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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Neelam Singhal Mohd. Ashraf School of ICT Gautam Buddha University Greater Noida India

ISBN: (纸本)9781479968329

Data mining is now one of the most active field of research. Extracting those nuggets of information is becoming crucial and one of its important technique is classification. It helps to group the data in some predefined classes. Various techniques for classification exists which classifies the data using different algorithms. Each algorithm has its own area of best and worst performance. This paper concentrates on the four most famous algorithms, i.e., Decision Tree, Naïve Bayes, K Nearest Neighbour and Genetic Programming and the effect on their performance of time and accuracy when the number of instances are incrementally decreased. This paper will also investigate the difference in result when working with binary class or multiclass datasets and suggest the algorithms to follow when using certain kind of dataset.

关键词： classification algorithms Accuracy Data mining Decision trees Timing Genetic programming Breast cancer

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Comparison of classification algorithms in classification of ECG beats by time series

Comparison of classification algorithms in classification of...

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IEEE Signal Processing and Communications Applications (SIU)

作者： Yasin Kaya Hüseyin Pehlivan Bilgisayar Mühendisliği Bölümü Karadeniz Teknik Üniversitesi Trabzon Türkiye

Today one of the most important health problems are fatal heart related diseases. Early diagnosis and treatment of heart disease can prevent sudden death. Detected through the human body and seen as a result of activity of the heart's electrical signals is called electrocardiogram (ECG). ECG signal, which can be easily obtained without causing any harm to patient's body, is a good indicator of the disorder during operation of the hearth. In this study, Normal beats (N), left bundle branch block (LBBB), right bundle branch block (RBBB) and Paced beat(P) beats are classified and the classification performance has been analyzed. Time series of the signal is used as an input vector for classification algorithms instead of extracting features from the signal. Independent component analysis (ICA) is used for feature reduction. Neural networks, k-nearest neighbour, Bayes, and Decision trees classification algorithms were used. In this study, kNN showed best accuracy rates.

关键词： Electrocardiography classification algorithms Heart Time series analysis Artificial neural networks Diseases

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Deep Learning and Regularization algorithms for Malicious Code classification

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IEEE ACCESS 2021年 9卷 91512-91523页

作者： Wang, Haojun Long, Haixia Wang, Ailan Liu, Tianyue Fu, Haiyan Hainan Normal Univ Sch Informat Sci & Technol Haikou 571158 Hainan Peoples R China Geneis Beijing Co Ltd Beijing 100102 Peoples R China

Network security has become a growing concern within the popularity and development of the Internet. Malicious code is one of the main threats to network security. Different types of malicious code have different functions and cause different harms. Therefore, improving the detection efficiency and recognition accuracy of malicious code is becoming an urgent problem to be solved. While traditional machine learning methods for malicious code detection largely depend on hand-designed features with experts' knowledge of the domain or focus on the images which come from malicious code binary files. These methods spend too much time on feature extraction. With the emergence of a large amount of malicious code data, the efficiency of traditional machine learning algorithms is getting worse and worse. In this paper, a workflow based on deep learning is proposed to detect and classify malicious codes. This workflow adopts a convolutional neural network (CNN) and the regularization algorithms to classify malicious code with N_gram semantic feature as input of the model. The convolutional neural network can automatically extract the features of malicious code while avoiding the need for manual feature selection. Regularization algorithms not only speed up the training process of the deep model but also improve the generalization ability in the case of effective prevention of over-fitting of the model. The proposed method is compared with the state-of-the-art methods and other deep learning models. Experimental results show that our workflow can improve the accuracy and efficiency of malicious code classification.

关键词： Malware Feature extraction Deep learning Convolutional neural networks classification algorithms Machine learning algorithms Support vector machines Malicious code classification deep learning convolutional neural networks N-gram regularization algorithm

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Detection of Illegal Players in Massively Multiplayer Online Role Playing Game by classification algorithms

Detection of Illegal Players in Massively Multiplayer Online...

