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检索条件"主题词=CLASSIFICATION algorithms"

共 26481 条记录，以下是4971-4980 订阅

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Sentiment Analysis of Customer Reviews on DANA Application Service using Multinomial Naïve Bayes Classifier Algorithm

Sentiment Analysis of Customer Reviews on DANA Application S...

引用

IEEE International Conference on Communication, Networks and Satellite (ComNetSat)

作者： Rachel Margareth Simamora M. Yoka Fathoni Information System Faculty of Industrial Engineering Universitas Telkom Purwokerto Indonesia

ISBN: (数字)9798350368086

ISBN: (纸本)9798350368093

The rapid advancement of information and communication technology has had a significant impact on various aspects of life. One of the prominent impacts is the ease with which people can conduct transactions without the need to physically meet. E-wallets, because of technology, facilitate seamless financial transactions. Among those e-wallets, DANA has gained popularity and a large user base. This research aims to analyze customer sentiment regarding DANA application services by using Naïve Bayes Classifier algorithm. Data is extracted from customer reviews on Twitter, categorized into positive, negative, and neutral sentiments. By implementing Naïve Bayes Classifier. By utilizing the Naïve Bayes Classifier, analyzing customer comments about the DANA app on Twitter. Twitter is an ideal platform for sentiment analysis because of its role as a place to vent and express opinions. The results of the researcher’s experiments, based on 451 training data and 113 test data points, resulted in an accuracy of precision values of ${7 0 \%}, 97 \%$ and $89 \%$, f1- score of $81 \%, 97 \%$, and $42 \%$, and recall of ${9 8 \%} \%, {9 7 \%}$ and ${2 8 \%}$. Also, Naïve Bayes classification can produce the highest accuracy of ${8 0 \%}$. The results show a reliable level of accuracy in classifying sentiment in the form of positive results 122 tweets, negative 228 tweets and neutral 214 tweets from customer posts.

关键词： Sentiment analysis Accuracy Social networking (online) Reviews Online banking Blogs Vents Training data Bayes methods classification algorithms

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Enhancing Intrusion Detection Systems through Advanced Feature Engineering and Machine Learning Techniques

Enhancing Intrusion Detection Systems through Advanced Featu...

引用

Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： Aditya Patel Department of Computer Science Adelphi University

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

This study investigates enhancing intrusion detection systems (IDS) through machine learning and advanced feature engineering. I evaluate classification algorithms, including k-nearest neighbors (KNN) and support vector machines (SVM), on the ADFA-LD 12 dataset, finding that SVMs, especially with two-sequence feature spaces, outperform other methods. A detailed analysis of linear and sigmoid kernels shows that the two-sequence space improves detection accuracy, with recursive feature elimination identifying optimal performance with fewer than 240 features. One-class SVMs, used for outlier detection, perform poorly. The study underscores the potential of SVMs and suggests further research into scalable algorithms and improved kernel methods for unsupervised detection.

关键词： Support vector machines Machine learning algorithms Intrusion detection Machine learning Nearest neighbor methods Feature extraction Information and communication technology classification algorithms Kernel Anomaly detection

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ReLAx: Deep Reinforcement Learning Based Resource Allocation for Next-G RANs

ReLAx: Deep Reinforcement Learning Based Resource Allocation...

引用

IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Ayman Younis Chuanneng Sun Dario Pompili Department of Electrical and Computer Engineering Rutgers University-New Brunswick NJ USA

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Next-Generation Radio Access Network (Next-G RAN) will leverage a novel architecture that accelerates the tran-sition from inflexible networks to agile and adaptable networks. In this paper, we introduce a novel Deep Reinforcement Learning-based Resource Allocation (ReLAx) framework to deal with the joint optimization of UE association and power allocation in Next-G RAN systems. The ReLAx problem has been formulated to maximize the network Energy Efficiency (EE) under the constraints of Quality of Service (QoS), fronthaul link, functional split configuration, and transmit power budget. The optimization problem is cast as non-convex and NP-complete. A multitask Deep Deterministic Policy Gradient (DDPG) method is proposed to solve the complexity, in which two actors are trained to gen-erate UE association and power allocation, respectively. To speed up the training process and reduce computational resources, we introduce soft multitask learning as a constraint during training so that one model would not drift too far away from the other one. Our real-time experiments on a fully containerized Next-G RAN testbed show the effect of functional splits on CPU utilization and system latency. In addition, simulation results show that the proposed resource allocation solution outperforms competing traditional algorithms, such as standard DDPG and Weighted Minimum Mean Square Error (WMMSE).

