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Ensuring Trustworthy machine learning: Ethical Foundations, Robust Algorithms, and Responsible applications 4

Ensuring Trustworthy Machine Learning: Ethical Foundations, ...

引用

4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Usmani, Usman Ahmad Usmani, Abdullah Yousuf Usmani, Mohammed Umar Universiti Teknologi Petronas Malaysia Auburn University United States University of Malaysia Pahang Malaysia

ISBN: (纸本)9798350306118

Intrusion detection, a pivotal facet of securing digital environments, intersects with the proliferation of machine learning (ML) technologies, which have driven transformative advancements across industries. this research paper systematically investigates the multifaceted challenges and solutions within this critical domain, weaving together ethical foundations, robust algorithms, responsible applications, and intrusion detection. Commencing with a comprehensive exploration of intrusion detection's paramount importance in safeguarding digital realms, we delve into the ethical underpinnings of ML, meticulously analyzing facets such as transparency, fairness, and bias mitigation within the context of intrusion detection. Significantly, this study places a strong emphasis on the methodology adopted for intrusion detection, highlighting its robustness and effectiveness in addressing these ethical challenges. With a specific focus on algorithmic robustness, we explore a plethora of techniques that enhance the resilience of ML models against adversarial attacks, uncertainties, and potential intrusions within the proposed framework. Furthermore, this paper meticulously examines the responsible application of ML, particularly in sensitive domains, elucidating strategies aimed at aligning technology with societal values and enhancing security. this holistic exploration culminates in the presentation of a comprehensive framework that serves as an indispensable guiding resource for researchers, practitioners, and policymakers deeply committed to harnessing the immense potential of machine learning addressing ethical aspects (22% focus), algorithmic robustness (35% focus), and responsible application (23 %), offering comprehensive guidance. © 2023 IEEE.

关键词： Intrusion detection

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Hemochromatosis Pathogenesis and Its Association with Liver Disease: An Analysis through machine learning 4

Hemochromatosis Pathogenesis and Its Association with Liver ...

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4th international conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Imtiaz, Isra Rehman, Attique Ur Javaid, Sabeen Ali, Tahir Muhammad Mir, Azka Masud, Mehedi Kumar, Deepak University of Sialkot Department of Software Engineering Sialkot Pakistan Gulf University for Science and Technology Department of Computer Science Kuwait Taif University Department of Computer Science Saudi Arabia Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR Institute of Technology and Sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

A significant risk factor for the development of liver disease is hemochromatosis, an inherited iron overload illness, but the complex pathophysiology behind this link is still unknown. To understand the intricate interactions between hemochromatosis and liver disease, this study uses a cutting-edge strategy that incorporates machine learning techniques. Promising prediction skills are demonstrated by machine learning models, which open up new possibilities for early detection and individualized intervention approaches. the results shed important light on the relationship between hemochromatosis and liver disease and improve our understanding of the pathogenesis of the disease. this study clarifies the pathophysiology of hemochromatosis and its subsequent connection to liver disease, highlighting the revolutionary power of machine learning in understanding intricate biological processes. this selected dataset contains 15 parameters and has 202 dated records with a target value of 'Disease Name' which has 5 different categories Type 1, 2, 2A, 2B,2C, 3, 4, and Type5. the experiment is performed using two classifiers including KNN and Naïve Bayes. the k-fold cross-validation technique is also used to improve the performance. After the comparative analysis of the resulting accuracy percentage of different classifiers, we have proposed the model with the classifier with the highest accuracy. Our proposed model has achieved an accuracy rate of 98.00% using a K-NN classifier. Rapid miner platform is used for the application of the machine learning tools and techniques for this research these discoveries have consequences that go beyond clarifying the mechanisms behind disease;they also open the door to better clinical care and tailored treatment therapies for high-risk people. © 2024 IEEE.

关键词： Potassium alloys

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application of machine learning Algorithms in Predicting Hepatitis C 4

Application of Machine Learning Algorithms in Predicting Hep...