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International Conference on Advanced Information Networking and Applications (AINA)

作者： Zhongqqiang Zhang Hiroaki Anada Junpei Kawamoto Kouichi Sakurai Graduate School of Information Science and Electrical Engineering Kyushu University Fukuoka JAPAN Institute of Systems Information Technologies and Nanotechnologies Fukuoka SRP Center Building 7F Fukuoka JAPAN Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka JAPAN

Online games have become one of the most popular games in recent years. However, fraud such as real money trading and the use of game bot, has also increased accordingly. In order to maintain a balance in the virtual world, the operators of online games have taken a stern response to the players who conduct fraud. In this study, we have sorted out players' behaviors based on players' game playing time in order to support and find potentially illegal players in the MMORPG. In this paper, we added a topic model to the experiment and used k-means as a major tool to classify the players in the World of War craft Avatar History Dataset and find potentially illegal players.

关键词： Games Clustering algorithms Avatars Algorithm design and analysis classification algorithms Euclidean distance Resource management

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A comparative study of classification algorithms for predicting gestational risks in pregnant women

A comparative study of classification algorithms for predict...

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International Conference on Computers, Communications, and Systems (ICCCS)

作者： B. N. Lakshmi T. S. Indumathi Nandini Ravi VIAT PG Research Centre Bangalore Karanataka India Obs & Gyn) Obstetrics and Gynaecology Bangalore Karnataka India

ISBN: (纸本)9781467397575

Pregnant women must be protected from complications during the period of gestation which is a stage every woman undergoes many physiological changes, sometimes inducing complications that may turn severe and lead to death of both mother and fetus. classification methods are popularly used method whose algorithms are best suitable in medical diagnosis. Among them C4.5 Decision Tree algorithm and Naïve Bayes algorithm are effectively used for classifying medical data. Both algorithms are most capable to classify the pregnancy data of present research study. The main aim of this paper is to highlight concept of standardization of parameters selected for data collection in study to achieve elevated results of classification by selected classification algorithms. The paper also presents how efficiently risk levels can be predicted by the selected algorithms to protect pregnant women from complications in pregnant women health. Pregnancy data of present research study is considered for classifying and predicting complications leading to different risk levels during pregnancy. The best algorithm among selected two algorithms namely C4.5 and Naïve Bayes Classifier is identified in the paper for further improving the efficiency of selected classifier. By identifying the risks in pregnancy, present maternal and fetal mortality rate can be efficiently reduced to lower rates thus providing a safe pregnancy period, post natal period and a healthy baby to pregnant mothers.

关键词： Pregnancy classification algorithms Decision trees Algorithm design and analysis Prediction algorithms Pediatrics Blood pressure

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A New Malware classification Framework Based on Deep Learning algorithms

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IEEE ACCESS 2021年 9卷 87936-87951页

作者： Aslan, Omer Yilmaz, Abdullah Asim Siirt Univ Comp Engn Dept TR-56100 Siirt Turkey Republ Turkey Minist Agr & Forestry TR-06800 Ankara Turkey

Recent technological developments in computer systems transfer human life from real to virtual environments. Covid-19 disease has accelerated this process. Cyber criminals' interest has shifted in a real to virtual life as well. This is because it is easier to commit a crime in cyberspace rather than regular life. Malicious software (malware) is unwanted software which is frequently used by cyber criminals to launch cyber-attacks. Malware variants are continuing to evolve by using advanced obfuscation and packing techniques. These concealing techniques make malware detection and classification significantly challenging. Novel methods which are quite different from traditional methods must be used to effectively combat with new malware variants. Traditional artificial intelligence (AI) specifically machine learning (ML) algorithms are no longer effective in detecting all new and complex malware variants. Deep learning (DL) approach which is quite different from traditional ML algorithms can be a promising solution to the problem of detecting all variants of malware. In this study, a novel deep-learning-based architecture is proposed which can classify malware variants based on a hybrid model. The main contribution of the study is to propose a new hybrid architecture which integrates two wide-ranging pre-trained network models in an optimized manner. This architecture consists of four main stages, namely: data acquisition, the design of deep neural network architecture, training of the proposed deep neural network architecture, and evaluation of the trained deep neural network. The proposed method tested on Malimg, Microsoft BIG 2015, and Malevis datasets. The experimental results show that the suggested method can effectively classify malware with high accuracy which outperforms the state of the art methods in the literature. When proposed method tested on Malimg dataset, 97.78% accuracy is obtained which is outperformed most of the ML-based malware detection method.