关键词： Training Simulation Quality of service Deep reinforcement learning Real-time systems classification algorithms Resource management Optimization Next generation networking Radio access networks

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Enhancing Intrusion Detection Systems Using Ensemble Machine Learning Techniques

Enhancing Intrusion Detection Systems Using Ensemble Machine...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Isaac Adedoyin Oyebade Isaac Oluwafemi Elesemoyo Mercy Oka Ebute Abidemi Emmanuel Adeniyi Joseph Bamidele Awotunde Akpan Caleb Solomon Agbotiname Lucky Imoize Kehinde Amudat Odulana Department of Computer Science Bowen University Iwo Nigeria Department of Computer Engineering Elizade University Ilara-Mokin Nigeria Department of Accounting Anderson School of Management University of New Mexico USA Department of Computer Science University of Ilorin Ilorin Nigeria Department of Electrical and Electronics Engineering University of Lagos Akoka Lagos Nigeria School of Physics Engineering and Computer Science (Data Science and Analytics) University of Hertfordshire

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Intrusion detection systems are an essential part of the current cybersecurity environment, and IDS devices are intended to scan the activity of the network and systems to identify possible improper actions or violations. IDSs work in analyzing the traffic of networks and the traffic of networks and the behavior of systems, distinguishing suspicious information and signs of violation of legal access. This capability is critical for protecting information systems’ characteristics, such as integrity, confidentiality, and availability. Useful data was collected from Kaggle data archives, and several base models such as K-Nearest Neighbor, Random Forest, Logistic Regression, and Decision Tree classifier will be used. Their predictions are combined using a Voting Classifier. The models are trained and evaluated based on accuracy, precision, recall, and F1-Score. Results show that the Voting classifier achieved an accuracy of 99.78%, with a precision of 99.67% and recall of 99.92% for intrusion detection. The Logistic Regression and K-Nearest Neighbor models achieved accuracies of 95.37% and 99.52%, respectively, while having a precision of 94.93% and 99.51%, respectively. The Decision Tree and Random Forest models achieved accuracies of 99.76% and 99.86%, respectively, while having a precision of 99.75% and 99.83%, respectively. These findings suggest ensemble techniques significantly enhance intrusion detection, making networks safer.

关键词： Logistic regression Accuracy Sensitivity Intrusion detection Telecommunication traffic Nearest neighbor methods classification algorithms Decision trees Object recognition Random forests

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Deciphering Descriptors-Understanding Personality Beyond Resumes Using Machine Learning algorithms

Deciphering Descriptors-Understanding Personality Beyond Res...

引用

Trends in Engineering Systems and Technologies (ICTEST), International Conference on

作者： Nandini P Sony P Niranjana B Nair Sneha Devan Sona Martin Computer Science Department Managed by IHRD Govt. Model Engineering College Kochi India

ISBN: (数字)9798350307795

ISBN: (纸本)9798350307801

Recruiters face challenges in accurately evaluating candidates, with reliance on resumes alone hindering effectiveness. Predicting personality traits and assessing technical and soft skills pose additional hurdles, leading to inefficient selections and potential mismatches. To address this, a system integrating personality prediction via machine learning and resume analysis is proposed. Using a random forest model, candidate personality traits are predicted from standardized tests, encompassing dimensions like extraversion and conscientiousness. Simultaneously, the system conducts in-depth resume analyses, extracting qualifications, work experience, and interpersonal skills. By considering personality-job compatibility, this system offers comprehensive insights for informed decision-making, enhancing recruitment efficiency and reducing mismatches.

