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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Bhuvana, J. Srivastav, Vaibhav Kumar, Sunil Department of Computer Science and Information Technology Bangalore India Computer Science & Engineering Vivekananda Global University Jaipur India College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

The Recurrent Neural Networks (RNNs) software for scientific image analysis poses both an assignment and an opportunity to discover the spatiotemporal context to perceive significant patterns within scientific images. RNNs are capable of capturing lengthy-term dependencies in high-dimensional statistics, which has made them appropriate for use in clinical photograph analysis. additionally, they own a hierarchical architecture making it feasible to capture each low-level and excessive-level capabilities in a unmarried version. As such, RNNs can be used to extract spatiotemporal features from scientific images and contain the understanding of preceding frames with current frames to refine the know-how of a given frame. it allows RNNs to focus the eye of the version on applicable regions within the photo that denote a circumstance of hobby or to combine temporal statistics over a sequence of a couple of snapshots related to a particular affected person. This paper will discover the unique strategies and opportunities for utilizing RNNs for clinical photo evaluation and discuss a number of the demanding situations related to this approach. © 2024 IEEE.

关键词： associated hierarchical incorporate information possibilities

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Detection of Distributed Denial of Service in The Application Layer of IoT Using Machine Learning 15

Detection of Distributed Denial of Service in The Applicatio...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Nanthiya, D. Gopal, S.B. Manjula Devi, R. Praveenkumar, R. Vijayakumar, S.D. Raja, T. Dharanish Sriram, L. Kongu Engineering college Department of Computer Technology -UG Perundurai India Kongu Engineering College Department of Electronics and Communication Engineering Perundurai India Kpr institute of Engineering and Technology Department of Computer science and Engineering Coimbatore India Builders Engineering College Department of Electronics and Communication Engineering Kangeyam India Vivekanandha college of Technology for women Department of Electronics and Communication Engineering Tiruchengode India

ISBN: (纸本)9798350370249

Cybersecurity prevents harmful assaults on computers, networks, and mobile devices. Included in the category of cyber-attacks are threats like as malware, distributed denial of service assaults, phishing, spoofing, code injection, and supply chain attacks. Massive denial-of-service assaults are destructive. As part of a Distributed Denial-of-Service (DDoS) assault, hackers bypass botnets and go straight for the server. In order to temporarily block or suspend a website, these botnets send a flood of requests to the server. The server typically takes requests and returns responses. There will be a brief outage if the server gets more requests than it can handle. While this attack makes the service inaccessible, it prevents data theft. A machine-learning approach is used to detect server-side DDoS assaults. The term 'machine learning' refers to software that can learn new tasks and solve old ones without any input from a human. Learning Vector Quantization, Support Vector Machine, Naive Bayes, K-Nearest Neighbour, Random Forest, and Decision Tree are the six ML classification models used since these ML procedures enhance accuracy. The main goal of this research is to use the CIDAD dataset to find ways to protect the application layer of the Internet of Things (IoT) against Distributed Denial of Service (DDoS) attacks. The lower layers of the IoT network architecture are disregarded by this dataset, which comprises information at the COAP level. © 2024 IEEE.

关键词： CIDAD Cyber security Cyber-attacks DDoS attacks

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SSDRec: Self-Augmented Sequence Denoising for Sequential Recommendation 40

SSDRec: Self-Augmented Sequence Denoising for Sequential Rec...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Zhang, Chi Han, Qilong Chen, Rui Zhao, Xiangyu Tang, Peng Song, Hongtao College of Computer Science and Technology Harbin Engineering University China School of Data Science City University of Hong Kong Hong Kong School of Cyber Science and Technology Shandong University China

