Collection Of Datasets Research Articles

Sensor-level MEG combined with machine learning yields robust classification of mild traumatic brain injury patients

ObjectiveDiagnosis of mild traumatic brain injury (mTBI) is challenging despite its high incidence, due to the unspecificity and variety of symptoms and the frequent lack of structural imaging findings. There is a need for reliable and simple-to-use diagnostic tools that would be feasible across sites and patient populations. MethodsWe evaluated linear machine learning (ML) methods’ ability to separate mTBI patients from healthy controls, based on their sensor-level magnetoencephalographic (MEG) power spectra in the subacute phase (<2 months) after a head trauma. We recorded resting-state MEG data from 25 patients and 25 age-sex matched controls and utilized a previously collected data set of 20 patients and 20 controls from a different site. The data sets were analyzed separately with three ML methods. ResultsThe median classification accuracies varied between 80 and 95%, without significant differences between the applied ML methods or data sets. The classification accuracies were significantly higher with ML than with traditional sensor-level MEG analysis based on detecting pathological low-frequency activity. ConclusionsEasily applicable linear ML methods provide reliable and replicable classification of mTBI patients using sensor-level MEG data. SignificancePower spectral estimates combined with ML can classify mTBI patients with high accuracy and have high promise for clinical use.

Penerapan Particle Swarm Optimization Untuk Meningkatkan Kinerja Algoritma K-Nearest Neighbor Dalam Klasifikasi Penyakit Diabetes

The study aims to apply the KNN algorithm to classify diabetes by combining the PSO algorithm as a selection so as to obtain the best accuracy from the KNN algorithm in classifying, so that it can be applied to diagnose diabetes. This research consists of several stages of research including dataset collection, data pre-processing, data sharing, finding the optimal k-value to the classification process and performance or accuracy testing. From this research, the accuracy of the KNN algorithm before feature selection using PSO is 75% at k-optimal 19 and after feature selection using PSO is obtained an increase in accuracy to 77.213% at the same k value with features that affect the pima dataset, namely Glucose, Blood Pressure, Skin Thickness, Insulin, BMI, Diabetes Pedigree Function, and Age. Thus the use of KNN with PSO feature selection can be used to identify diabetes because it has a much better level of accuracy.

Prediksi Ketebalan Powder Coating Menggunakan Algoritma SVM Dan Naïve Bayes

Data Mining is a method that has been widely used to make scientific discoveries from a collection of datasets which so far have only been stored without further management. In the industrial world the use of data mining methods has helped with problems that are often found in the industrial field. Data mining helps in making predictions regarding thickness quality problems in a panel box product. Data mining is very useful for finding patterns in complex manufacturing data processing processes. Especially when we talk about consumers or service users of our product panels who want the panel to have good powder coating quality. This made the researchers conduct research to find the accuracy value which would later be used as a definite reference regarding the thickness of the powder coating. The results of this test the svm algorithm is better than naïve Bayes because the data in general can be categorized as a good result which has an accuracy of 97.60%, precision 99.56% and 96.03% recall. This res ult is an illustration for consumers to ensure that the panels to be purchased are of the best quality. By showing the data that has been processed, the consumer is sure that the purchase is really valid&#x0D;

Determining the Rice Seeds Quality Using Convolutional Neural Network

Seed inspection is crucial for plant nurseries and farmers as it ensures seed quality when growing seedlings. It is traditionally accomplished by expert inspectors filtering samples manually, but there are some challenges, such as cost, accuracy, and large numbers. Speed and accuracy were the main conditions for increasing agricultural productivity. Machine learning is a sub-science of Artificial Intelligence that can be applied in research on the classification of rice seed quality. The pipeline of a machine learning system is dataset collection, training, validation, and testing. Model making begins with taking data on the characteristics of rice seeds based on physical parameters in the form of seed shape and color. The dataset used is two thousand images divided into two categories, namely superior seeds and non-superior seeds. Training and Validation was conducted using the Convolutional Neural Network (CNN) algorithm with the concept of cross-validation on Google Collaboratory notebooks. The ratio split of train data and validation data in modeling from a dataset is 80:20. The result of the model formed is a model with the development of a Deep Convolutional Neural Network (Deep CNN) that can classify the digital image data of rice seeds from the results of data calls uploaded into the system. The results of the experiment conducted on 30 test data can be analyzed so that the system can classify superior and non-superior seeds with a precision value of 93% and a recall of 95%.

