Akwasi Oppong, Jerry Galley · Zenodo (CERN European Organization for Nuclear Research) · 2026
This research aims to investigate the use of data analysis and machine learning techniques to classify breast cancer tumors as benign or malignant based on the features of the cells as they appear in digital images. The dataset consists of 600 data points and nine variables. The variables include the radius of the cell, texture of the cell, the cell's perimeter, the cell's area, smoothness of the cell, compactness of the cell, symmetry of the cell, and fractal dimension of the cell. Exploratory data analysis was conducted to evaluate the variables' distribution and variability. Several classification algorithms were implemented and tested on the dataset. The algorithms included Logistic Regression, Decision Trees, Random Forests, and Gradient Boosting. The accuracy of the algorithms was also evaluated. The results showed that the accuracy of the algorithms was around 51.7%, indicating that the features were challenging to use in the classification of the types of tumors.
Cited by 0Open access
Leila Megouache, Abdelhafid Zitouni, Salheddine Sadouni et al. · Ingénierie des systèmes d information · 2024
International audience
Cited by 1Open access
Mikhaïl Kanevski, V. Timonin, Loris Foresti et al. · IRIS · 2010
The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.
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Tamal K. Dey, Sayan Mandal, Soham Mukherjee · BMC Bioinformatics · 2021
In this work, we engender representative persistent cycles to discern the gene expression data. These cycles allow us to directly procure genes entailed in similar processes.
Cited by 4Open access
Sumanta Kuila · International Journal of Recent Technology and Engineering (IJRTE) · 2020
Electrocardiogram (ECG) is the analysis of the electrical movement of the heart over a period of time. The detailed information about the condition of the heart is measured by analyzing the ECG signal. Wavelet transform, fast Fourier transform are the different methods to disorganize cardiac disease. The paper elaborates the survey on ECG signal analysis and related study on arrhythmic and non arrhythmic data. Here we discuss the efficient feature extraction process for electrocardiogram, where based on position and priority six best P-QRS-T fragments are studied. This survey examines the the outcome of the system by using various Machine learning classification algorithms for feature extraction and analysis of ECG Signals. Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Artificial Neural Network (ANN) are the most important algorithms used here for this purpose. There are several publicly available data sets which are used for arrhythmia analysis and among them MIT-BIH ECG-ID database is mostly used. The drawbacks and limitations are also discussed here and from there future challenges and concluding remarks can be done.
Cited by 1Open access
Kacim Mohammad, Assaf Rabih, Mourad Oumar et al. · 2025
Brain cancer is a life-threatening disease that necessitates accurate and early detection to improve patient outcomes. Magnetic Resonance Imaging (MRI) is the standard diagnostic technique for identifying brain tumors; however, manual interpretation by radiologists can be subjective, leading to inconsistencies in diagnosis. Recent advancements in Machine Learning (ML) have significantly improved tumor classification accuracy, but traditional methods often rely solely on pixel-based features, which may not fully capture the morphological complexity of tumors. This study explores the integration of Topological Data Analysis (TDA) with ML models to enhance brain tumor classification. Specifically, persistent homology, a core TDA technique, is leveraged to extract structural and geometric features from MRI scans, complementing conventional statistical features without the implication of any preprocessing technique. Four ML classifiers—Decision Tree, Naïve Bayes, Support Vector Machine (SVM), and Random Forest—were evaluated. The results demonstrate that incorporating topological features, and especially the novel use of persistent entropy, significantly enhances classification accuracy. The Random Forest classifier achieved the highest performance, with an accuracy of 98.12% and an AUC score of 0.9974. These findings suggest that TDA-based approaches provide valuable complementary information for tumor classification, paving the way for more robust and automated brain cancer detection systems.
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Associate Professor, JNTUHCEH. Kukatpally, Hyderabad, India., B. Kranthi Kiran, Padmaja Pulicherla · International Journal of Engineering and Advanced Technology · 2019
These days’ data gathered is unstructured. It is becoming very hard to have labelled data gathered, due to the volume of the data being generated every second. It is almost impossible to train a model on the unstructured/unlabelled data. The unlabelled data will be divided into groups using the ML techniques and CNN/Deep learning/Machine Learning techniques will be trained using the grouped data generated. The model will be enhanced over time by the feedback given by the users and with addition of new data as well. Existing models can be trained over labelled data only. Without labelled data models cannot be used for prediction and reinforcement learning. In this approach though the data is unlabelled if a feature column is specified we will be able to train the model with the help of SME. This will be helpful in many areas of classification and prediction of the trends and patterns. Machine learning, Deep learning techniques (Supervised) will be used to implement the data. Tools used will be Python, PyTorch and TensorFlow. Input can be any data (Audio/Video/pictographic/text). Labelled data and a model file which could be used for further predictions, and which will be improved over feedback.