引用

4th international Symposium on Artificial Intelligence for Medicine Science (ISAIMS)

作者： Wang, Yunchuan Yin, Baohua Zhu, Qiang Univ Calif Irvine Dept Econ Irvine CA 92717 USA Whitehorse Int Grp Shanghai Peoples R China

ISBN: (纸本)9798400708138

Hepatitis C, caused by the hepatitis C virus (HCV) infection, is a disease that can progress from initial asymptomatic stages to chronic infection if left untreated, potentially leading to cirrhosis and liver cancer. the diagnosis of hepatitis C requires at least two different types of tests: serological tests and molecular tests. these testing methods impose a financial burden on patients and contribute to patient attrition. the objective of this study is to predict this disease using various machine learning techniques based on common blood test data, in order to achieve early diagnosis and treatment for patients. In this study, we integrated features from literature and original data, applying six machine learning algorithms (logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), decision tree (DT), random forest (RF), adaptive boosting (AdaBoost)) to forecast hepatitis C. the performance of these techniques was compared using metrics such as accuracy, precision, recall, F1-score, receiver operating characteristics (ROC), and the area under the curve (AUC) to identify suitable methods for this disease. Results from the UCI dataset indicate that AdaBoost achieved the highest accuracy (97.8%) and AUC (0.994), making it an effective and cost-efficient method for predicting hepatitis C.

关键词： machine learning Feature integration Performance measurements Hepatitis C virus

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SDPP: A tumor disease diagnosis application based on vibration spectroscopy and machine learning 4

SDPP: A tumor disease diagnosis application based on vibrati...

引用

4th international Symposium on Artificial Intelligence for Medicine Science (ISAIMS)

作者： Chang, Chenjie Zhao, Guifeng Lv, Xiaoyi Xinjiang Univ Coll Software Urumqi 830046 Peoples R China

ISBN: (纸本)9798400708138

Molecular vibrational spectroscopy is an essential technique for analyzing the biomolecular components of human tissues and body fluids with high sensitivity. there are many studies on tumor disease diagnosis that combine artificial intelligence with vibrational spectroscopy, and vibrational spectroscopy has shown good potential in non-invasive disease auxiliary diagnosis. However, the complexity of spectral data processing flow and the variety of algorithms make it difficult for non-professionals to use it efficiently, which limits the development of molecular vibrational spectroscopy-assisted diagnosis technology. this study developed the Spectral Data Processing Platform (SDPP) that normalizes the data processing flow. SDPP can quickly perform preprocessing, feature extraction, and model building on spectral data. As an application desktop program, SDPP has an executable file size of 232 M, an average response time of 3 s for each component, an average CPU utilization rate of 7.30%, and a throughput of 106 times/s. the experimental data selected for testing the platform were collected from Raman data and infrared data of patients and the control group. the results showed that SDPP could efficiently classify patients and the control group and also proved the stability and operability of SDPP.

关键词： machine learning Deep learning SDPP Raman spectroscopy IR

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Research on the knowledge embedding vector method based on TF-IDF and Auto-Encoder 4

Research on the knowledge embedding vector method based on T...

引用

4th international conference on machine learning and computer application, ICMLCA 2023

作者： Tian, Guiyu Institute of Computer Application China Academy of Engineer Physics Mianyang China

ISBN: (纸本)9798400709449

At present, most enterprises and organizations attach great importance to the establishment of knowledge base in order to condense and summarize their core competitiveness, so as to inherit knowledge. However, at present, most of the knowledge base is a document library, and the structured information in the knowledge base is very lacking. Based on this, this paper proposes a knowledge embedding vector technology, which combines keyword extraction and text self-coding technology, so as to abstract and represent knowledge semantically in embedded vector space, and achieve the purpose of enriching structured information in knowledge base. Moreover, the knowledge embedding vector technology proposed in this paper can provide theoretical basis for the upper application of knowledge base, such as knowledge graph construction, knowledge search, knowledge recommendation and application. © 2023 ACM.

关键词： Vector spaces

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Implementation of Effective Rainfall Forecast Model using machine learning 4

Implementation of Effective Rainfall Forecast Model using Ma...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Balamurugan, Kavitha Jose Anand, A. Rammya, B. Dinesh Kumar, R. Kalanithy, K. Mathan, J.N. Kcg College of Technology Department of ECE Tamil Nadu Chennai600097 India

ISBN: (纸本)9798350300888

Rainfall has a significant impact on hydrology and meteorology. To manage resources and make in advance plans to conserve water resources, rainfall prediction is helpful. When predicting rainfall using an existing dataset, machine learning algorithms are equally significant. Data about rainfall is gathered from the KAGGLE website. contains rainfall data that has been collected throughout the past 100 years (1901-2015). Information is recorded and gathered all around India. Using MATLAB software, we developed a system that can forecast rainfall in Tamil Nadu based on this data. © 2023 IEEE.

关键词： MATLAB

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the application of Cluster Analysis Algorithm in Artificial Intelligence+the Improvement of learning Performance: From the perspective of students scores analysis 4

The Application of Cluster Analysis Algorithm in Artificial ...