关键词： Industries Machine learning algorithms Resumes Predictive models Prediction algorithms classification algorithms Reliability

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Exploring Deep Learning for Comprehensive Dog Breed Identification

Exploring Deep Learning for Comprehensive Dog Breed Identifi...

引用

Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： S. Priyanka S. Keerthika P. Jayadharshini S. Jeneshamalar A. Malavika T. Dinesh Babu Artificial Intelligence Kongu Engineering College Perundurai Tamil Nadu

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

The breed of a dog can be identified from an image using computer vision and deep learning techniques. With the use of user-submitted dog images, the model will identify the breed of the dog by choosing from among the 70 breeds included in the dataset. Inception, Xception, InceptionResNetV2, and MobileNetV2 are a few of the deep learning algorithms that are used in the proposed work to determine dog breeds. The Xception, Inception, InceptionResNetV2, and MobileNetV2 models exhibit high accuracy rates in the current system, with $\mathbf{9 1. 6 7 \%}, \mathbf{9 0. 9 2 \%}$, and $\mathbf{9 0. 0 3 \%}$, respectively. The usefulness of these algorithms in identifying breeds is evaluated through comparison. The outstanding performance of these algorithms especially Xception, which attained the highest accuracy is highlighted in this research. These models’ efficacy shows how deep learning techniques have made tremendous progress in dog breed recognition.

关键词： Deep learning Computer vision Accuracy Computational modeling Neural networks Dogs Computer architecture Solids Real-time systems classification algorithms

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Enhancing Understanding of Public Sentiment on Twitter Using SVM and Lexicon Methods

Enhancing Understanding of Public Sentiment on Twitter Using...

引用

Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Dewi Khairani Andi Setiawan Siti Ummi Masruroh Informatics Engineering Faculty of Science and Technology UIN Syarif Hidayatullah Jakarta Jakarta Indonesia

ISBN: (数字)9798350367492

ISBN: (纸本)9798350367508

Indonesia has experienced 655 terrorism incidents between 2000 and 2024, leading to numerous casualties. This study aims to enhance understanding of public sentiment on Twitter in response to notable terrorism events, specifically the suicide bombing of the Astana Anyar Police Station in Bandung in 2022. Using a Support Vector Machine (SVM) algorithm for classification and two lexicon-based methods (VADER and InSet) for sentiment analysis, the study compared their performance and accuracy. The VADER lexicon indicated predominantly negative sentiments, while the InSet lexicon showed more positive sentiments, likely due to differences in dictionary sizes, data preparation, and feature extraction methods. The model using the VADER-labeled dataset achieved a 94% accuracy rate, compared to 93% for the InSet-labeled model. These high accuracy levels demonstrate the models proficiency in categorizing sentiment data, providing valuable insights into public reactions to significant events and underscoring the importance of selecting appropriate lexicon-based methods for sentiment analysis in social media contexts.

关键词： Support vector machines Sentiment analysis Accuracy Social networking (online) Law enforcement Terrorism Feature extraction classification algorithms Labeling Cultural differences

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Cloud based Early Acute Lymphoblastic Leukemia Detection Using Deep learning based Improved YOLO V4

Cloud based Early Acute Lymphoblastic Leukemia Detection Usi...

引用

Data Science and Information System (ICDSIS), IEEE International Conference on

作者： Raj Kumar Gudivaka Layth Hussein T M Aruna R Rana Veer Samara Sihman Bharattej Priyan Malarvizhi Kumar ETeam AP India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of AIML Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science Saint Leo University Florida United States Department of Information Science College of Information University of North Texas Texas USA

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

Leukemia is fatal cancer in both children and adults and is divided into acute and chronic. Acute lymphoblastic leukemia (ALL) is a subtype of this cancer. Early diagnosis of this disease can have a significant impact on the treatment of this disease. However, recently many of the models are created for ALL detection, still the models are suffering from the detecting the tiny blood cell from the collected ALLIDB1 dataset. Proposed method is trained using Improved you only look once version four (YOLO v4) and is uploaded into Hadoop Distributed File System (HDFS) Hadoop framework. The collected dataset contains imbalance classes, so initially the dataset is undertaken by the preprocessing technique of data augmentation method for creating the synthetic data and solve the classes imbalance issue. The processed images are fed into the proposed YOLO v4 model to detecting the tiny blood cells. The suggested Improved YOLOv4 algorithm is shown to have a high effectiveness in enhancing the detection and recognition precision of healthy and blast cells.