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T Manivannan, A.S. Sahana, P. K. Rajani et al. · 2025
Since cardiovascular disease (CVD) is still one of the world's leading causes of death, prompt intervention depends on early identification. Conventional diagnostic techniques depend on the manual interpretation of biological data, which can be laborious and error-prone. Using biological data, this study investigates the use of techniques based on machine learning (ML) for CVD detection and classification. Accuracy, recall, accuracy, and F1-score are used to compare and evaluate different categorization models, such as Support Vector Machines (SVM), Random Forests (RF), & artificial neural networks (ANNs). Important biomedical variables like blood pressure, cholesterol, beat variability, and ECG values are included in the collection. Techniques for feature selection are used to maximize the prediction power of the model. The experimental findings show that ML-based classification greatly improves CVD detection accuracy. Important biomedical variables like blood pressure, cholesterol, heart rate variation, and ECG values are included in the collection. Techniques for feature selection are used to maximize the prediction power of the model. When compared to conventional techniques, the experimental results show that ML- based classification greatly improves the efficacy of CVD detection. The results highlight how ML might help medical practitioners diagnose diseases early, which can eventually improve patient outcomes.
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Shemim Begum, Debasis Chakraborty, Ram Sarkar · 2015
The k Nearest Neighbour (kNN) method is one of the most popular algorithm in clustering and data classification. The kNN algorithm founds to be performed very efficient in the experiments on different dataset. In this paper, we focus on the classification problem. The algorithm is experienced over Leukemia dataset. Initially three feature selection algorithm Consistency Based Feature Selection (CBFS), Fuzzy Preference Based Rough Set (FPRS) and Kernelized Fuzzy Rough Set (KFRS) is applied on the dataset and then kNN is applied as a classifier onto the dataset. The results of our experiment demonstrates that CBFS algorithm generally perform better than other two KFRS and FPRS algorithm respectively.
Cited by 49
Sujata Panda, Hima Bindu Maringanti · Communications in computer and information science · 2021
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Jichao Chen · 2021
Many real-world applications deal with data that have multiple feature sets. Such data is called multiview data, where each feature set is considered as a view. Many machine learning tasks have their multiview versions, which have similar objectives but dealing with multiview data. For example, the most general classification and clustering tasks have their multiview versions. Multiview classification aims to categorize multiview data into predefined classes. It is quite useful when dealing with complicated objects presented in different formats (e.g., 3D models). Multiview clustering separates the multiview data into clusters by considering their pairwise similarities in all views. It is widely used in feature learning and data labeling. Multiview spectral clustering is one of the most popular kinds of multiview clustering algorithms, which leverages eigendecomposition on the graph Laplacian to find the clusters. Meanwhile, extreme learning machine (ELM) is attracting more and more attention for its promising performance in various real-world applications. Therefore, the thesis investigates ELM for classification and clustering on multiview data. Firstly, the thesis investigates multiview 3D shape classification. The thesis extends the idea of ELM's random neurons to octree based operations and proposes octree based convolutional autoencoder extreme learning machine (OCA-ELM) for 3D shape classification. The ELM auto-encoder (ELM-AE) is adopted for feature extraction and feature fusion, and an ELM classifier is ultilized for the final classification. The experimental results prove that the input and operations based on octree can improve the classification performance. Secondly, the thesis investigates spectral clustering with ELM. ELM is a training algorithm for single-layer feedforward networks, famous for its nonlinear feature mapping. The thesis develops a clustering algorithm called unsupervised feature selection based extreme learning machine (UFS-ELM). The proposed algorithm first transfers the data into a hidden space with nonlinear feature mapping of the ELM. It reduces the dimensionality of features to the desired number of clusters while preserving the graph structure. An unsupervised feature selection method is embedded in the formulation to eliminate the worthless hidden neurons. With a simple constraint, the method can directly output the clustering result without post-processing. Experimental results demonstrate the effectiveness of UFS-ELM on a wide range of datasets. Thirdly, the thesis explores spectral clustering on multiview data. The views of multiview data might have different data types, scales, and data distributions. The information of multiple views needs to be fused into a single one at some moment to derive a consistent clustering result across different views. Existing multiview spectral clustering methods fuse multiple similarity matrices or Laplacian matrices into a common matrix. This thesis proposes a term of distance fusion that fuses the information from the distance perspective. Specifically, the proposed method provides a comprehensive distance measure of a unified scale. A common distance can be produced by combining multiple distance matrices without weighting. With the distance fusion, the thesis presents a novel multiview spectral clustering method called distance fusion with adaptive neighbors (DFAN). An empirical study on several multiview datasets shows that the proposed DFAN method surpasses the state-of-the-art multiview spectral clustering methods. The experimental results also show that the proposed method enlarges the distances between different clusters and is robust to views with misleading information. Finally, the thesis proposes to incorporate extreme learning machine with multiview spectral clustering. Existing multiview spectral clustering methods construct a similarity matrix based on the pairwise distances measured in the original data space or linearly projected space. However, the actual data structure might lie on a complex nonlinear manifold. In this thesis, the extreme learning machine is adopted to learn a nonlinearly mapped embedding space where the intrinsic structure is preserved. The distances measured in both embedding space and the original space can be fused to derive a more robust distance measure for each view. Massive experiments demonstrate the proposed multiview clustering method, termed dual distance adaptive multiview clustering (DAMC), is superior to the state-of-the-art multiview clustering methods.
