP

ICARE: the principles of Explainable AI in a Context-aware R...

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Workshops of the EDBT/ICDT 2024 Joint Conference, EDBT/ICDT-WS 2024

作者： Dalla Vecchia, Anna Oliboni, Barbara Quintarelli, Elisa Dept. of Computer Science University of Verona Verona Italy

In this paper, we summarize our previous contribution to the research area of Explainable Recommender Systems in the healthcare domain, called ICARE (Intuitive Context-Aware Recommender with Explanations), which is a framework based on data-mining algorithms that can provide personalized recommendations with contextual and intuitive explanations. In particular, we consider the scenario related to physical activities to improve sleep quality, and we now describe how the system satisfies the four principles of Explainable Artificial Intelligence. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Recommender systems

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Employee Watcher: A Machine Learning-based Hybrid Insider Threat Detection Framework 7

Employee Watcher: A Machine Learning-based Hybrid Insider Th...

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7th Cyber Security in Networking Conference, CSNet 2023

作者： Rauf, Usman Wei, Zhiyuan Mohsen, Fadi Dept. of Mathematics & Computer Science Mercy College NY United States Bernoulli Institute for Mathematics Computer Science & Artificial Intelligence University of Groningen Groningen Netherlands

ISBN: (纸本)9798350342871

Insider threats refer to harmful actions carried out by authorized users within an organization, posing the most damaging risks. The increasing number of these threats has revealed the inadequacy of traditional methods for detecting and mitigating insider threats. These existing approaches lack the ability to analyze activity-related information in detail, resulting in delayed detection of malicious intent. To address this, our paper presents a hybrid insider threat detection framework. We enhance prediction accuracy by incorporating a layer of statistical criteria using information gain metrics on top of Machine Learning-based classification. We evaluate the performance of our framework using a real-life threat test dataset (CERT r4.2) and compare it to existing methods on the same dataset [7]. Our initial evaluation demonstrates that our proposed framework achieves an accuracy of 98.94% in detecting insider threats, surpassing the performance of existing methods. Additionally, our framework effectively handles potential bias and data imbalance issues that can arise in real-life scenarios. © 2023 IEEE.

关键词： Machine learning

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DURATION-BASED INVESTIGATION OF USER CONTENT CHOICES IN THE EXIT OF FILTER BUBBLES 6

DURATION-BASED INVESTIGATION OF USER CONTENT CHOICES IN THE ...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Bunza, Rabiatu DEPT. OF COMPUTER SCIENCE FACULTY OF SCIENCE AND ENGINEERING SWANSEA UNIVERSITY United Kingdom

ISBN: (纸本)9798350394634

Content personalization on social platforms has been linked to the creation of filter bubbles. The algorithms provide content recommendations based on the user’s browsing history and interests, which limits content diversity. Studies have been conducted on platforms such as Facebook and YouTube to investigate user awareness and strategies to exit filter bubbles. However, no study has employed a multi-platform, user-centered perspective in investigating filter bubble exit through change in search content, as well as the duration of the content transition across multiple platforms. This study examines the duration of user exit of filter bubbles, across three social platforms;TikTok, YouTube Shots, and Instagram Reels, as well as the impact of word similarity on the duration of the transition between contents, across the platforms. The methodology applied involved triggering the filter bubble effect through user interactions with the search content across the different platforms. Findings revealed that the mean time of a transition between search terms on TikTok is significantly higher than the mean time on Instagram reels, and YouTube shorts. Similarly, the study identified differences in the duration of transition across the different platforms, due to the similarity of the search terms. Apart from improving user awareness of a filter bubble exit strategy, this study contributes to Human-computer Interaction research by highlighting some design implications for future social media application design. © 2024 IEEE.

