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检索条件"主题词=Machine Learning Algorithms"

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machine learning Based International Trade Data Mining and Trend Prediction Algorithm

Machine Learning Based International Trade Data Mining and T...

引用

Digital Analysis and Processing, Intelligent Computation (DAPIC), International Conference on

作者： Mei Tang Xiaomei Li Xuan Song Weifang Engineering Vocational College Qingzhou Shandong China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

Under the wave of economic globalization, the connection between the economies of various countries and the international economic system has become increasingly close, and the dynamic changes in international economy and trade have a profound impact on the development trajectory of each country. With the increasing frequency and complexity of global trade activities, how to accurately predict international trade trends has become a major issue of concern for policy makers, corporate strategic planners, and risk managers. This article proposes an innovative solution to this problem: an international trade data mining and trend prediction algorithm that combines machine learning (ML) and deep learning (DL) technologies. This algorithm cleverly combines the data processing efficiency of ML with the feature recognition ability of DL, and deeply mines and intelligently analyzes large-scale and multi-dimensional international trade data, aiming to discover potential patterns in international trade and predict their future trends. Experimental verification shows that the algorithm proposed in this paper performs well in predicting international trade trends. It not only significantly improves the accuracy and stability of predictions, but also provides solid data support and decision-making references for policy formulation, corporate strategic planning, and risk management.

关键词： International trade machine learning algorithms Accuracy Decision making Strategic planning Prediction algorithms Market research Stability analysis Data mining Risk management

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Smart Voice Signature: A machine learning Approach to Speaker Identification

Smart Voice Signature: A Machine Learning Approach to Speake...

引用

International Conference on Computer and Communications Technologies (ICCCT)

作者： Shafiul Islam Mazumder Md Tanzid Mollah Labiba Tahia Tawsif Mustasin Rafin Nafiun Al Amin N M SaifKabir Fahim Islam Farhan Md Tanvir Chowdhury Department of Computer Science and Engineering American International University Dhaka Bangladesh Department of Computer Science and Technology Algoma University Brampton Canada Department of Electrical and Electronic Engineering BRAC University Dhaka Bangladesh Department of Computer Science and Engineering BRAC University Dhaka Bangladesh Department of Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

Speaker identification is recognizing individuals based on their unique vocal characteristics. This paper explores the enhancement of speaker identification systems through machine learning techniques, focusing on Mel-Frequency Cepstral Coefficients (MFCC) for feature extraction and the application of K-Nearest Neighbors (K-NN) and Decision Tree algorithms using the ‘Weka’ software tool. This study aims to develop a robust system valuable in forensic science, security, and other areas where verifying an individual's claimed identity based on their voice is crucial. By leveraging these sophisticated algorithms, the system enhances the accuracy and efficiency of voice-based identity verification. This research seeks to improve the precision of speaker recognition. It aims to enhance the visualization tools that aid in analyzing voice data, thus facilitating a more intuitive understanding of the results. This integration of advanced machine learning techniques with practical visualization enhancements is expected to broaden the applicability of speaker identification technologies, making them more effective in diverse real-world environments where quick and reliable identification is needed. Through this study, the system is anticipated to contribute significantly to security and forensic analysis, providing a dependable method for identity confirmation in various applications.

关键词： Technological innovation machine learning algorithms Accuracy Forensics Software algorithms Data visualization machine learning Transforms Tagging Reliability

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Bias and Performance Disparities in Reinforcement learning for Human-Robot Interaction 25

Bias and Performance Disparities in Reinforcement Learning f...

引用

ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Zoe Evans Matteo Leonetti Martim Brandão King's College London London UK

ISBN: (数字)9798350378931

ISBN: (纸本)9798350378948

Bias has been shown to be a pervasive problem in machine learning, with severe and unanticipated consequences, for example in the form of algorithm performance disparities across social groups. In this paper, we investigate and characterise how similar issues may arise in Reinforcement learning (RL) for Human-Robot Interaction (HRI), with the intent of averting the same ramifications. Using an assistive robotics simulation as a case study, we show that RL for HRI can perform differently across models with different waist circumferences. We show this behaviour can arise due to representation bias-unbalanced exposure during training-but also due to inherent task properties that may make assistance difficult depending on physical characteristics. The findings underscore the need to address bias in RL for HRI. We conclude with a discussion of potential practical solutions, their consequences and limitations, and avenues for future research.

关键词： Training Industries Ethics machine learning algorithms Service robots Social groups Prevention and mitigation Human-robot interaction Reinforcement learning Network architecture

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Performance Evaluation of machine learning Approaches for Wearable Sensor-Driven Human Activity Recognition for Elderly People

Performance Evaluation of Machine Learning Approaches for We...