关键词： Malware Feature extraction Deep learning Computer architecture Cloud computing classification algorithms Static analysis Malware malware classification malware detection malware variants deep neural networks transfer learning deep learning

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FBG Tactile Sensing System Based on SVP-Transformer for Material classification

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IEEE SENSORS JOURNAL 2025年第3期25卷 5370-5379页

作者： Lyu, Chengang Li, Lin Zhang, Ze Wang, Tianle Li, Peiyuan Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

Tactile is an essential sensory modality for the interaction between the human hand and the surrounding environment, playing a crucial role in tasks such as object grasping, tactile perception, and object classification. In this study, we propose a material classification scheme based on fiber Bragg grating (FBG) tactile sensing technology and deep learning algorithms. Considering the sensing characteristics of FBGs and the principles of human tactile dynamic and static perception, the biomimetic finger is equipped with FBGs, which is based on the simulation results of ANSYS finite element. By simulating the sliding motion of biological fingers, tactile data is collected from different material surfaces. Wavelength-swept optical coherence tomography technology is used to obtain the interference signals of the FBGs, which are then processed to obtain the sensing signals of static stress and dynamic vibration. Based on the obtained dynamic and static multivariate time series tactile datasets, we construct a Transformer-based multivariate time series classification model named Shape-level Variable-Position Transformer (SVP-T) model, for the classification of eight common object surface materials. The average classification accuracy reaches more than 86%, and the single recognition time is only about 0.94 s, which proves the effectiveness of the combination of sensing system and deep learning algorithm. The method proposed in this article has broad application prospects in the fields of healthcare, robotics, and industrial manufacturing.

关键词： Sensors Stress Vibrations Fiber gratings Biomimetics Optical fiber sensors Heuristic algorithms Tactile sensors Optical reflection classification algorithms Deep learning fiber Bragg grating (FBG) object classification static-dynamic tactile sensing system

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Vision-Based Nut Quality classification Using Conditional GAN and CNN

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 11455-11468页

作者： Hung, Kuei-Jung Lee, Tzu-Chen Wang, Chiao-Sheng Lin, Tsung-Chun Guo, Chen-Wei Conan Tsay, Der-Min Perng, Jau-Woei Natl Yang Ming Chiao Tung Univ Inst Elect & Control Engn Hsinchu 300 Taiwan Natl Sun Yat sen Univ Dept Mech & Electromech Engn Kaohsiung 804 Taiwan Natl Formosa Univ Dept Automat Engn Yunlin 632 Taiwan Carnegie Mellon Univ Dept Mech Engn Pittsburgh PA 15213 USA

In this study, the quality of a nut is discussed by considering images of the internal thread, and an analysis is conducted using traditional machine-learning and deep-learning algorithms. Compared to the traditional contact methods, the vision-based method has the advantage of fast computing speed and is not affected by the conditions of tapping speed. The pitch and pitch diameter of the internal thread are the indicators that characterize nut quality classification. For one nut, 36 internal thread images are collected, one image per 10 degrees, by the self-designed laser triangulation measurement platform. Using the laser triangulation method, the information on both indicators can be obtained and analyzed. In the traditional machine-learning methods, the internal thread images undergo several preprocessing procedures to obtain the region of interest and calculate the depth between the crest and root. Subsequently, 33 handcrafted features are used to extract the features from the 36 processed images. Finally, the features are classified by three families of machine-learning algorithms, including support vector machines, k-nearest neighbors, and decision trees. In the deep-learning method, conditional generative adversarial network and convolutional neural network (CNN) are used for data augmentation and nut quality classification, respectively. The experimental results show that the proposed CNN model can achieve a higher classification accuracy rate. Furthermore, the proposed CNN model trained with the generated images is better equipped to detect the nut quality under different decision thresholds.