关键词： Social networking (online)

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Comprehensive Women's Health Risk Assessment Platform 2

Comprehensive Women's Health Risk Assessment Platform

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Navya, S. Saha, Sharmistha Nagarajan, Deepa Sathyabama Institute of Science and Technology Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350349221

The goal of this research is to develop a machine learning-based method for predicting several diseases in women, with an emphasis on polycystic ovarian syndrome (PCOs), diabetes, heart disease, and breast cancer. Timely and accurate prediction of these disorders is essential for early intervention and prevention due to their impact on women's health and prevalence. Predictive models are trained using a wide variety of medical data, including genetic markers, lifestyle factors, demographic data, and clinical test findings. To find patterns and connections between these predictors and the incidence of diseases, machine learning methods are used, such as decision trees, random forests, and support vector machines. The models are able to forecast with accuracy the probability that a woman would develop one or more of these diseases by efficiently obtaining and evaluating data from these datasets. Metrics including accuracy, precision, recall, and F1-score are used to assess the models' performance;the outcomes demonstrate the models' promising levels of predictive power. The creation of a system like this can facilitate targeted screening and preventive measures, assist medical practitioners in identifying high-risk individuals, and eventually lead to better patient outcomes. © 2024 IEEE.

关键词： Diseases

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Intelligent Traffic Management System: An Advanced Solution 7

Intelligent Traffic Management System: An Advanced Solution

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Rushi, Tankala Zamaan, Shaik Mohammad Pravin, A. Sathyabama Institute of Science And Technology Dept. of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350359299

The Intelligent Traffic Management System (ITMS) is a cutting-edge solution designed to address multiple aspects of traffic management and safety. This advanced system incorporates various technologies to tackle issues such ashelmet compliance, traffic signal violations, number plate identification, cell phone usage tracking, and proximity-based police station alerts. By leveraging the power of artificial intelligence and machine learning algorithms, the ITMS can accurately detect and enforce helmet compliance among motorcyclists, ultimately reducing the risk of head injuries in road accidents. Furthermore, the system utilizes advanced video analytics to detect violations of traffic signals, ensuring that motorists adhere to traffic rules and regulations. Additionally, the ITMS employs automatic number plate recognition (ANPR) technology to quickly and efficiently identify vehicles with illegal or expired license plates, enabling law enforcement agencies to take appropriate action. Moreover, the system includes a cell phone usage tracking feature which uses computer vision techniques to identify drivers who are using their phones while driving, contributing toa decrease in distracted driving incidents. Lastly, by introducing proximity-based police station alerts, the system offers a comprehensive approach to address challenges and improve efficiency, the ITMS enables prompt response from law enforcement agencies by automatically notifying the nearest police station in the event of a traffic violation or accident. In summary, the Intelligent Traffic Management System provides a comprehensive and holistic approach to management and safety through itsfeatures of helmet compliance, traffic signal violation detection, number plate identification, cell phone usage tracking, and proximity-based police station alerts. © 2024 IEEE.

关键词： Traffic signals

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Optical Mark Recognition with Object Detection and Clustering 6

Optical Mark Recognition with Object Detection and Clusterin...

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6th International Conference on Electrical Engineering and Information and Communication Technology, ICEEICT 2024

作者： Tabassum, Kaifa Rahman, Zhalok Shahjalal University of Science and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385779

OMR, or optical mark recognition, is the automated analysis of human-marked documents. Presently, many competitive examinations rely on multiple-choice questions, and the answers to these questions are recorded in OMR sheets. In this study, we present an automated answer detection system from an OMR sheet image using computer vision techniques. Typically, evaluating an OMR sheet from an image involves scanning, extracting data, and interpreting the results through various image processing techniques. However, traditional approaches have limitations. They cannot detect empty responses or recognize multiple choices properly without a specified layout or total number of questions stated. We took a unique approach by employing an object detection model, YOLOv8n, to detect and classify OMR marks. To address overlapping bounding boxes, we implemented the DBSCAN clustering algorithm to effectively group the columns and determine question order. Our system is designed to work with multiple layouts, as it can detect columns through clustering. Furthermore, it can identify unanswered questions as well as multiple marked options. To train and evaluate our model, we created a custom dataset with five different OMR sheet layouts. We achieved a remarkable precision of 96.5%, a recall of 99.8%, and an mAP (mean average precision) of 97.3%. These results demonstrate the efficacy of our approach to OMR sheet analysis. © 2024 IEEE.

关键词： Object detection

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