引用

machine learning and Autonomous Systems (ICMLAS), International Conference on

作者： Shaik Shafi Jeeharshitha K Sony G Varshitha Reddy J K Charan Kumar Kiran Kumar G Electronics and Communication Engineering B V Raju Institute of Technology Narsapur Medak India ECE BVRIT Narsapur Medak India ECE VNR VJIET Hyderabad India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

In the recent past, due to the availability of data from Wearable Sensors and Smart Phones at lower cost with energy efficiency, recognizing and tracking uncharacteristic behavior particularly in elderly people has become focal point in research. In addition, there exists more than 962 million aged people globally and 1 in 6 people will be aged 60 years or over by 2030. Thus, in this paper, we aim to analyze and assist elderly people in performing daily activities using wearable sensor data, considering the decline in both physical activity and mental abilities due to aging factor. To achieve this, monitoring elderly people's daily activities in indoor environments via wearable sensors data is essential. machine learning algorithms like Support Vector machine (SVM), Decision Tree, and Random Forest are utilized for activity recognition using the data collected from wearable sensors. These algorithms are chosen to handle timestamped data and for their ability to deliver high accuracy, and optimized memory usage, making them well-suited for this application. Furthermore, performance of three machine learning algorithms has been evaluated using accuracy, precision, F1 score, recall and support parameters. Among all the techniques tested, the SVM accomplished superior accuracy, achieving 96.34 %, followed by Random Forest at 92.67%, and Decision Tree with 85.57%.

关键词： Support vector machines Accuracy machine learning algorithms Aging Prediction algorithms Decision trees Older adults Wearable sensors Random forests Monitoring

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machine learning based Enhanced Intrusion Detection for Cybersecurity

Machine Learning based Enhanced Intrusion Detection for Cybe...

引用

Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： Chinthalapalli Karthik Reddy Gurugubelli Keerthi G Pranay Arokiaraj Jovith A Department of Networking and Communications SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Modern cyber security relies heavily on intrusion detection systems or IDS. However there are a number of issues with traditional IDS techniques such as high false positive rates, issues with scalability and trouble identifying zero-day attacks. IDS performance has increased due to recent developments in machine learning (ML); however problems like imbalanced datasets and processing overhead still exist. This article proposes an ML-based intrusion detection system (IDS) that makes use of Extra Trees Classifier (ETC), Gaussian Naive Bayes, and AdaBoost to improve detection accuracy. The usefulness of the suggested model is demonstrated by comparison with current techniques which emphasize its better classification performance resilience in real-time intrusion detection scenarios and versatility in managing a variety cyberthreats.

关键词： Adaptation models Accuracy machine learning algorithms Scalability Computational modeling Intrusion detection Telecommunication traffic Real-time systems Classification algorithms Random forests

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machine learning for Mental Health Prediction From Social Media Activity

Machine Learning for Mental Health Prediction From Social Me...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： S. Mohanraj R.Ramesh Krishna M. Shan Adams C. Nallusamy Department of Information Technology K.S.Rangasamy College of Technology Tiruchengode India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

The study in question investigates applying machine learning techniques to text data from Naive Bayes classifiers in predicting depression symptomology. The dataset encompasses the text and its relevant labels, while other variables include LIWC variables such as sentence length, post ID, subreddit, and lexical attributes. After data preprocessing-cleaning and feature selection-the topic modeling process involves Latent Dirichlet Allocation (LDA). The main purpose is to detect latent themes in the textual data, as well as to assess the influence of individual words among different publications. Then the model is trained on a labeled dataset and evaluated against an independent one. These findings show good accuracy and F1 score to validate the performance to successfully detect depression symptoms on textual data. This work precisely shows how machine learning algorithms can be used in mental health research, especially in early detection and treatment for those who may manifest depressive symptoms with their online behavior.

关键词： Ethics Accuracy machine learning algorithms Social networking (online) Visual analytics Mental health machine learning Depression Naive Bayes methods Videos

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Early Detection of Alzheimer's Disease Using machine learning Techniques

Early Detection of Alzheimer's Disease Using Machine Learnin...

引用

Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Yash Kumar Megha Aishwarya Ladha Vageshwari Chaudhary Aryan Arman Yash Kalbhile Computer Science Engineering Chandigarh University Kharar Punjab

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Alzheimer's disease (AD) is a disease of the central nervous system that manifests as dementia and other mental disorders. It's critical to correctly diagnose Alzheimer's disease as well as in organising the treatment of the illness and early intervention. Current inventive advancements in machine learning (ML) has the potential to improve diagnosis in the early stages of AD. In this work, we make use of clinical, genetic, and neuroimaging data to contrast several machine learning algorithms, such as CNNs, gradient boosted classifiers, and SVMs. We also review some of the issues arising when applying these models such as previous knowledge, choice of features, and quality of the models. Our findings also reveal that CNNs are effective in learning features from neuroimaging datasets, wherein SVM and gradient boosting classifiers also give a significant account on the clinical and the genetic data. New possibilities in the development of machine learning (ML) are appearing and can assist in increasing the detection rate of Alzheimer's disease with the help of utilizing large neuroimaging, genetics, and medical data. Traditional methods of analysis often cannot identify these signals but through machine learning techniques, the signals can easily be identified from this data. Last of all, we deliberate on the drawbacks of the current approach to machine learning and the potential lines of investigation.