ISBN: (纸本)9798350317152

Traditional sequential recommendation methods assume that users' sequence data is clean enough to learn accurate sequence representations to reflect user preferences. In practice, users' sequences inevitably contain noise (e.g., accidental interactions), leading to incorrect reflections of user preferences. Consequently, some pioneer studies have explored modeling sequentiality and correlations in sequences to implicitly or explicitly reduce noise's influence. However, relying on only available intra-sequence information (i.e., sequentiality and correlations in a sequence) is insufficient and may result in over-denoising and under-denoising problems (OUPs), especially for short sequences. To improve reliability, we propose to augment sequences by inserting items before denoising. However, due to the data sparsity issue and computational costs, it is challenging to select proper items from the entire item universe to insert into proper positions in a target sequence. Motivated by the above observation, we propose a novel framework-Self-augmented Sequence Denoising for sequential Recommendation (SSDRec) with a three-stage learning paradigm to solve the above challenges. In the first stage, we empower SSDRec by a global relation encoder to learn multi-faceted inter-sequence relations in a data-driven manner. These relations serve as prior knowledge to guide subsequent stages. In the second stage, we devise a self-augmentation module to augment sequences to alleviate OUPs. Finally, we employ a hierarchical denoising module in the third stage to reduce the risk of false augmentations and pinpoint all noise in raw sequences. Extensive experiments on five real-world datasets demonstrate the superiority of SSDRec over state-of-the-art denoising methods and its flexible applications to mainstream sequential recommendation models. The source code is available online at https://***/zc-97/SSDRec. © 2024 IEEE.

关键词： User profile

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Deployment Strategies for Wireless Sensor Networks Using Math Modeling Techniques

Deployment Strategies for Wireless Sensor Networks Using Mat...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Sahal, Nisha Murugan, R. Raizada, Aasheesh College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Sanskriti University Department of Computer Science Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9798350383348

Wi-Fi sensor networks comprise small, low-value, low-energy sensors configured to interface with the physical surroundings. Due to their constrained power, wireless sensor networks should be deployed and operated in a distributed way. It affords an undertaking for many obligations, which include topology manipulation, energy-green routing, and coverage tracking. This paper proposes numerous deployment strategies for wireless sensor networks using mathematical modeling strategies. Mainly, the proposed techniques use convex optimization and integer programming to research the trade-offs between network insurance, power intake, interference, and topology. Simulation effects display the effectiveness of the proposed techniques in enhancing network overall performance. The paper also discusses extending those strategies to metaheuristics further to improve the scalability and applicability of the proposed method. © 2024 IEEE.

关键词： constrained networks obligations optimization surroundings

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Enhanced Health Monitoring System for Internet of Medical Things using Machine Learning 15

Enhanced Health Monitoring System for Internet of Medical Th...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Swedheetha, C. Manikandan, P. Rajalakshmi, J. Muthuraja, T. Kavin Kumar, K. Chandrasekaran, Gokul Sriram Srivathsan, N. Velammal College of Engineering and Technology Department of Computer Science and Engineering India Kalasalingam Academy of Research and Education Krishnankoil Department of Electronics and Communication Engineering Virudhunagar India Sethu Institute of Technology Department of Electronics and Communication Engineering Virudhnagar India Kongu Engineering College Perundurai Department of Electronics and Communication Engineering Erode638060 India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350370249

This brief looks at mobile operation, speech recognition, and voice robotization. Patients with physical limitations, such as Parkinson's disease and cerebral palsy, as well as those with speech impairments, can express their requirements with this application. The initiative enhances the general well-being and independence of people with impairments. Increasing the automation of current technologies is the main objective of this design. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Optimizing Purchase Intention Prediction in E-Commerce 1

Optimizing Purchase Intention Prediction in E-Commerce

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Jayanthi, S. Rajeshwari, D. Goud, Naresh M. Geetha, R. Franklin, S. Benjamin Rajyalakshmi, Pannangi Department of Artificial Intelligence &data Science Telangana India Sri Indu Institute of Engineering and Technology Department of CSE Telangana Hyderabad India Vardhaman College of Engineering Department of Computer Science and Engineering Telangana Hyderabad India Bengaluru India Faculty of Behavioural and Social Sciences Department of Social Work Chennai India TKR College of Engineering and Technology Department of CSE Hyderabad India

