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Exploring the possibilities of integration of cyber-psychology for human behaviour in a smart city

International Journal of Information and Communication Technology 2024年第2期25卷 150-167页

作者： Wen, Liping Ting, Zhou Zheng, Huang Alfred Daniel, J. Antonidoss, A. Department of Psychology University of Chinese Academy of Sciences Beijing100101 China Institute of Psychology Chinese Academy of Sciences Beijing100101 China Department of Medical Psychology School of Health Humanities Peking University Beijing100191 China Department of Computer Science & Engineering SNS College of Technology India Department of Computer Science and Engineering Hindustan Institute of Technology and Science India

The smart city idea differs between cities and nations. In all meanings and characteristics of a smart city, public involvement is the only thing that remains common. Therefore, it is a very significant field to study human behaviour and development in smart cities. This paper presents a framework for identifying qualities necessary for people to be classified as intelligent persons and to integrate these human behavioural characteristics in cyber technology. Human behaviour in a smart city has been analysed using the machine learning algorithm and big data analytics. The integrated machine learning and big data analytics framework (iML-BD) classifies the cyber behaviour of intelligent persons in a smart city by observing the cyber activities performed by the individuals. Furthermore, this paper handles the risk factors for cyber-acquired and cyber-dependant crime violence and abuse that vulnerable internet and public access devices using blockchain technology. Blockchain is a method of storing data that takes too long to alter, modify, or manipulate. A blockchain is an electronic accounting system that is reproduced and spread through the Bitcoin protocol’s entire communication network. The case study performed on iML-BD has resulted in the highest performance in terms of prediction accuracy of 94.98%. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Smart city

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Emerging Security Challenges in Advanced Cloud Computing Environment 2

Emerging Security Challenges in Advanced Cloud Computing Env...

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2nd IEEE International Conference on Advancements and Key Challenges in Green Energy and Computing, AKGEC 2024

作者： Kumar, Anshul Tiwari, Gagan Malviya, Aadarsh Noida International University School of Sciences Department of Computer Science Greater Noida India Dronacharya Group of Institutions Department of Computer Science Greater Noida India

ISBN: (纸本)9798350390179

Cloud computing is a archetype that permits users to access a shared pool of computing resources on an on-demand or pay-per-use basis. It offers numerous benefits to individuals and organizations, including significant cost savings in both capital and operational expenditures. However, there are several obstacles that prevent widespread adoption of cloud computing, with security being a major concern. Insufficient security measures can have negative consequences for this computing model, consequential in individual, ethical, and economic harm. The security issues that various cloud entities - such as cloud facility providers, data holders, and cloud users - face will be covered in this paper. It will specifically focus on the crypto-cloud, which encompasses various aspects of announcement, multiplication, and service level agreements (SLAs). By examining the causes and effects of different cyber-attacks, the paper aims to propose necessary security enhancements. © 2024 IEEE.

关键词： Cloud computing Crypto-cloud Issues in cloud Virtualization Security challenges

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Customer Churn Prediction in Subscription-Based Services 5th

Customer Churn Prediction in Subscription-Based Services

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5th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Kolli, Mahi Varadharajan, Nayantara Ajith, Keerthana Dinesh Kumar, K. Department of Computer Science and Engineering Amrita School of Computing AmritaVishwaVidyapeetham Bangalore India

ISBN: (纸本)9789819788644

The rapid growth of subscription-based services in the telecom industry has led to a larger subscriber base for service vendors. However, customer churn, or the loss of clients, has become a critical issue for telecom companies. This paper addresses the problem of customer churn or attrition in the telecommunication industry and performs a churn prediction model that implements Machine Learning techniques on a big data platform. The study employs key algorithms, including Logistic Regression, Decision Tree, KNN Classifier, Random Forest and Support Vector Machine (RBF), to identify potential defectors and enable companies to implement retention techniques. This paper focuses on voluntary churn and implements engineering features and selection methods to improve predictive accuracy. As markets expand and customer acquisition costs increase, effective customer retention becomes paramount, prompting service companies to invest in prediction analytics for proactive churn management. The study leverages prior research and experiments with multiple machine-learning algorithms to build a predictive model of customer churn. The utilized model uses big data and innovative feature engineering and selection methods to predict customers who are most likely to churn. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Telecommunication industry

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Redundant data Detection and Deletion to Meet Privacy Protection Requirements in Blockchain-Based Edge Computing Environment

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China Communications 2024年第3期21卷 149-159页