Perancangan Awal Sistem Automatic Self-Checkout Untuk Produk Buah Berbasis CNN dan Sensor Berat Loadcell

&#x0D; &#x0D; &#x0D; The check-out system is one of the most important systems when we make purchases at a shopping center, shops, supermarkets, or mini markets. Determining different types of fruit is one of the problems faced in making payments, especially in supermarkets, because cashiers must determine the category labeling and weight of a particular fruit to determine the price. In this research, a design is proposed which utilizes Deep Learning in the field of image processing using the Convolutional Neural Network (CNN) algorithm utilizing RaspberryPi3. In making a classification system that uses deep learning, there are several main process stages, such as dataset collection, system design, training, and testing to see the results of the designed CNN model. The processed dataset in this research is a fruit image dataset derived from the Fruits-360. The results of the learning process obtained CNN models with 100% accuracy and loss of ≤ 0.026. The results of the CNN model that has been carried out will be combined with the load cell sensor which is expected to be used as an automatic self-checkout, especially for fruits. On the other hand, the calibration process of the loadcell weight measurement sensor is conducted which resulting an error measurement rate of 0.2 grams.&#x0D; &#x0D; &#x0D;

Towards sustainable agriculture: A lightweight hybrid model and cloud-based collection of datasets for efficient leaf disease detection

Agricultural sustainability is a crucial component of the global economy and faces several challenges, including plant diseases. However, the application of deep learning models in unmanned aerial vehicles (UAVs) to detect plant diseases is hindered by their computational complexity and the lack of public datasets and information in this field. In this paper, we have two objectives. Firstly, we present a cloud-based collection by compiling and analyzing 38 available public datasets, which simplifies the work of researchers by reducing the time spent searching for suitable datasets. Secondly, we propose a lightweight model named Tiny-LeViT based on the transformer architecture for efficient leaf disease classification in edge network contexts. Our experiments on five popular datasets show that the proposed model outperforms its competitors, achieving at least 9% higher frame rate while retaining comparable F1-scores with an average of 97.25% on both RTX workstation and edge devices.

