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检索条件"主题词=Machine Learning Algorithms"

共 25877 条记录，以下是661-670 订阅

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A Comparative Analysis and Evaluation of machine learning algorithms for Malware Detection

A Comparative Analysis and Evaluation of Machine Learning Al...

引用

International Conference on Adaptive Science & Technology (ICAST)

作者： Beverlyn Ewuradwoa Amanfu Govindha Ramaiah Yeluripati Computer Science and Informations Systems Department Ashesi University Berekuso Ghana

ISBN: (数字)9798350385403

ISBN: (纸本)9798350385410

The increasing complexity of malware has become a threat to the security of individuals, businesses, and institutions that operate in cyberspace. New malware variants are regularly being created with obfuscation techniques to steal confidential information and cause harm to users' computers while evading detection. Due to this, malware detection and analysis are critical components of Cybersecurity. This study documents the processes undertaken to perform a comparative analysis and evaluation of the current machine-learning algorithms for malware detection and analysis to determine the most efficient model. Efficiency was measured in terms of accuracy, precision, recall, specificity, f1 score, index of balanced accuracy, and Matthew’s correlation coefficient. The findings indicate that the Random Forest classifier is the most efficient as it outperformed the other algorithms studied. The study also identified factors that enhanced the performance of machine learning models, concluding that feature selection using Recursive feature elimination and handling imbalance in the dataset using Synthetic Minority Oversampling Technique improve model performance.

关键词： Measurement Analytical models machine learning algorithms Accuracy Feature extraction Malware Real-time systems Classification algorithms Random forests Long short term memory

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Assessing machine learning algorithms for Real-Time Fake Currency Detection

Assessing Machine Learning Algorithms for Real-Time Fake Cur...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Ajanthaa Lakkshmanan Revanth Sai Grandhi V. Girish Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

Counterfeiting poses a significant threat as the circulation of fake currency diminishes the value of genuine notes, thereby disrupting the country's economic stability. Addressing this issue is critical before the proliferation of counterfeit notes becomes unmanageable. Manual detection methods are often unreliable since counterfeit notes are produced with materials and inks that closely resemble the original. Across the country, counterfeit detection is typically carried out using hardware-based systems. However, these methods are time-consuming and struggle to process large volumes efficiently. To address these challenges and streamline the process of counterfeit currency detection, this study proposes an image processing-based computational technique. The objective is to accurately determine whether a given note is genuine or counterfeit, with a high prediction rate. This detection is enhanced using deep learning algorithms, which analyze key attributes such as color, form, paper thickness, serial numbers, and image filters on the currency. The proposed model is trained on a real-time dataset consisting of both genuine and counterfeit notes. Experimental results demonstrate that this method achieves an overall accuracy of 96.41%.

关键词： Deep learning machine learning algorithms Manuals Computer architecture Streaming media Feature extraction Real-time systems Convolutional neural networks Counterfeiting Currencies

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Performance Evaluation of machine learning algorithms on the Intrusion Detection System of Military Defence Operations

Performance Evaluation of Machine Learning Algorithms on the...

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Advances in Data Engineering and Intelligent Computing Systems (ADICS), International Conference on

作者： Raja Nandini M Nachiyappan. S Anusha K Asha Jerlin M Vellore Institute of Technology Chennai India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

Intrusion detection system is used against harmful attacks also attacker's tools and strategies are always evolving. But implementing an authorized IDS system is equally a significant job. Several experiments were carried out and analyzed in this work in order to evaluate several machine learning and few deep learning classifiers in military defense environment (KDD dataset, NSL-KDD dataset) It was successful in computing numerous performance measures with the purpose of evaluate the chosen classifiers. The emphasis was on false negative and false positive performance indicators to strengthen the intrusion detection system's detection The executed trials revealed that the random forest and SVM has given markable results.

关键词： Support vector machines Radio frequency Deep learning Performance evaluation machine learning algorithms Military computing Intrusion detection

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IoT-Based Livestock Monitoring and Management System Using machine learning algorithms

IoT-Based Livestock Monitoring and Management System Using M...