ISBN: (纸本)9798331518943

This study investigates the effectiveness of deep learning models - Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM - in predicting purchase intention on e-commerce platforms using session-level data. The dataset, sourced from the UCI Machine Learning Repository, comprises 12,330 sessions with features such as 'PageValues,' 'BounceRates,' 'ExitRates,' and user behavior attributes like 'Month' and 'VisitorType.' After preprocessing categorical variables using Label Encoding and standardizing numerical features, the data was split into an 80-20% training and testing set. Each of the five models was then trained and assessed using metrics such as accuracy, ROC-AUC, and other classification measures. Among the models, the CNN achieved the highest performance with an accuracy of 90.34% and a ROC-AUC score of 0.9156, followed by the LSTM model with an accuracy of 89.72% and a ROC-AUC score of 0.9102. Although the RNN model performed well, it showed relatively lower metrics with an accuracy of 88.40% and a ROC-AUC of 0.8934. 'PageValues' exhibited the strongest positive correlation with purchase intention (Pearson correlation: 0.4926, Spearman correlation: 0.6264), while 'ExitRates' and 'BounceRates' had significant negative correlations of -0.2071 and -0.1507, respectively. This study identifies CNN and LSTM as robust models for predicting purchase intentions in e-commerce, with 'PageValues' being the most influential predictor. The results provide valuable insights for optimizing e-commerce platforms to improve conversion rates. Future research will focus on external validation and expanding the dataset to ensure broader applicability across various e-commerce contexts. © 2024 IEEE.

关键词： Convolutional neural networks

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Image Hiding with High Robustness Based on Dynamic Region Attention in the Wavelet Domain

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computer Modeling in engineering & sciences 2024年第10期141卷 847-869页

作者： Zengxiang Li Yongchong Wu Alanoud Al Mazroa Donghua Jiang Jianhua Wu Xishun Zhu Department of Information Engineering Gongqing CollegeNanchang UniversityJiujiang332020China School of Information Engineering Gongqing Institute of Science and TechnologyJiujiang332020China Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou511400China School of Information Engineering Nanchang UniversityNanchang330031China School of Mathematics and Statistics Hainan Normal UniversityHaikou571158China

Hidden capacity,concealment,security,and robustness are essential indicators of hiding ***,hiding algorithms tend to focus on algorithmic capacity,concealment,and security but often overlook the robustness of the *** practical applications,the container can suffer from damage caused by noise,cropping,and other attacks during transmission,resulting in challenging or even impossible complete recovery of the secret *** image hiding algorithm based on dynamic region attention in the multi-scale wavelet domain is proposed to address this issue and enhance the robustness of hiding *** this proposed algorithm,a secret image of size 256×256 is first decomposed using an eight-level Haar wavelet *** wavelet transform generates one coefficient in the approximation component and twenty-four detail bands,which are then embedded into the carrier image via a hiding *** the recovery process,the container image is divided into four non-overlapping parts,each employed to reconstruct a low-resolution secret *** lowresolution secret images are combined using densemodules to obtain a high-quality secret *** experimental results showed that even under destructive attacks on the container image,the proposed algorithm is successful in recovering a high-quality secret image,indicating that the algorithm exhibits a high degree of robustness against various *** proposed algorithm effectively addresses the robustness issue by incorporating both spatial and channel attention mechanisms in the multi-scale wavelet domain,making it suitable for practical *** conclusion,the image hiding algorithm introduced in this study offers significant improvements in robustness compared to existing *** ability to recover high-quality secret images even in the presence of destructive attacksmakes it an attractive option for various *** research and experimentation can explore the algorith

关键词： Image hiding robustness wavelet transform dynamic region attention

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Predicting Software Errors Using Neural Networks 12

Predicting Software Errors Using Neural Networks

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12th International Conference on System Modeling and Advancement in Research Trends, SMART 2023

