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Advance Keylogger - Capturing Keystrokes 6

Advance Keylogger - Capturing Keystrokes

引用

6th IEEE international conference on Advances in Science and technology, ICAST 2023

作者： Shirke, Aditya Pawar, Radhika Bivalkar, Mandar Waghela, Harsh Shah, Zenith K. J. Somaiya Institute of Technology Department of Computer Engineering Maharashtra Mumbai India

ISBN: (纸本)9798350359817

This research explores the development and ethical considerations surrounding an advanced keylogger designed for parental control and employee monitoring. We present the technical aspects of our keylogger, emphasizing its keystroke-capturing features and tailored functionalities for monitoring. Ethical concerns regarding informed consent, privacy, and responsible usage are addressed, along with legal implications in the surveillance landscape. Results from testing and experimentation are presented and analyzed within the context of our research question, highlighting the potential benefits and challenges associated with this technology. This paper stresses the need for a balanced approach, considering technological advancement and ethical responsibility while complying with legal frameworks. It highlights the potential advantages of these tools for enhancing the safety and security of children and organizations in the digital era. © 2023 IEEE.

关键词： Python

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Simple Additive Weighting in the development of a Decision Support System for the Selection of House Construction Project Teams

Simple Additive Weighting in the Development of a Decision S...

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2023 international conference on computer Science, Information technology and Engineering, ICCoSITE 2023

作者： Aulawi, Hilmi Nuraeni, Fitri Setiawan, Ridwan Rianto, Wiby Fabian Surya Pratama, Adhitya Maulana, Helmi Institut Teknologi Garut Department of Industrial Engineering Garut Indonesia Institut Teknologi Garut Department of Computer Science Garut Indonesia Institut Teknologi Garut Department of Architect Garut Indonesia

ISBN: (纸本)9798350320954

Every time they get a project, planning consultants and building implementers always work with contractors to work on the project. However, in project work, there are often several obstacles to the selected contractor, such as delays in work, workmanship not according to plan, and inappropriate building specifications to unskilled experts. This happens because the contractor selection process that is not right causes the construction phase to be not optimal it affects the quality of the resulting building. The selection of contractors involves a complex multi-criteria where each criterion used has different importance and the information about it is not known precisely so a method is needed to overcome these problems. For this reason, it is necessary to have a system in supporting contractor selection decisions, known as the Decision Support System. This system was built using the Rapid Application development (RAD) design method with the stages of Requirements Planning, User Design, Construction, and Cutover. The construction of this DSS uses one of the methods in making decisions, namely Simple Additive Weighting (SAW) to make an assessment based on predetermined decision-making criteria. From this research, a website-based decision support system was obtained using the javascript programming language, with a score of 79.7% for the ease of use of the system, and a 93.3% score for the usefulness of the system. © 2023 IEEE.

关键词： Contractors

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Hourly Journal: A Mobile App for Optimizing Your Daily Routines 7

Hourly Journal: A Mobile App for Optimizing Your Daily Routi...

引用

7th international conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Sureshkumar, S. Suryavarshini, S. Chellammal, P. Murugan, K R. Senthil Rani, P. Kavitha Kaviyaraj, R. J.J College of Engineering and Technology CSE Trichy India Sri Krishna College of Engineering and Technology CSE Coimbatore India J.J College of Engineering and Technology Department of Computer Science and Engineering Trichy India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India SRM Institute of Science and Technology Department of Computational Intelligence Kattankulathur India

ISBN: (纸本)9798350397253

There are certain apps to record how people spent their day and whether they lived the day as per their goals. But these only make people feel guilty for having wasted the day or happy on being productive, but no app shows what is the pattern for success and failure. But with Hourly Journal it is possible to understand the pattern of daily time investments and fix it, empower it and grow in life overall. Mobile application development is becoming more popular due to the expanding popularity of smartphones and tablets. Traditional software development has had a significant influence on the development of mobile apps. However, the ultimate product is software designed to take use of mobile devices' specific capabilities and technology. To solve the problem of performance on each specific device, you must first design an app for that device. This implies writing code that is tailored to the exact hardware of a certain device. In the case of iOS devices, this is simple since mobile developers simply need to create apps for the iPhone and iPad to make their apps universally usable. For Android devices, on the other hand, the hardware and operating system versions vary from device to device. With the Hourly Journal available in the palm of your hand, people can use it as a modern-day diary, with revolutionary futuristic insights into the patterns of people's activities and tendencies. Based on tag tracking there is scope for human behavioral prediction in future too. © 2023 IEEE.

关键词： Software design

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A personalized test paper generation model for English examination based on knowledge tracking 5

A personalized test paper generation model for English exami...