O-120 Predicting Embryo Ploidy Status Using Time-lapse Images

Abstract Study question Can deep learning models using time-lapse images of embryo development be used to predict embryo ploidy and provide supplemental information to embryologists for clinical decision-making? Summary answer We developed a general MDBS-Ploidy model that uses time-lapse images and maternal age to predict embryo quality scores and ploidy status. What is known already Ploidy status, or the presence or lack of chromosomal abnormalities, is an important factor in a successful pregnancy. Embryos with abnormalities are classified as aneuploid, whereas those without are euploid. With the advent of artificial intelligence in computer vision and the collection of large IVF-related datasets combining images, videos, and clinical outcomes, a variety of methods have been developed to automatically assess embryo quality and other characteristics using images from time-lapse sequences. To our knowledge, a video classification model validated by heterogeneous data to predict both embryo quality score and ploidy is lacking. Study design, size, duration The training dataset for the model consisted of 1,998 time-lapse sequences captured by the Embryoscope®. Time-lapse image sequences consisted of around 360–420 frames captured during 5 days of development. PGT-A results were used as ground truth labels for all ploidy prediction tasks, with embryos classified as euploid or aneuploid. The dataset also included clinical information such as blastocyst score (BS) and maternal age at the time of oocyte retrieval. Participants/materials, setting, methods The MDBS-Ploidy consists of two steps. The first step is quality score prediction from day-5 time-lapse video input using a Bidirectional Long Short-Term Memory architecture with added output layers for multitasking. In the second step, the predicted score, in addition to maternal age, is used to predict the ploidy status of the embryo using a logistic regression model. We evaluated the performance of the MDBS-Ploidy using area under the receiver-operating-characteristic (AUROC) on a validation dataset. Main results and the role of chance For the MDBS-Ploidy quality score prediction module, the Pearson correlation between scores predicted by the MDBS-Ploidy and ground truth quality scores is 0.70 on the validation dataset, suggesting moderate correlation strength. As for the aneuploidy prediction module, the MDBS-Ploidy can discriminate between euploidy and aneuploidy with an AUROC of 0.76 ± 0.002. The MDBS-Ploidy performs comparably with a model trained on the embryologist-annotated blastocyst score. We replicate these comparative results in external validation datasets as well, namely the Embryoscope+® dataset with 1,000 embryos and the IVI Valencia Spain dataset with 543 embryos. Moreover, the MDBS-Ploidy is completely automated, requiring only time-lapse images from 96 to 112 hpi and maternal age to predict embryo ploidy status. This allows the MDBS-Ploidy to be adapted clinically without interrupting ongoing workflows. The MDBS-Ploidy also provides a certain level of explainability; embryologists can use the intermediate quality score (from the first module) to determine why an embryo is classified as a certain ploidy status. With a recall of 0.84 ± 0.004 for the validation dataset, the MDBS-Ploidy shows promise for successfully selecting euploid embryos. We believe that the MDBS-Ploidy can provide supplemental information for selecting the most viable embryo. Limitations, reasons for caution Our model primarily uses data from time-lapse microscopy. Clinics without access to this technology will be unable to use our model. Wider implications of the findings This model is clinically relevant for making decisions as to whether an embryo may be chromosomally normal. Automated quality score prediction is instrumental to embryologists who are currently annotating embryos manually. Future iterations of the model can theoretically be adopted into clinical practice because it is end-to-end and fully automated. Trial registration number not applicable

Stacking ensemble transfer learning based thermal displacement prediction system

In the precision machining industry, machine tools are usually affected by various factors during machining, and various machining errors generated accordingly. Where thermal error is one of the most common and difficult to control factors for machine tools. Therefore, in this study, six temperature sensors and an eddy current displacement meter are provided in a machine tool with 4-axis for dataset collection required for the model training, then data are organized and normalized. Next, data are introduced into a variety of learning models and validated by k -Fold cross-validation for predicting those nonlinear factors that affect the errors. At the end, predicted results are summarized and compared to find out the best two model with better predictive performance for pre-trained model of transfer learning. It observes better predicted results from a retraining conducted through applying Multilayer Perceptron (MLP) on these two pre-trained models, wherein MAE value as 0.40, RMSE as 0.52625 and R 2 score as 0.99696 respectively.

Transfer Learning for Renewable Energy Systems: A Survey

Currently, numerous machine learning (ML) techniques are being applied in the field of renewable energy (RE). These techniques may not perform well if they do not have enough training data. Additionally, the main assumption in most of the ML algorithms is that the training and testing data are from the same feature space and have similar distributions. However, in many practical applications, this assumption is false. Recently, transfer learning (TL) has been introduced as a promising machine-learning framework to mitigate these issues by preparing extra-domain data so that knowledge may be transferred across domains. This learning technique improves performance and avoids the resource expensive collection and labeling of domain-centric datasets; furthermore, it saves computing resources that are needed for re-training new ML models from scratch. Lately, TL has drawn the attention of researchers in the field of RE in terms of forecasting and fault diagnosis tasks. Owing to the rapid progress of this technique, a comprehensive survey of the related advances in RE is needed to show the critical issues that have been solved and the challenges that remain unsolved. To the best of our knowledge, few or no comprehensive surveys have reviewed the applications of TL in the RE field, especially those pertaining to forecasting solar and wind power, load forecasting, and predicting failures in power systems. This survey fills this gap in RE classification and forecasting problems, and helps researchers and practitioners better understand the state of the art technology in the field while identifying areas for more focused study. In addition, this survey identifies the main issues and challenges of using TL for REs, and concludes with a discussion of future perspectives.