作者： Pandey, Ratnesh Kumar Chand, Jay Srivastava, Ashish Kumar Singh, Narendra Pal Gautam, Rakesh Kumar Invertis University Department of Computer Science & Engineering UP Bareilly India Kamla Nehru Institute of Physical and Social Sciences Department of Computer Science & Engineering UP Sultanpur India Madyanchal Professional University Department of Computer Science & Engineering MP Bhopal India Iimt College of Engineering Department of Computer Science & Engineering UP Greater Noida India Iet Department of Computer Science & Engineering Department of Information Technology UP Lucknow India

ISBN: (纸本)9798350369861

The growing call for online technology software has additionally elevated the call for great and occasional-cost merchandise. Predicting software vulnerabilities is one of the most essential obligations of a very good security gadget. Growth nice at low cost by decreasing ordinary testing and preservation efforts. Early detection of defects in the software program improvement existence Cycle (SDLC) leads to early improvement and transport in the closing period of software program control, which leads to consumer delight and gives self assurance to the development group. in the beyond decade, many machine learning-primarily based blunders prediction software program were proposed to attain excessive accuracy. synthetic Neural Networks (ANN) are considered one of the system gaining knowledge of techniques and encompass maximum of the prediction schemes and models. ∗ This examine provides a vital analysis of the modern facts published due to the fact that 20152018. On the usage of neural networks for software program prediction. This take a look at is based totally on the studies system information from 3 widely used digital libraries (which include IEEE, Elsevier and Springer) and then conducted the subsequent take a look at. 8 most crucial articles were selected in the course of the entire method. studies courses are examined meticulously. ∗ This paintings will help researchers look into the modern-day country of software disorder prediction through focusing on neural networks and provide benchmarks for destiny trends. This white paper uses device getting to know and deep learning to find out the quality device gaining knowledge of algorithms for software defect prediction (SEP). This have a look at uses the NASA Metric statistics program (MDP) dataset to evaluate and predict the overall performance and accuracy of deep getting to know and machine getting to know fashions. the 2 mastering algorithms used are help vector machine (SVM) and logistic regression. Two deep

关键词： Forecasting

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A Unified Framework for Convergence Analysis in Social Networks 63

A Unified Framework for Convergence Analysis in Social Netwo...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Lee, Ti-Chung Huang, Jun-Kai Su, Youfeng National Sun Yat-sen University Department of Electrical Engineering Kaohsiung80424 Taiwan Fuzhou University College of Computer and Data Science Fuzhou350116 China

ISBN: (纸本)9798350316339

This paper proposes a unified framework for the stability analysis of discrete-time nonlinear systems from social networks, including the Friedkin-Johnsen opinion model, two opinion dynamics models in the study of social power, and a general nonlinear polar opinion model. Three novel convergence results are proposed to treat various conditions based on LaSalle invariance principle. Several applications are provided to illustrate the power of the proposed framework. © 2024 IEEE.

关键词： Nonlinear simulations

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Review of Machine Learning Approaches and Challenges for Detecting Phishing Attacks 15

Review of Machine Learning Approaches and Challenges for Det...

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

作者： Hiregoudar, Rudragouda Saboji, S.V. Department of Computer Science and Engineering Basaveshwar Engineering College Bagalkot587104 India Visvesvaraya Technological University Belagavi590018 India

ISBN: (纸本)9798331300579

Currently, the practise of "phishing" involves sending emails that seem to be from reputable companies to deceive recipients into divulging personal information, including passwords and credit card numbers. In "phishing" an attacker delivers a malicious link that is typically passed off as a legitimate one via spam emails or social media. This study includes a literature overview to fully comprehend the phishing assault(s), in this paper reviewed machine learning, deep learning, hybrid learning, and scenario-based approaches for phishing attack detection. In this paper, comparative analysis performed on various phishing detection methods. © Grenze Scientific Society, 2024.

关键词： Phishing

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