引用

5th international conference on Artificial Intelligence and computer Science, AICS 2023

作者： Zhou, Ya Shao, Xiuli Huang, Guimin Guo, Qingkai Deng, Nanxiao Lin, Jianxing School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guangxi Guilin541004 China

ISBN: (纸本)9781510668621

In recent years, personalized paper grouping is a hot research topic in the field of intelligence education. In this paper, we propose an optimized deep knowledge tracking model Mul-MAKT combined with a genetic algorithm for personalized paper grouping with English practice as the target. Firstly, we use students’ records (exercise labels, response results, learning behaviors, exercise attributes) as input to the knowledge tracking model to predict students’ performance at the next moment and also to obtain students’ mastery level of knowledge points. Based on the students’ mastery levels, we adjust the students’ ability in each type of English exercise and calculate the students’ weaknesses in each type of question, so as to extract the questions from the test bank with the difficulty matching the students’ ability and containing the students’ weaknesses. © 2023 SPIE.

关键词： Students

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A New Classification Approach of Cultural Heritage: Deep Learning Based Image Recognition

A New Classification Approach of Cultural Heritage: Deep Lea...

引用

2023 international conference on Sustaining Heritage: Innovative and Digital Approaches, ICSH 2023

作者： Atas, Serhat Karakose, Mehmet Computer Engineering Department Elazig Turkey

ISBN: (纸本)9798350398281

With the development of technology, artificial intelligence is becoming gigantic and entering many areas. This development of artificial intelligence has also revealed beautiful studies by making contributions to the cultural heritage. These studies have shown that our cultural self and technology are actually not far from each other and are instrumental in useful studies. Many platforms have contributed to the development of various studies as a front for combining cultural heritage with developing artificial intelligence. In these various studies, not only artificial intelligence, but many different technologies have been used and tried to contribute to the cultural heritage. We have brought together deep learning and cultural heritage by deconstructing a study in this field. In our study, together with the special data set we have created, we have carried out a study that tries to recognize cultural heritage structures with deep learning by choosing a suitable model for our study, and we have described the contributions we have made in this study in this article. © 2023 IEEE.

关键词： Image classification

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Advancing Healthcare 5.0 through Federated Learning: Opportunity for Security Enforcement using Blockchain 32

Advancing Healthcare 5.0 through Federated Learning: Opportu...

引用

32nd international conference on Software, Telecommunications and computer Networks, SoftCOM 2024

作者： Mathew, Ugochukwu O. Rodriguez, Demostenes Zegarra Rosa, Renata Lopes Ayub, Muhammad Shoaib Adasme, Pablo Federal University of Lavras Brazil Department of Computer Science Lavras Brazil Universidad de Santiago de Chile Department of Electrical Engineering Santiago Chile

ISBN: (纸本)9789532901382

As Healthcare 5.0 becomes more widely accepted, the healthcare industry in general is changing, leading to better patient care and more system efficiency. The degree of digital automation that introduced cutting-edge technology applications to the health sector is seriously challenged by this shift. Concerns over patient's privacy and security are heightened by the rising digitization of healthcare institutions, which makes it harder to collaborate and share data seamlessly. The exponential growth of healthcare data necessitates effective processing and analysis in order to maximize patient outcomes and healthcare delivery. To fully realize the potential of Healthcare 5.0, healthcare practitioners, information technology specialists, data scientists, and researchers must address these complex security difficulties. Healthcare 5.0 provides a comprehensive solution to the pressing issues facing the digitalized healthcare industry by thoroughly examining its foundational principles and exploring its practical implementation through cloud computing, data analytics, and federated learning. Through healthcare federated learning, the national electronic health (e-health) policy and strategy will be legitimized. In this paper, the authors categorized and combined healthcare technology with federated learning artificial intelligence (AI) at various automation stages. The paper discussed the issues that are now facing the healthcare industry, such as security, privacy and dependability. The paper provided readers with guidance on how to use AI and federated learning to solve healthcare information system synchronization. In conclusion, the authors discussed broad security topics and future research in the healthcare management system utilizing federated learning and blockchain approach. © 2024 University of Split, FESB.

关键词： Electronic health record

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A Novel Real-Time Web Based Intrusion Detection System (IDS) 4

A Novel Real-Time Web Based Intrusion Detection System (IDS)

引用

4th international Multidisciplinary Information technology and Engineering conference, IMITEC 2024

作者： Tamuka, Nyashadzashe Sibanda, Khulumani University of Fort Hare Department of Computer Science Eastern Cape Alice South Africa Walter Sisulu University Department of Applied Informatics Eastern Cape East London South Africa

ISBN: (纸本)9798350387988

The exponential growth of web applications employing scripting languages like PHP and JavaScript has brought about significant security challenges, particularly in terms of software vulnerabilities. Traditional Intrusion Detection Systems (IDS) often struggle to keep pace with the rapid evolution of web applications, especially when it comes to detecting zero-day attacks in real-time. Furthermore, these studies necessitate huge datasets for intrusion detection. This study proposed a proactive, real-time IDS specifically designed to address these challenges. Utilizing the agile methodology for iterative development, the IDS was implemented using PHP and tested against common web attacks, achieving an impressive 92% accuracy in detecting intrusions. The system's real-time capabilities were further enhanced by its ability to notify administrators immediately via email, thus providing a dynamic and robust solution to web security threats. © 2024 IEEE.