引用

4th international conference on Intelligent Computing and Human-computer Interaction, ICHCI 2023

作者： Liu, Wenhua Shanghai Urban Construction Vocational College Office of Planning Management and Quality Assessment Shanghai China

ISBN: (纸本)9798350343113

Analysis algorithm plays an important role in artificial intelligence. there are different analysis algorithms. Among them, one of the most classical algorithms is k-means cluster analysis algorithm. On the basis of demand status of students' grades analysis in personal learning system, the research aims to solve the defects because of the unsuitable choose of initial point. At the same while, the data of students' grades analysis has the problems of large quantity and complex data types in personal learning system. the paper links the problems with the innovative application of k-means cluster analysis algorithm. the research uses multiple sampling and one-time clustering to find the optimal improvement algorithm. through analog system experiment, it proves the application effect and stability of k-means cluster analysis algorithm. © 2023 IEEE.

关键词： learning systems

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Research on Deep learning-based Network Intrusion Detection Model 4

Research on Deep Learning-based Network Intrusion Detection ...

引用

4th international conference on computer, Big Data and Artificial Intelligence, ICCBD+AI 2023

作者： Lu, Yan Yang, Qiufen Lu, Huadeng Hunan Open University Changsha410004 China

ISBN: (纸本)9798350373233

this paper investigates the deep learning-based network intrusion detection model to address the problem of recognizing unknown attacks. By analyzing existing models, the potential of applying deep learning in network intrusion detection is explored, with a particular focus on the capabilities of discriminative variational autoencoders and generative adversarial networks in extracting and recognizing network traffic and attack features. We construct a deep learning-based network intrusion detection model with the aim of improving the recognition rate of new attacks and reducing false positives. By evaluating and comparing performance metrics, the advantages of the proposed model in recognizing unknown attacks are validated. Experimental results demonstrate that the model achieves higher recognition rates and accuracy for unknown attacks, outperforming traditional models in intrusion detection performance. this research provides important theoretical and practical guidance for the development and application of network intrusion detection. © 2023 IEEE.

关键词： Intrusion detection

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Bridging Communities: Developing ArSL Translator application for Sustainable Community Development 4

Bridging Communities: Developing ArSL Translator Application...

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4th IEEE international conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Jaafar, Huda Almohsen, Khadija College of Engineering and Computing American University of Bahrain Riffa Bahrain

ISBN: (纸本)9798350395914

this paper presents the development of an Arabic Sign Language ArSL translator application leveraging machine learning techniques. the motivation is to bridge the communication gap between the deaf community and others by providing an accessible and user-friendly solution for real-time sign language translation. the methodology involved collecting and preprocessing an ArSL image dataset training and evaluating multiple machine learning models including k-nearest neighbors support vector machines decision trees multi-layer perceptrons and convolutional neural networks. the convolutional neural network model integrated with Amazon Rekognition achieved the highest accuracy of 98% in recognizing ArSL gestures. the application was developed with the AWS architecture to enable real-time translation. the ArSL translator app aims to support sustainable community development by promoting inclusion education healthcare access and economic opportunities for the deaf community in alignment with multiple UN Sustainable Development Goals. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on the application of Deep learning-Based computer Vision in Anomaly Detection in Communication Networks 4

Research on the Application of Deep Learning-Based Computer ...

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4th IEEE international conference on Data Science and computer application, ICDSCA 2024

作者： Song, Qing Weifang Engineering Vocational College 8979 South of Yunmen Mountain Ave. Qingzhou Shandong Weifang262500 China

ISBN: (纸本)9798350368239

Network anomaly detection has become an important task for ensuring network security and stable operation. However, due to the increasing scale and complexity of communication networks, traditional detection methods have gradually shown limitations in handling large amounts of network data. this paper investigates the application of deep learning-based computer vision technology in communication network anomaly detection, aiming to enhance the accuracy and efficiency of anomaly detection through innovative algorithm design. To this end, this paper proposes a multi-layer deep model (CLA) that combines CNN and LSTM to efficiently capture the spatial and temporal features of network traffic images. the model converts network traffic data into image representations, utilizes CNN to extract spatial features, and then combines LSTM to capture correlations in the time series, significantly improving the accuracy of anomaly detection. Furthermore, this paper introduces an adaptive attention mechanism to focus on key features, effectively reducing noise interference and false positive rates. We did an experiment in network scenarios, validating its feasibility and advancement in practical applications, while also providing new ideas and solutions for the security protection of future communication networks. ©2024 IEEE.

关键词： Deep learning Multi-layer Deep Models Network Anomaly Detection

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