关键词： Neuroimaging Support vector machines Technological innovation machine learning algorithms Reviews Mental disorders machine learning Genetics Alzheimer's disease Medical diagnostic imaging

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machine learning Models for Intrusion Detection System

Machine Learning Models for Intrusion Detection System

引用

International Conference on Communication Systems and Network Technologies (CSNT)

作者： Sourabh Hiremath Rohit D B Shudhanshu Singh Sheela S V Radhika K R Dept of ISE BMSCE Bengaluru India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Protecting networks from mischievous attacks in the scenario of cybersecurity require Intrusion Detection System (IDS). Leveraging machine learning algorithms to develop IDS has gathered substantial consideration due to its capacity to adapt and study from vast datasets. In this research paper, an extensive analysis of intrusion detection has been conducted utilizing various machine learning models trained on the CICIDS2018 dataset. Fifteen distinct models have been explored, ranging from traditional methods to sophisticated deep learning architectures. Subsequently, Random Forest has been identified as the optimal classifier based on its superior performance metrics of F1 score 99.79%, precision 99.79%, and recall 99.81%. Moreover, a framework has been proposed for real-time intrusion detection using the Random Forest classifier, employing CICFlowMeter for data collection, which aligns with the dataset used for model training.

关键词： Training Analytical models Adaptation models machine learning algorithms Intrusion detection Telecommunication traffic Network security Real-time systems Computer security Random forests

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Breast Cancer Diagnosis using machine learning: Challenges and Opportunities

Breast Cancer Diagnosis using Machine Learning: Challenges a...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： M Shobha N Ashok Kumar Department of ECE School of Engineering Mohan Babu University Tirupati Andhra Pradesh India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The purpose of this paper is to review breast cancer diagnosis methods. Cancer begins in cells, the basic building units of tissue. Sometimes the cell growth process fails, and new cells form when the body does not require them, while old or damaged cells do not die as they should. When this happens, a collection of cells frequently develops a mass of tissue known as a lump, growth, or tumor. One of the obstacles that current mammography image detection algorithms confront is the difficulty in analyzing dense tissues. This study provides a quick summary of various breast cancer diagnosis methods. This study also concludes that the features of the mammogram are the primary determinant of any method's accuracy. machine learning algorithms use these features as input for the predictions. Texture-Based Feature Extraction methods can be used to extract features from the mammogram images. To detect anomalies (tumors, masses, calcifications) and classify diseases (benign/malignant), we need a systematic approach that includes preprocessing, feature extraction, anomaly detection, and classification. In order to increase accuracy, machine learning algorithms need a lot of data. Obtaining sufficient data is one of the biggest obstacles to using machine learning algorithms. Therefore, an effective feature extraction method can be used to extract features from the mammography images before applying machine learning algorithms for the predictions.

关键词： Deep learning Support vector machines machine learning algorithms Accuracy Systematics Reviews Feature extraction Breast cancer Mammography Tumors

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Exploring Breast Cancer Risk Factor Through machine learning Algorithm: Random Forest Classification

Exploring Breast Cancer Risk Factor Through Machine Learning...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Demara Hediana Dewi Achmad Choiruddin Desak Gede Agung Suprabawati Department of Technology Management Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Statistics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Surgery Oncology Surgery Division Faculty of Medicine Universitas Airlangga Surabaya Indonesia

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

Breast cancer remains a significant health challenge in Indonesia, with 66,271 new cases reported by WHO in 2024. According to data from RSUD. Dr. Soetomo Surabaya, from January to June 2024, breast cancer screening was carried out on around 1,950 patients. Classification of cancer stages is crucial for determining appropriate treatment so the healing process can be faster. However, this process requires a lengthy process because it requires a process in clinical examination. In addition, the number of breast cancer patients in Indonesia is relatively high, so the handling process is often not fast. RSUD Dr. Soetomo has excellent potential to utilize available data to analyze breast cancer risk factors using machine learning algorithms to provide faster treatment recommendations. However, the available data is not complete, even if it is complete, it can speed up the analysis process using the algorithm. Applying the random forest algorithm can classify risk factors quickly and accurately because it does not use manual analysis, which takes time. With random forest implementation, the classification accuracy rate reaches 97%. This algorithm can be relied on to provide an overview of the most significant risk factors. Thus, this algorithm can identify high-risk patients earlier to be prioritized for immediate treatment. In this study, the factors that influence someone to have breast cancer are BMI, physical activity, age, age of first menstruation, and passive smoking. The findings of this study will raise awareness of the importance of breast cancer prevention for hospitals and the general public to be aware of the risk factors associated with breast cancer.

关键词： Training machine learning algorithms Accuracy Prevention and mitigation Training data Prediction algorithms Breast cancer Classification algorithms Random forests Tuning

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