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Science, Technology, Engineering and Management (ICSTEM), International Conference on

作者： Nagendar Yamsani K. Muthukumaran B Santhosh Kumar Asha V Navdeep Singh JoshuvaArockia Dhanraj School of Computer Science and Artificial Intelligence SR UNIVERSITY Warangal Department of Biomedical Engineering DSCET-Anna University Chennai Institute of Aeronautical Engineering Hyderabad Department of Computer Applications New Horizon College of Engineering Bangalore Lovely Professional University Phagwara Department of Computer Science and Engineering (AI&ML) School of Engineering Dayananda Sagar University Bengaluru Karnataka India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

This article provides a description of an Internet of Things (IoT) system that employs machine learning algorithms to provide real-time and precise insights into the health and behaviour of animals. A network of Internet of Things (IoT) sensors strategically placed throughout the cattle area of the proposed system is capable of monitoring a variety of attributes, including temperature, humidity, grazing patterns, and movements. The information gathered by these sensors is wirelessly transmitted to a central centre, where it is analysed by algorithms that acquire expertise through practice. To extract meaningful results from acquired data, supervised and unsupervised machine learning algorithms are implemented. The system maintains real-time surveillance of farmers or administrators and promptly alerts them to any anomalous events that may occur in regards to the animals' welfare or behaviour. This empowers them to expeditiously implement preventative measures against infections or optimise feeding regimens. Investigating historical data can yield significant insights that facilitate well-informed decision-making concerning enduring concerns like resource allocation or breeding initiatives. The incorporation of machine learning technology and the internet of things (IoT) into livestock management practices increases overall agricultural efficiency, productivity, and animal welfare.

关键词： Temperature sensors Temperature measurement machine learning algorithms Animals Surveillance Sensor phenomena and characterization Real-time systems

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Development of machine learning algorithms for fault detection in power systems - a review

Development of machine learning algorithms for fault detecti...

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Science, Engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Olayanju Olamide. Samson Saheed Lekan Gbadamosi Moses Oluwafemi Onibonoje Evans. E. Ojo Department of Electrical Electronic and Computer Engineering Afe Babalola University Ado-Ekiti Nigeria Department of Electrical and Electronic Engineering Bowen University Iwo Osun State Nigeria Dept. of Electrical Power Engineering Durban University of Technology Durban South Africa

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

Detecting and locating faults within electrical grids presents substantial challenges in power systems engineering, leading to energy loss, reduced revenue, and equipment damage. This paper provides a comprehensive review of the integration and utilization of machine learning algorithms to enhance fault identification processes. Acknowledging the constraints of traditional methods, the paper delves into the historical evolution of fault detection in power systems. By highlighting the significance of machine learning, this review underscores its pivotal role in fault prevention, energy conservation, and bolstering the resilience of power infrastructures.

关键词： Fault diagnosis Support vector machines machine learning algorithms Reviews Fault detection machine learning Electrical fault detection

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Social Media Text Sentiment Classification Based on Bag-of-Words Models with Multiple machine learning algorithms

Social Media Text Sentiment Classification Based on Bag-of-W...

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Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Siyuan Lee National University of Defense Technology Changsha Hunan China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

In this paper, we use bag-of-words model combined with various machine learning algorithms to classify the sentiment of social media texts. Firstly, the data is classified into three categories of positive, neutral and negative according to the textual emotions, of which 47.81% are positive emotions, 31.28% are neutral emotions and 20.9% are negative emotions. Then, this paper establishes three sets of bag-of-words models based on the common adjectives of each textual emotion, transforms the text into a vector representation, and realises the conversion from text-based data to numerical data. Subsequently, algorithms such as logistic regression, Random Forest and support vector machine are used for training and the prediction accuracy of each model is evaluated. The results show that the Random Forest model performs best with an accuracy of 65%, followed by the logistic regression model at 63% and the Support Vector machine model at 59%. The results highlight the superiority of Random Forest in social media text sentiment classification and provide an important reference for related research.