关键词： Network intrusion

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GWO-CNN: Trust and Intelligent Recommendation system based model to improve marketing strategies on e-commerce 2

GWO-CNN: Trust and Intelligent Recommendation system based m...

引用

2nd IEEE international conference for Women in Innovation, technology and Entrepreneurship, ICWITE 2024

作者： Sethi, Vikas Kumar, Rajneesh Mehla, Stuti Department of Computer Science and Engineering Mullana Ambala India Panipat Institute of Engineering and Technology Department of Computer Science and Engineering Panipat India

ISBN: (纸本)9798350383287

In 21st century, AI-based intelligent recommendation system uses rating predictions, which are frequently utilized and helps users swiftly filter down their options and make informed judgements from an abundance of material. Nevertheless, as recommender systems only contain a small number of explicit data and are subject to cold start and sparsity issues, the majority of existing recommendation models have poor accuracy. The majority of conventional algorithms used to make recommendations simply take into account the straight-forward connection among consumers and products, largely ignoring the interests, prospective employment factors, or other social aspects of the products being *** this study focus is on user preferences, as they are key to tailoring recommendations to individual needs. By combining trust values with user preferences, the system can recommend items that not only have a high trust value but also align with a user's individual tastes or needs. For this proposed model used feature matrix, Grey Wolf Optimizer (GWO) and Convolution Neural Network (CNN). GWO is an optimization algorithm used to find optimal weights or parameters for the recommendation system. Once the GWO has found optimal or near-optimal weights, they are integrated into the feature matrix. These weights can help prioritize or scale certain features to improve the recommendation quality. Then CNN will extract patterns and relationships from the feature matrix. Based on the processed feature matrix, the system predicts the top 'K' items to recommend to the user. © 2024 IEEE.

关键词： Recommender systems

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Enhancing Cyber Security Through Phish-Net -An Artificial Neural Network for Phishing Detection 2

Enhancing Cyber Security Through Phish-Net -An Artificial Ne...

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2nd IEEE international conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Diviya, M. Jyoshika Shri, K. Gopala Krishnan, D. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9798350372892

The widespread development of internet technology has made life easier in many ways, such as social networking, online banking, and information retrieval. But this growth has also brought forth a multitude of security risks, one of which is phishing, which has become a major worry. Phishing attacks employ websites that seem similar to authentic ones to obtain sensitive user data illegally, especially by using phony URLs. Finding malicious URLs is one of cybersecurity's biggest challenges. The potential losses in money resulting from phishing make it imperative to create efficient methods for detecting phishing websites. To tackle such problems, Phish-Net has been proposed using 3-layered Artificial Neural Network by fine tuning the hyperparameter that is used to catch the phishing sites and also assessed the classifier's effectiveness using a large dataset. The suggested strategy surpasses current approaches in accuracy, obtaining 98.0% and further study in this field can enhance their functionality and aid in protecting against online dangers. © 2024 IEEE.

关键词： Phishing

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Optimizing Network Management and Virtualization Using Machine Learning Approach: Network Slice Prediction

Optimizing Network Management and Virtualization Using Machi...

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2024 international conference on Science, Engineering and Business for Driving Sustainable development Goals, SEB4SDG 2024

作者： Akinyemi, Lateef Adesola Oshinuga, Olamide Peter Oladejo, Sunday Oladayo Ekwe, Stephen Obono Sumbwanyanbe, Mbuyu Mnkandla, Ernest Shoewu, Oluwagbemiga Omotayo University of South Africa Departments of Computer Science and Elect. Engineering CSET Johannesburg South Africa Lagos State University Department of Electronic and Computer Engineering Lagos Nigeria Stellenbosch University School for Data Science and Computational Thinking Stellenbosch South Africa Cape Peninsula University of Technology Department of Elect. Electronic and Computer Engineering Cape Town South Africa University of South Africa Department of Electrical Engineering CSET Johannesburg South Africa University of South Africa Department for Computer Science CSET Johannesburg South Africa

ISBN: (纸本)9798350358155

This study demonstrates the utilization of Machine Learning (ML) for network slice prediction, enabling the optimization of resources for diverse network slices. Traditional methods for network slice prediction often lack efficiency and result in inaccuracies. By leveraging ML algorithms such as Naive Bayes and Random Forest, an intelligent framework that automates network slice prediction is developed. This framework enhances network virtualization and management, facilitating resource allocation. The ML algorithms take real-time network conditions and usages, such as packet delay and smart city, as input and output for selecting the most suitable network slice. Data analysis is further conducted to reveal the connections between the input parameters and how these parameters influence the selection of the accurate network slice. Network slicing plays a crucial role as it enables the customization of services and facilitates efficient scaling to meet the specific needs of different applications and industries. The accuracy scores of the employed ML algorithms were generally perfect, except for the KNN and SVM classifiers, which achieved an accuracy of 94.30% and 92.16%, respectively, for the prediction of network slices based on incoming network connections and usages. © 2024 IEEE.

关键词： Data Analysis Machine Learning Network Management Network Slicing Network Virtualization

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