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共 232 条记录，以下是31-40 订阅

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Empowering Precision machine: Deep learning Technique for Lung Cancer Diagnosis 2

Empowering Precision Machine: Deep Learning Technique for Lu...

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2nd international conference on Computing and Data Analytics, ICCDA 2024

作者： Jamil, Danish Shah, Syed Mehr Ali Khan, Muhammad Numan Ali Aljarwani, Fatma Mahfoodh Mohammed University of Santiago de Compostela Spain Sir Syed University of Engineering & Technology Department of Software Engineering India College of Computing & Information Sciences University of Technology and Applied Sciences Musandam Oman

ISBN: (纸本)9798331542573

This research paper delves into the realm of lung cancer diagnosis, a critical area in modern healthcare owing to its substantial global impact. Leveraging cutting-edge technology and machine learning methodologies, specifically employing the VGG16 model, our study aims to enhance the early detection of lung cancer through the analysis of CT scan images. The dataset utilized comprises CT scan images sourced from the Lung Image Database Consortium (LIDC/IDRI) Image Dataset, categorized into three classes: malignant, benign, and normal. By harnessing the capabilities of the VGG16 model, we seek to accurately predict the classification of CT scan images into these distinct categories. Through rigorous experimentation and analysis, our findings shed light on the efficacy of this approach in aiding medical professionals in the timely identification and classification of lung cancer cases. Ultimately, our research contributes to advancing the field of medical imaging and underscores the potential of deep learning in revolutionizing healthcare diagnostics. © 2024 IEEE.

关键词： Lung cancer

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UUT: Unveiling Unknown Threats with machine learning for Exploit Detection 2

UUT: Unveiling Unknown Threats with Machine Learning for Exp...

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2nd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Khan, Aadil Sharma, Ishu Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350327533

In cybersecurity, attack detection indicates finding and countering efforts to take advantage of weak spots in computer systems using cutting edge technologies like AI and machine learning. Exploits are software or procedures that exploit vulnerabilities in computer systems, networks, or applications to compromise security. It's a tool or method used by attackers to exploit system flaws. software defects, configuration mistakes, and design weaknesses may be exploited. A successful exploit may provide unauthorized access, data alteration, or malicious code execution. AI quickly looks at how networks work, finds strange behavior, and spots possible security holes before they get worse. This proactive strategy enables firms to react to and decrease cyber hazards promptly, reducing their consequences and improving overall network security. Early cybersecurity vulnerability discovery is essential to minimize harm. Early exploit detection permits quick reaction, reducing cyberattack damage. Early detection helps firms fix vulnerabilities, protect sensitive data, and strengthen defenses, minimizing the chance of breaches and improving cybersecurity resilience. By trying out different machine learning methods for attack identification, this research article gives useful information. According to the results, the decision tree and XGBoost algorithms are the best at finding and reducing risks. Through its review and suggestions of strong attack detection methods, this research study helps to improve cybersecurity practices. © 2024 IEEE.

关键词： Cyber-attacks Exploits detection machine learning Network Intrusion Security unauthorized access

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Enhancing Transparency in DDoS Detection on SDN Using Explainable ML Models 2

Enhancing Transparency in DDoS Detection on SDN Using Explai...

引用

2nd international conference on Information and Communication Technology, ICICT 2024

作者： Raihan, Md. Zobair Islam, Md. Saiful Institute of Information and Communication Technology Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9798331508227

software-Defined Networking (SDN) is a networking approach that allows network administrators to define and manage network behavior through software rather than relying on physical hardware configurations. SDN can be both beneficial and challenging. The benefits include centralized management, automated detection, and efficient mitigation. SDN’s programmability enables rapid deployment of DDoS defenses and real-time analytics. However, SDN’s complexity and dependency on software can make it vulnerable to DDoS attacks. Additionally, SDN’s centralized architecture can create a single point of failure, making it crucial to design robust networks and implement proactive security measures. In this research, we aim to address the DDoS attack detection using machine learning-based methods. Our research is conducted on the CIC-DDoS2019 dataset and the result demonstrates that our optimized XGBoost model with chosen hyperparameters surpasses existing models with significant improvements in attack detection, achieving a recall score of 100%. Explainable AI (XAI) techniques were applied to provide insights into the model’s decision-making process. ©2024 IEEE.