作者： Zhang Lejun Peng Minghui Su Shen Wang Weizheng Jin Zilong Su Yansen Chen Huiling Guo Ran Sergey Gataullin Cyberspace Institute Advanced Technology Guangzhou UniversityGuangzhou 510006China College of Information Engineering Yangzhou UniversityYangzhou 225127China School Math&Computer Science Quanzhou Normal UniversityQuanzhou 362000China Computer Science Department City University of Hong KongHong Kong 999077China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 21004China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230601China Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035China Guangzhou University Library Guangzhou UniversityGuangzhou 510006China Central Economic and Mathematics Institute Russian Academy of Sciences MIREA-Russian Technological University Moscow RegionRussia

With the rapid development of information technology,IoT devices play a huge role in physiological health data *** exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge *** storage capacity of edge nodes close to users is *** should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging *** paper proposes a redundant data detection method that meets the privacy protection *** scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot *** has the same effect as zero-knowledge proof,and it will not reveal the privacy of *** addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the *** use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is *** feasibility of the scheme is proved through safety analysis and efficiency analysis.

关键词： blockchain data integrity edge computing privacy protection redundant data

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Robust Hybrid Machine Learning Model for Financial Fraud Detection in Credit Card Transactions 2

Robust Hybrid Machine Learning Model for Financial Fraud Det...

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

作者： Jahnavi, Dingari Mona, A. Pulata, Sandeep Sami, Sasank Vakamullu, Bharadwaj Mohan Bharathi, G. Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350327533

Financial fraud is a rising problem that affects both organizations and people, necessitating cutting-edge solutions to lessen its effects. The majority of machine learning models used now in the field of fraud detection include Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF). While these traditional models frequently lack sensitivity, have trouble figuring out complex fraud patterns, and are rigid in their ability to adjust to new threats. To overcome them, this research work provides a significant advancement by combining Decision Trees and Logistic Regression into a hybrid model, boasting an impressive accuracy of 98.1%. The proposed methodology not only improves the system's ability to detect fraud accurately but also makes it more flexible to the changing fraud strategies. Additionally, a thorough comparison analysis highlights the approach's undeniable benefits. In a nutshell, this work provides a method for detecting financial fraud using a hybrid model that expertly strikes a compromise between sensitivity and accuracy. In addition to improving fraud detection, this innovation also strengthens the security of financial systems. © 2024 IEEE.

关键词： Comparison analysis Contemporary world Financial fraud Flexibility Fraudulent transactions Hybrid model

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DoS Attack Detection Using Machine Learning 7

DoS Attack Detection Using Machine Learning

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7th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2024

作者： Kosasih, Michael Gunawan, Stanley Figo Immanuel, Surya Junior, Franz Adeta Maulina, Ayu Bina Nusantara University School of Computer Science Cyber Security Program Computer Science Department Jakarta Indonesia Bina Nusantara University School of Computer Sciene Computer Sciene Department Jakarta Indonesia

ISBN: (纸本)9798331519643

This research explores the detection of Denial of Service (DoS) attacks using a machine learning approach. DoS attacks aim to make internet services unavailable, which negatively impacts individuals and organizations. Intrusion Detection System (IDS) has long been used as a method of preventing DoS attacks. In previous studies, real-time implementation of ML IDS has not been conducted. Therefore, in this research, the Decision Tree algorithm is combined with the ensemble bagging method, and the data is processed using the NearMiss undersampling technique. The CICIDS2017 dataset is used as the DoS attack database. The test results show that the machine learning-based IDS achieves an accuracy of 97.5% and a recall of 99.5%, which shows higher performance compared to the traditional IDS, which achieves an accuracy of 17.5% and a recall of 19.5%. Furthermore, the research concludes that the Bagging Decision Tree with the NearMiss balancing method is the most effective approach, achieving an accuracy of 0.99 and a recall of 0.89. While Snort is more precise in identifying attacks, it may fail to detect them entirely. On the other hand, the machine learning-based IDS proves to be more sensitive to attacks, leading to better detection, though it carries a higher risk of false positives. The results demonstrate that the machine learning-based IDS significantly outperforms traditional methods in terms of accuracy and recall, making it a more reliable tool for detecting DoS attacks and reducing the risk of undetected threats. © 2024 IEEE.

关键词： Denial of Service (DoS) Detection Intrusion Detection System (IDS) Machine Learning Security

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Object Detection in Shelf Image of the Retail store using Single Stage Detector 2

Object Detection in Shelf Image of the Retail store using Si...