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Akwasi Oppong, Jerry Galley · Zenodo (CERN European Organization for Nuclear Research) · 2026
This research aims to investigate the use of data analysis and machine learning techniques to classify breast cancer tumors as benign or malignant based on the features of the cells as they appear in digital images. The dataset consists of 600 data points and nine variables. The variables include the radius of the cell, texture of the cell, the cell's perimeter, the cell's area, smoothness of the cell, compactness of the cell, symmetry of the cell, and fractal dimension of the cell. Exploratory data analysis was conducted to evaluate the variables' distribution and variability. Several classification algorithms were implemented and tested on the dataset. The algorithms included Logistic Regression, Decision Trees, Random Forests, and Gradient Boosting. The accuracy of the algorithms was also evaluated. The results showed that the accuracy of the algorithms was around 51.7%, indicating that the features were challenging to use in the classification of the types of tumors.
Cited by 0Open access
Aliea Salman Sabir, Huda Adil Ali, Maalim A. Aljabery · Informatica · 2024
Many things, such as goods, products, and websites are evaluated based on user's notes and comments. One popular research project is sentiment analysis, which aims to extract information from notes and comments as a natural language processing (NLP) to understand and express emotions. In this study we analyzed the sentiment of ChatGPT labeled tweet datasets sourced from the Kaggle community using five Machine Learning (ML) algorithms; decision tree, KNN, Naïve Bayes, Logistic Regression, and SVM. We applied three feature extraction techniques: positive/negative frequency, a bag of words (count vector), and TF IDF. For each classification algorithm. The results were assessed using accuracy measures. Our experiments achieved an accuracy of 96.41% with SVM classifier when using TF- IDF as a feature extraction technique.
Cited by 12Open access
Takashi Sonoke, H. Demura · TRANSACTIONS OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES AEROSPACE TECHNOLOGY JAPAN · 2020
SAR (Synthetic Aperture Radar) has been popular in Earth remote sensing because this is available for all-weather and night time, unlike optical sensors. Identification and classification of ships have been remarked from viewpoints of coast guard such as finding and monitoring marine accidents and poaching boats without Automatic Identification System (AIS). This research uses SAR data provided by PALSAR2/ALOS2. This research adopts the Convolutional Neural Network (CNN). The label is given to ship SAR image using AIS. Results with PALSAR 2 data with a resolution of 100 m/pixel showed high accuracy of 95%. This research showed the availability of a method to identification of ships from SAR images using CNN.
Cited by 2Open access
El Mehdi .., Amine Saddik · Fusion Practice and Applications · 2022
It is crucial to quickly identify plant diseases since they impede the development of affected plants. Despite the widespread use of Machine Learning (ML) models for this purpose, the recent advances in a subset of ML known as Deep Learning (DL) suggest that this field of study has much room for improvement in terms of detection and classification accuracy. To identify and categorize plant diseases, a wide variety of established and customized DL architectures are deployed with several visual analysis methods. In this study, we use deep learning techniques to create a model for a convolutional neural network that can identify and diagnose plant diseases using very basic photos of healthy and sick plant leaves. The models were trained using an open library of 20639 photos that included both healthy and diseased plants across 15 different classifications. Some model architectures were trained, with the highest performance obtaining a success rate of 97.70% in detecting the correct [plant, illness] pair (or healthy plant). Due to its impressive success rate, the model is a valuable advising or early warning tool, and its technique might be developed to help an integrated plant disease diagnosis system function in actual production settings.