关键词： Deep learning

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Analysis of Code Smells Using machine learning Algorithms 1

引用

2nd international conference on Modern Research in Aerospace engineering, MRAE 2023

作者： Jain, Varun Singh, Rohinish Rai, Nishant Gupta, Aakanshi Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India

ISBN: (数字)9789819713066

ISBN: (纸本)9789819713059

A code smell is a sign or indication that is introduced in the source code of a software program during the design or implementation phases, which may lead to more significant problems during software maintenance. The existing methods have limitations in extracting sufficient rich information from source code, as they frequently reflect various code smells using an easy-to-understand code representation. The objective of this research is examining the code smells with machine learning classifiers. This research suggests the use of metrics extraction software named as WekaNose for the analysis of code smells and training different machine learning models for determining the efficient algorithm. In this research we have used ML algorithms like Random forest, Decision Tree, SVM, Naive Bayes and boosted algorithms such as LightGBM and AdaBoost. The proposed approach performs remarkably well in single code smell identification according to the experimental results obtained. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： machine learning

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COLLEGEBOT: Virtual Assistant System for Enquiry Using Natural Language Processing 2

COLLEGEBOT: Virtual Assistant System for Enquiry Using Natur...

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2nd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Selvi, A. Mounika, V. Rubika, V. Uvadharanee, B. M.Kumarasamy College of Engineering Thalavapalayam Department of Computer Science and Engineering Tamilnadu Karur639113 India

ISBN: (纸本)9798350327533

The Artificial Intelligence (AI) and Natural Language Processing (NLP) software or application helps to interact with people in a human-like fashion by delivering information, answering questions, completing tasks, and offering assistance in different ways is called a virtual assistant. These virtual assistants can be found in many different places, such as smart speakers, websites, apps, and smartphones. Virtual assistants are able to comprehend human language and provide responses. They process and interpret text or voice inputs using NLP algorithms, allowing users to interact with them in a conversational fashion. Therefore, this study analyse a virtual assistant for college inquiry, a sophisticated artificial intelligence program created to offer prospective college students and their families all-inclusive support during the college admissions process. It can comprehend user requests and respond to them in a conversational manner with the use of Artificial Intelligence (AI) and Natural Language Processing (NLP). These virtual assistants provide individualised advice by providing answers that are specific to each user's questions, whether they relate to particular academic programmes, admission requirements, financial aid options, or campus amenities. They are particularly skilled at responding to frequently requested queries, ensuring prompt and accurate responses while also freeing up administrative staff to concentrate on more difficult responsibilities. This research study offers a platform for users to interact with chatbots that have undergone extensive machine learning training on datasets. machine learning algorithms employ a more natural approach to computing rather than a logical one. The training data affects the outcomes. © 2024 IEEE.

关键词： College enquiry chatbot machine learning Natural language processing Text mining Virtual Assistant system

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Personal Exercise Trainer Using Computer Vision 2

Personal Exercise Trainer Using Computer Vision

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2nd international conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Hari Ram, S. Ashok, A. Ramalingam, A. Dheenadhayalan, S. National Engineering College Dept. of Computer Science and Engineering Tamil Nadu Kovilpatti India

ISBN: (纸本)9798350328202

The Personal Trainer online application tracks users progress and offers realtime feedback to encourage users in their daily workout routines. To identify and analyze human body movements, the program uses the mediapipe library, which integrates opencv and machine learning methods. The Personal Trainer online app provides an engaging and immersive workout experience by incorporating these technologies. The application supports several exercises, including weightlifting, squats, bicep curls, and pull-ups. The software uses the webcam on the computer to record realtime footage of the user completing the workout after they choose it. The user's body motions are then recognized from the recorded video using opencv and mediapipe, and performance-based feedback is given. Users may quickly move through the various training modes, check their activity history, and measure their overall progress thanks to the app's user-friendly and intuitive interface. © 2024 IEEE.

关键词： Computer vision

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Ethical Considerations of Bias and Fairness in AI Models 2

Ethical Considerations of Bias and Fairness in AI Models

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2nd international conference on Disruptive Technologies, ICDT 2024

作者： Verma, Sarvachan Paliwal, Neha Yadav, Kanchan Vashist, P.C. Ajay Kumar Garg Engineering College Department of Information Technology UP Ghaziabad India UP Ghaziabad India Gla University Department of Mechanical Engineering UP Mathura India Gl Bajaj Institute of Technology and Management Department of Information Technology Up Greater Noida India

ISBN: (纸本)9798350371055

The ethical implications of artificial intelligence (AI) are becoming increasingly important. AI systems have the potential to incite bias against certain races and genders, either through programming choices or through the data collected to train the device. Thus, it is imperative to address ethical issues pertaining to justice and bias in order to guarantee the most responsible and optimal application of AI in society. Integrating AI ethics into software development processes is one way to deal with bias and equality in machine learning models. AI developers also have to deal with bias through length of information, in addition to programming concerns. Determining the lifespan of records entails making deliberate decisions about record entry that reduce prejudice and ensure fairness. It entails getting rid of protected characteristics that could have biased effects, such race or gender. Diversifying the kind of records that are included in models can also help to lessen the risk of bias. © 2024 IEEE.