关键词： Support vector machines Logistic regression machine learning algorithms Accuracy Social networking (online) Predictive models Vectors

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Pothole Sensing and Analysis with Doppler Audio Signal Spectrum Employing machine learning algorithms

Pothole Sensing and Analysis with Doppler Audio Signal Spect...

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IEEE Symposium on Wireless Technology and Applications (ISWTA)

作者： Suraya Zainuddin Muhammad Aiman Dani Ismail Haslinah Mohd Nasir Nur Emileen Abd Rashid Khairul Khaizi Mohd Shariff Izwan Zainal Abidin Fakulti Teknologi dan Kejuruteraan Elektronik dan Komputer University Teknikal Malaysia Melaka Melaka Malaysia Microwave Research Institute Universiti Teknologi MARA Selangor Malaysia Terradrone Technology Malaysia Sdn Bhd Kuala Lumpur Malaysia

ISBN: (数字)9798350351354

ISBN: (纸本)9798350351361

Potholes are imperfections, whether minor divots or massive craters, on the surface of roads, streets, or pavements caused by depressions or holes that are dangerous for cars and pedestrians. Several approaches have been investigated to raise pothole detection accuracy; each has benefits and drawbacks. This research proposes a pothole sensing approach by applying machine learning (ML) algorithms and the power spectrum density (PSD) of Doppler audio signals. Although potholes are notable to be localised based on their depth, continuous waveform (CW) is known for its capability to identify based on targets’ movement. Furthermore, neighbouring harmonics are likely to conceal the target's reflected signal. A distinct viewpoint is provided by placing the radar on a moving vehicle where the radar is moving despite the subject of interest. In this research, machine learning (ML) algorithms are compared, and the potential of the Doppler audio signal for pothole identification is analysed. Waveform audio file (.wav) datasets for potholes and non-potholes were obtained using a commercially available K-LC2 Doppler radar. In order to extract the spectral features, audio Doppler signals were transformed to the power spectral density (PSD). Various ML classification methods were applied to the extracted features. The outcome indicates that the Cubic Support Vector machine (SVM) produced the highest performance of 97.22% accuracy, 100% precision, 95.2% recall and 97.56% F1-Score, for the 80:20 distribution, in comparison to other models presented.

关键词： Support vector machines machine learning algorithms Accuracy Roads machine learning Feature extraction Sensors

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Evaluating Sparkle and Beyond the Four Cs: Unveiling a New Frontier in Diamond Valuation with Advanced machine learning algorithms

Evaluating Sparkle and Beyond the Four Cs: Unveiling a New F...

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Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Md Shaik Amzad Basha K Pushpalatha Shruti Jose M Martha Sucharitha GITAM School of Business Gandhi Institute of Technology & Management (Deemed to be University) Bengaluru India Master of Business Administration Mallareddy Engineering College (Autonomous) Telangana India Department of Professional Studies Christ (Deemed to be University) Bengaluru India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

The gemological and high-end retail sectors evaluate diamonds using the “Four Cs”: carat weight, color, clarity, and cut. This study evaluates machine learning diamond valuation accuracy using these parameters. machine learning might revolutionize diamond assessment by improving accuracy and objectivity. This research compares machine learning diamond price prediction techniques. This study attempts to improve gemological data-driven valuation by creating models that best represent a diamond's features and market value. This study uses a vast diamond price dataset. Following data cleaning, normalization, and encoding, we trained and assessed numerous models like Linear, Ridge, Lasso, ElasticNet, Polynomial Regression, Decision Tree Regressor, Random Forest Regressor, Gradient Boosting Regressor, XGBoost Regressor Cat Boost Regressor, K-Nearest Neighbors Regressor, Bayesian Ridge Regressor, SGD Regressor. We used MSE, RMSE, and MSE to evaluate the models' efficacy. Statistical investigations supported our findings. Prediction ensemble and boosting algorithms Cat Boost, XGBoost, Gradient Boosting, and Polynomial Regression performed best. The high R2 values of these models indicate the highest agreement with observed diamond prices. Carat weight was the best predictor, according to industry experts, and feature significance analysis verified this. Combining traditional expertise with machine learning analytics might change diamond assessment, according to the study. Superior models may be used in valuation procedures to improve efficiency and accuracy. This research thoroughly evaluates multiple machine learning diamond price prediction approaches, adding something unique to gemology. It provides the framework for future innovation by showing how machine learning might change valuation methods. Since the study covers gemology and predictive analytics, the findings are groundbreaking.