关键词： Fasteners Feature extraction Instruction sets Inspection Machine learning Message systems Torque Measurement by laser beam Convolutional neural networks classification algorithms Conditional generative adversarial network deep learning internal thread of nut machine learning nut quality classification

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Glioma Brain Tumor classification Using Convolution Neural Network and Majority Voting

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IEEE ACCESS 2025年 13卷 46228-46248页

作者： Pilaoon, Pongsak Hamamoto, Kazuhiko Maneerat, Noppadol King Mongkuts Inst Technol Ladkrabang Sch Engn Bangkok 10520 Thailand Tokai Univ Sch Informat & Telecommun Engn Tokyo 1088619 Japan

Glioma brain tumors are malignant diseases for which early detection and instant treatment will increase the survival rate. Several studies have reported the efficiency of deep learning convolutional neural networks (CNN) in diagnosing brain tumors using magnetic resonance imaging (MRI). In this study, we investigated the potential of state-of-the-art classifiers to achieve the highest accuracy in the detection of brain tumors using MRI. For this purpose, we introduced a comparative study of eight state-of-the-art classifiers. The methodology comprised three different approaches: 1) an imbalanced dataset, 2) a balanced dataset using image augmentation, and 3) ensemble learning using the best of the top five models for majority hard and soft voting. The dataset comprised converted MRI data from the repository of molecular brain neoplasia data (REMBRANDT) and brain tumor segmentation 2021 (BraTS) databases. An increasing number of MRI images and datasets has prevented overfitting. Initially, a preprocessing stage morphological operation and contrast-limited adaptive histogram equalization (CLAHE) algorithms were used to remove skeletons and artifacts and optimize the image contrast for readiness classification. The stochastic gradient descent with the momentum algorithm option was used to train the network. The trained model was used to predict the testing dataset, and the results from each pretrained network were evaluated. The experimental results demonstrated that the prediction accuracy of the trained network was significantly improved using a balanced training dataset. The discriminative image region used to interpret the predicted result using the gradient-weighted class activation mapping (Grad-CAM) algorithm was proposed in the final stage for trustworthiness. The experimental results showed that the best approach was inceptionV3 with a balanced dataset. The accuracy, sensitivity, specificity, and area under the curve were 99.73%, 99.61%, 100%, and 1.00, respe

关键词： Brain tumors Magnetic resonance imaging Tumors Feature extraction Accuracy Image segmentation classification algorithms Cancer Morphological operations Prediction algorithms Glioma brain tumor CLAHE morphological operation convolutional neural network Grad-CAM deep learning majority voting

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Domain Adversarial Active Learning for Domain Generalization classification

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第1期37卷 226-238页

作者： Chen, Jianting Ding, Ling Yang, Yunxiao Di, Zaiyuan Xiang, Yang Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China

Domain generalization (DG) tasks aim to learn cross-domain models from source domains and apply them to unknown target domains. Recent research has demonstrated that diverse and rich source domain samples can enhance domain generalization capability. This work argues that the impact of each sample on the model's generalization ability varies. Even a small-scale but high-quality dataset can achieve a notable level of generalization. Motivated by this, we propose a domain-adversarial active learning (DAAL) algorithm for classification tasks in DG. First, we analyze that the objective of DG tasks is to maximize the inter-class distance within the same domain and minimize the intra-class distance across different domains. We design a domain adversarial selection method that prioritizes challenging samples in an active learning (AL) framework. Second, we hypothesize that even in a converged model, some feature subsets lack discriminatory power within each domain. We develop a method to identify and optimize these feature subsets, thereby maximizing inter-class distance of features. Lastly, We experimentally compare our DAAL algorithm with various DG and AL algorithms across four datasets. The results demonstrate that the DAAL algorithm can achieve strong generalization ability with fewer data resources, thereby significantly reducing data annotation costs in DG tasks.

关键词： Training Data models Data augmentation Adaptation models Uncertainty Representation learning Deep learning Costs Semantics classification algorithms Active learning (AL) domain generalization (DG) feature representation sample selection

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