Cited by 0
Sulbha Malviya · 2024
Recent advances in Artificial Intelligence (AI) have seen widespread applications across domains such as computer vision, natural language processing, and graph-based learning. Among these, Large Language Models (LLMs) have emerged as a cornerstone of modern AI research, with applications ranging from language understanding to content generation. As research in this field rapidly expands, newcomers face the challenge of navigating an overwhelming number of survey papers that attempt to consolidate knowledge on LLMs. This project addresses this issue by performing a comprehensive data exploration and analysis of metadata from recent LLM survey papers. Through the analysis of publication trends, citation patterns, and topical coverage, we aim to offer insights that simplify the discovery process for beginners and provide a clearer understanding of the current landscape. Our findings are intended to facilitate more efficient reading and research in the field of LLMs, enabling new researchers to quickly grasp key developments and emerging trends.
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I. J. Huang, Man-Chun Lee, Chung-Yuan Nieh et al. · Electronics · 2023
AIS ship-type code categorizes ships into broad classes, such as fishing, passenger, and cargo, yet struggles with finer distinctions among cargo ships, such as bulk carriers and containers. Different ship types significantly impact acceleration, steering performance, and stopping distance, thus making precise identification of unfamiliar ship types crucial for maritime monitoring. This study introduces an original classification study based on AIS data for cargo ships, presenting a classifier tailored for bulk carriers, containers, general cargo, and vehicle carriers. The model’s efficacy was tested within the Changhua Wind Farm Channel using eight classification algorithms across tree-structure-based, proximity-based, and regression-based categories and employing standard metrics (Accuracy, Precision, Recall, F1-score) to assess the performance. The results show that tree-structure-based algorithms, particularly XGBoost and Random Forest, demonstrated superior performance. This study also implemented a feature selection strategy with five methods, revealing that a model trained with only four features (three ship-geometric features and one trajectory behavior feature) can achieve high accuracy. Conclusively, the classifier effectively overcame the challenges of limited AIS data labels, achieving a classification accuracy of 97% for ships in the Changhua Wind Farm Channel. These results are pivotal in identifying abnormal ship behavior, highlighting the classifier’s potential for maritime monitoring applications.
Cited by 26Open access
Eng. Layla Dawood Almardoud, Hissam Tawfik, Soliman A. Mahmoud et al. · 2023
Alzheimer’s disease (AD) is a complex neurodegenerative disorder that presents significant challenges for early and accurate diagnosis. Early diagnostic and treatment strategies can help enhance the circumstances by slowing the progression of the illness and enhancing the patient and family’s quality of life. Machine learning (ML) approaches have shown promise in improving the diagnosis and prognosis of Alzheimer’s based on relevant risk factors. This paper aims to develop and evaluate a machine learning model for classifying Alzheimer’s, mild cognitive impairment (MCI), and normal cognition (NC) using a diverse data set from the ADNI database. The model had high performance with a sensitivity rate of up to 97%, accuracy rate of up to 94%, and specificity rate of up to 96%. Moreover, none of the Alzheimer’s cases were falsely detected as normal cognition but as mild cognitive impairment and none of the normal cognition cases were detected as Alzheimer’s, but as mild cognitive impairment. The algorithm that has the highest number of true positive detections, which is 78 out of 85 Alzheimer’s cases, is the decision tree algorithm. The performance of the system heralds a promising future for Alzheimer’s diagnosis by machine learning with the aim of developing smart health systems.
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Jianping Yao, Son N. Tran, Samantha Sawyer et al. · Artificial Intelligence Review · 2023
Abstract The growing demand for sustainable development brings a series of information technologies to help agriculture production. Especially, the emergence of machine learning applications, a branch of artificial intelligence, has shown multiple breakthroughs which can enhance and revolutionize plant pathology approaches. In recent years, machine learning has been adopted for leaf disease classification in both academic research and industrial applications. Therefore, it is enormously beneficial for researchers, engineers, managers, and entrepreneurs to have a comprehensive view about the recent development of machine learning technologies and applications for leaf disease detection. This study will provide a survey in different aspects of the topic including data, techniques, and applications. The paper will start with publicly available datasets. After that, we summarize common machine learning techniques, including traditional (shallow) learning, deep learning, and augmented learning. Finally, we discuss related applications. This paper would provide useful resources for future study and application of machine learning for smart agriculture in general and leaf disease classification in particular.
Cited by 51Open access
Marzieh Zare, Jussi-Pekka Lehtinen, Hesam Jafarian et al. · Proceedings of the international neural networks society · 2021
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