关键词： Technological innovation machine learning algorithms Accuracy Boosting Prediction algorithms Diamond Predictive analytics Regression tree analysis Cost accounting Random forests

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Enhanced Sensor Fault Detection in Aquatic Monitoring using Deep learning and machine learning algorithms

Enhanced Sensor Fault Detection in Aquatic Monitoring using ...

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International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： Kuna Venkateswararao Gali Pavan Ratnam Nayudu Jonnalagadda Balaji J. N. V. R. Swarup Kumar Irugu John Peter Noble K. E. Naresh Kumar Dept. of CSE GST GITAM Deemed to be University Visakhapatnam India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

This paper deals with the problem of probable sensor failure detection using different machine learning and Deep learning algorithms like VAE, AE, CNN, LSTM, OneClass-SVM, and Isolation Forest in Internet of Things applications, particularly in dynamic environments such as aquaculture, where the reliability and consistency of monitoring systems depend on sensor failure detection. The research shows that the CNN algorithm has a better solution with lower errors, such as MSE and MAE, against other methods under an extensive evaluation using absolute sensor data from an aquatic monitoring system. Further, the hybrid model integrating autoencoder and isolation forest shows outstanding potential to detect anomalies, outperforming conventional models. It offers a reliable solution for fault diagnosis in sensors within an IoT-enabled environment and enhances system credibility.

关键词： Deep learning machine learning algorithms Heuristic algorithms Fault detection Autoencoders Forestry Data models Monitoring Long short term memory Aquaculture

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Spam SMS Classifier Using machine learning algorithms

Spam SMS Classifier Using Machine Learning Algorithms

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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Bhagyashri Mankar Mohini Wanjari Diksha Gabhane Department of Information Technology Yeshwantrao Chavan College of Engineering (YCCE) Nagpur India Department of Artificial Intelligence and Machine Learning J.D. College of Engineering Nagpur India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

Spam via short messaging service (SMS) is becoming more prevalent as spammers use it as a means of reaching their targets. While spam SMS messages can be annoying, they can also be dangerous since they might attempt to steal personal information or contain links to malicious websites. The status of SMS spam detection is surveyed in this research. We go over the various kinds of SMS spam; characteristics that help identify spam, and machine learning techniques that have been employed to address the issue. We also note some of the difficulties and potential paths forward in the identification of SMS spam. Support Vector machine (SVM): SVMs are a class of machine learning algorithms that are applicable to applications involving both regression and classification. SVMs function by locating a hyper plane in the data that has the greatest margin of separation between the various classes. Multinomial Naive Bayes (MNB): A kind of Naive Bayes classifier called MNB is made especially for classification problems involving categorical data. If the data's features are independent of one another, MNB applies the Bayes theorem to determine the likelihood of each class given the data's features. Extra Trees Classifier (ETC): The random forest method serves as the foundation for the ensemble learning algorithm known as ETC. To arrive at a final prediction, ETC builds several decision trees using arbitrary subsets of the data and averages their forecasts. Random Forest Classifier (RFC): Based on the decision tree method, RFC is another kind of ensemble learning algorithm. In order to get at a final prediction, RFC builds several decision trees using arbitrary subsets of the data and averagetheirforecasts. To enhance the overall performance of the model, the predictions made by each individual classifier are combined using the Voting Classifier and Stacking Classifier ensemble models.

关键词： machine learning algorithms Predictive models Message services Prediction algorithms Data models Classification algorithms Naive Bayes methods Decision trees Ensemble learning Random forests

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