关键词： YOLO Sensitivity Feature extraction Real-time systems Numerical models classification algorithms Blood

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Arithmetic Optimization with SE-DenseNet based Image Retrieval and classification for Diabetic Retinopathy

Arithmetic Optimization with SE-DenseNet based Image Retriev...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： S Syed Mahamood Shazuli A. Saravanan Department of Computer and Information Sciences Annamalai University

ISBN: (纸本)9781665489638

Diabetes is a widespread disease that causes visible microvascular problems like macular edema and diabetic retinopathy (DR) in the human eye retina, the images were utilized for diagnosis and manual disease screening. This labour-intensive task would significantly benefit from automatic detection utilizing the artificial intelligence (AI) method. In addition, Content-based image retrieval (CBIR) models can be used for the retrieval of related images from huge databases and find useful in several application areas, particularly healthcare. This study develops an arithmetic optimization with SE-DenseNet based Image Retrieval and classification (AOSED-IRC) model for DR. The major intention of the AOSED-IRC model lies in the automated retrieval and classification of fundus images for DR diagnosis. To accomplish this, the presented AOSED-IRC model follows guided image filter (GIF) technique for noise removal. Besides, the AOSED-IRC model uses SE-DenseNet model where the DenseNet with dynamic calibration of feature channels is presented. Moreover, city block similarity measure is used for the retrieval process. For DR classification, AO algorithm with stacked autoencoder (SAE) is utilized in this study in which the hyperparameters of the SAE are tuned by the AO method. The experimental validation of the AOSED-IRC algorithm is tested utilizing benchmark DR dataset and the outcomes demonstrate the significant performance of the AOSED-IRC method over other recent models.

关键词： Retinopathy Heuristic algorithms Image retrieval Urban areas Benchmark testing Diabetes classification algorithms

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Predicting Toddler Stunting at Bandarharjo Health Center: Applications of K-Nearest Neighbors and Naïve Bayes algorithms

Predicting Toddler Stunting at Bandarharjo Health Center: Ap...

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International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM)

作者： Ashiva Prameswara Putu Harry Gunawan School of Computing Telkom University Bandung Indonesia CoE HUMIC School of Computing Telkom University Bandung Indonesia

ISBN: (数字)9798331531249

ISBN: (纸本)9798331531256

According to the 2021 Indonesian Nutrition Status Study (SSGI), the incidence of toddler stunting in the nation is 24.4 %, which is a significant public health problem. Stunting is characterized by having a height under standard caused by malnutrition and lack of healthcare before and after a birth which has negative impacts on the toddlers' physical growth and brain development. This study evaluates how imbalanced data influenced The K-Nearest Neighbors (K-NN) and Naïve Bayes in predicting stunting status. In the imbalanced dataset, K-NN shows high performance with an accuracy of 98.41 % and a Macro F1-Score of 89.30%, while Naive Bayes recorded an accuracy of 90.76% and a Macro F1-Score of 70.00%. After applying SMOTE to balance data, K-NN stayed stable with 98.15% accuracy and Macro F1-Score increased to 89.90%. Besides, Naive Bayes experienced a decline in accuracy to 87.14%, although Macro F1-Score increased to 87.20%. These findings highlight the advantages of deep K-NN handle data imbalance and its stability is better than Naive Bayes. The increased F1-Score after applying SMOTE reflects improved precision and recall for identifying stunted cases, even though overall accuracy declined. This decline can be attributed to the algorithm's sensitivity to class distribution because the dataset became more balanced. Collectively, these results underscore the importance of using balanced datasets.

关键词： Pediatrics Accuracy Nearest neighbor methods Predictive models Prediction algorithms Data models Stability analysis Bayes methods classification algorithms Standards

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