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2nd IEEE International Conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2024

作者： Bagyammal, T. Mahesh Madhav, K. Dhiraj Kumar, Y. Arthi Sri, N. Siva Srinivas, V. Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350354461

ISBN: (纸本)9798350354461

The primary objective is to develop a robust system for precise object identification on retail shelves, accurate item counting, and product categorization through class detection. This multifaceted approach directly tackles critical issues in retail industry, encompassing inventory management and optimized product placement. The ultimate goal is to boost operational efficiency and elevate customer satisfaction within the retail sector. The proposed methodology involves comprehensive steps, including data collection, annotation, pre-processing, and the deployment of advanced object detection models based on YOLOv5, with a particular emphasis on YOLO due to its real-time capabilities and being known as a single stage detector. By achieving these milestones, the proposed model promises to provide valuable solutions for the retail industry, reducing out-of-stock situations, minimizing manual labour, and delivering enhanced product organization. Thus, ensuring good performance compared to its other object detectors of the same use case. We have modified the architecture at different levels of the State-of-the-art YOLOv5 (Backbone, Head) and compared the models implemented for inference. We found out that Soft-IoU layer added in the head has given the best performance in detecting object with mean average precision of 79.7 © 2024 IEEE.

关键词： Customer satisfaction

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A Real Time Prediction Model for Round Winners of Counter Strike:Global Offensive 3

A Real Time Prediction Model for Round Winners of Counter St...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Paul, Rosebell Sajeer, Rihan Vaishnav, M.V. Abraham, Savio Vinu Sahajan, Shreya SCMS School of Engineering and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350396157

This research study aims to create a full time winner predictor program for Counter-Strike Global Offensive tournaments that can predict the round winner of the game being spectated. The algorithm creates accurate predictions of the round winner. The data required for the prediction has to be extracted from the game servers. The data is collected from the game servers using an API. The prediction can be viewed live by the user with the help of a GUI. The project can provide CS:GO enthusiasts and bettors with real-time insights and predictions for round winners in competitive matches. System aims to enhance the user's understanding of match dynamics and support informed decision making. © 2024 IEEE.

关键词： Forecasting

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Leveraging Group Classification with Descending Soft Labeling for Deep Imbalanced Regression 39

Leveraging Group Classification with Descending Soft Labelin...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Pu, Ruizhi Xu, Gezheng Fang, Ruiyi Bao, Bing-Kun Ling, Charles Wang, Boyu Department of Computer Sceince Western University Canada School of Computer Science Nanjing University of Posts and Telecommunications China

ISBN: (纸本)157735897X

Deep imbalanced regression (DIR), where the target values have a highly skewed distribution and are also continuous, is an intriguing yet under-explored problem in machine learning. While recent works have already shown that incorporating various classification-based regularizers can produce enhanced outcomes, the role of classification remains elusive in DIR. Moreover, such regularizers (e.g., contrastive penalties) merely focus on learning discriminative features of data, which inevitably results in ignorance of either continuity or similarity across the data. To address these issues, we first bridge the connection between the objectives of DIR and classification from a Bayesian perspective. Consequently, this motivates us to decompose the objective of DIR into a combination of classification and regression tasks, which naturally guides us toward a divide-and-conquer manner to solve the DIR problem. Specifically, by aggregating the data at nearby labels into the same groups, we introduce an ordinal group-aware contrastive learning loss along with a multi-experts regressor to tackle the different groups of data thereby maintaining the data continuity. Meanwhile, considering the similarity between the groups, we also propose a symmetric descending soft labeling strategy to exploit the intrinsic similarity across the data, which allows classification to facilitate regression more effectively. Extensive experiments on real-world datasets also validate the effectiveness of our method. Copyright © 2025, Association for the Advancement of Artificia Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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Exploring EfficientNet Variants for Image Encoding in Auxiliary Signal Guided Knowledge Encoder-Decoder Framework 9th

Exploring EfficientNet Variants for Image Encoding in Auxili...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Henry, Matthew Martianus Heryanto, Nur Adhianti Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

The growing number of medical images has led to radiologist burnout, which seriously impacts the radiologist's performance. To address the previously mentioned issue, an Auxiliary Signal Guided Knowledge (ASGK) multimodal encoder-decoder framework was designed to automatically generate the medical report based on the proposed medical graph and natural language decoder. It utilizes DenseNet-121 as the image encoder. With DenseNet-121 lack of computational and memory efficiency, this study aims to explore the potential of EfficientNetB0 to EfficientNetB4 as an ASGK image encoder substitute. The framework is trained with IU X-Ray dataset for 30 epochs, with Adam optimizer, a learning rate of 0.01 with 0.8 decay rate, binary cross entropy loss for the medical tags, and cross-entropy loss for the generated medical captions. During the framework training process with each image encoder, the parameter that achieves the highest CIDEr score on the validation set is considered the best image encoder parameter and will be used on the test set. On the test set, EfficientNetB3 as an ASGK image encoder has been shown to increase the CIDEr score to 0.35, a significant increase from the 0.28 CIDEr score obtained by the ASGK using DenseNet-121. This score is only a 1% decrease from the best validation score. It suggests that not only EfficientNetB3 increases the framework's performance, it is also less prone to overfitting. This study has demonstrated that EfficientNetB3 is a potential image encoder substitute for DenseNet-121 in the ASGK framework. © 2024 The Authors.

关键词： Medical imaging

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