关键词： Artificial Intelligence Bias Fairness machine learning Programming

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ML-Orchestrated Malware Detection Framework for Windows APIs in Critical Infrastructure Systems 2

ML-Orchestrated Malware Detection Framework for Windows APIs...

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2nd international conference on Computer, Communication and Control, IC4 2024

作者： Vaghasiya, Shayalkumar Gupta, Rajesh Mohapatra, Santoshinee Tanwar, Sudeep Garg, Deepak Institute of Technology Nirma University Department of Computer Science and Engineering Gujarat 382481 India Sr University School of Computer Science and Ai Telangana Warangal India

ISBN: (纸本)9798350387933

The security of critical infrastructure systems is crucial as they provide basic amenities and services necessary for functioning of a community, also they represent important part of country's economy. An attack on this critical infrastructure systems can have significant consequences. In this paper we discuss the This paper addresses the increasing security challenges in critical infrastructure systems due to cyber threats, particularly malware and propose a framework based on machine learning for robust malware classification. Our proposed framework includes a dataset of Windows API functions used by the software, preprocessing techniques, and the XGBoost model which achieves an accuracy of 86.98%. Using this model, we can detect and categorize five different types of malwares to provide security against malware attacks. The results show model's ability to successfully classify different kinds of malware. Realtime monitoring is another feature of this framework which is important for actively protecting critical infrastructure systems. © 2024 IEEE.

关键词： machine learning

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A Service-Oriented Digital Twin Framework for Dynamic and Robust Distributed Systems 2

A Service-Oriented Digital Twin Framework for Dynamic and Ro...

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2nd IEEE international conference on software Services engineering (SSE)

作者： Gu, Rong Seceleanu, Tiberiu Xiong, Ning Naeem, Muhammad Malardalen Univ Vasteras Sweden

ISBN: (纸本)9798350368529;9798350368512

Digital Twins (DTs) are virtual representations of physical products in many dimensions, such as geometry and behaviour. As a backbone of Industry 4.0, DTs help interpret and even predict the behaviour of physical processes, provide a virtual testbed for maintenance and upgrade, and enable automatic decision-making supported by artificial intelligence. Despite the promising future, challenges exist, such as the absence of a framework that facilitates the development and application of DTs in industrial contexts. We propose a service-oriented architecture (SOA) DT framework for dynamic and robust distributed systems. The framework contains two types of services. One includes the services provided to the users and is supported by an orchestration mechanism to ensure a quality of service (QoS). The other one refers to the common functions of all DTs. Further, we describe the DT-based decision-making enabled by our QoS-oriented learning of the framework and a Hoare-logic-based verification of QoS.

关键词： Digital Twins SOA System design machine learning Formal verification

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Morphed Picture Recognition using machine learning Algorithms 2

Morphed Picture Recognition using Machine Learning Algorithm...

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2nd IEEE international conference on Advances in Information Technology, ICAIT 2024

作者： Kadiri, Padmaja Anusha, Palagati Prabhu, Madhav Asuncion, Rolito Pavan, Voonna Sainath Suman, Jami Venkata Department of AIML Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati India Guru Nanak Institute of Technology Telangana Hyderabad India Faculty Computing and Electronic Engineering Middle East College Muscat Oman Department of CSE Gayatri Vidya Parishad College of Engineering Andhra Pradesh Visakhapatnam India Department of ECE GMR Institute of Technology Andhra Pradesh Rajam India

ISBN: (纸本)9798350383867

With the development of image alteration tools in the digital age, image authenticity has become a major topic. It is becoming more and more difficult to discern between photos that have been altered and those that have not due to the widespread availability of advanced image editing software. A combination of scikit-learn and OpenCV is used to recognize altered photos. In order to detect morphed information, the suggested system analyzes small distortions and changes in images using image processing algorithms. The system extracts pertinent information from the images by using OpenCV for feature extraction and image pre-processing. Then, a machine learning model that can differentiate between real and altered photos is created using scikit-learn. To improve its accuracy and resilience, the model is trained on a dataset that includes both real and altered images. The goal is to advance the area of picture forensics by offering a dependable method for identifying photos that have been altered. To enhance the overall security and reliability of digital content, the technology can be included into pre-existing image verification pipelines in addition to being used independently. © 2024 IEEE.

关键词： Adversarial machine learning

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