Transfer Active Learning Framework to Predict Thermal Comfort

Personal thermal comfort is the feeling that individuals have about how hot, cold or comfortable they are. Studies have hown that thermal comfort is a key component of human performance in the work place and that personalized thermal comfort models can be learned from user labeled data that is collected from wearable devices and room sensors. These personalized thermal comfort models can then be used to optimize the thermal comfort of room occupants to maximize their performance. Unfortunately, personalized thermal comfort models can only be learned after extensive dataset collection and user labeling. This paper addresses this challenge by proposing a transfer active learning framework for thermal comfort prediction that reduces the burdensome task of collecting large labeled datasets for each new user. The framework leverages domain knowledge from prior users and an active learning strategy for new users that reduces the necessary size of the labeled dataset. When tested on a real dataset collected from five users, this framework achieves a 70% reduction in the required size of the labeled dataset as compared to the fully supervised learning approach. Specifically, the framework achieves a mean error of 0.822±0.05, while the supervised learning approach achieves a mean error of 0.852±0.04.

Improving Mental Health Classifier Generalization with Pre-diagnosis Data

Recent work has shown that classifiers for depression detection often fail to generalize to new datasets. Most NLP models for this task are built on datasets that use textual reports of a depression diagnosis (e.g., statements on social media) to identify diagnosed users; this approach allows for collection of large-scale datasets, but leads to poor generalization to out-of-domain data. Notably, models tend to capture features that typify direct discussion of mental health rather than more subtle indications of depression symptoms. In this paper, we explore the hypothesis that building classifiers using exclusively social media posts from before a user's diagnosis will lead to less reliance on shortcuts and better generalization. We test our classifiers on a dataset that is based on an external survey rather than textual self-reports, and find that using pre-diagnosis data for training yields improved performance with many types of classifiers.

Simple kinesthetic haptics for object recognition

Object recognition is an essential capability when performing various tasks. Humans naturally use either or both visual and tactile perception to extract object class and properties. Typical approaches for robots, however, require complex visual systems or multiple high-density tactile sensors which can be highly expensive. In addition, they usually require actual collection of a large dataset from real objects through direct interaction. In this paper, we propose a kinesthetic-based object recognition method that can be performed with any multi-fingered robotic hand in which the kinematics is known. The method does not require tactile sensors and is based on observing grasps of the objects. We utilize a unique and frame invariant parameterization of grasps to learn instances of object shapes. To train a classifier, training data is generated rapidly and solely in a computational process without interaction with real objects. We then propose and compare between two iterative algorithms that can integrate any trained classifier. The classifiers and algorithms are independent of any particular robot hand and, therefore, can be exerted on various ones. We show in experiments, that with few grasps, the algorithms acquire accurate classification. Furthermore, we show that the object recognition approach is scalable to objects of various sizes. Similarly, a global classifier is trained to identify general geometries (e.g., an ellipsoid or a box) rather than particular ones and demonstrated on a large set of objects. Full scale experiments and analysis are provided to show the performance of the method.

Classification of COVID-19 Chest X-Ray Images Based on Speeded Up Robust Features and Clustering-Based Support Vector Machines

Abstract Due to the worldwide deficiency of medical test kits and the significant time required by radiology experts to identify the new COVID-19, it is essential to develop fast, robust, and intelligent chest X-ray (CXR) image classification system. The proposed method consists of two major components: feature extraction and classification. The Bag of image features algorithm creates visual vocabulary from two training data categories of chest X-ray images: Normal and COVID-19 patients’ datasets. The algorithm extracts salient features and descriptors from CXR images using the Speeded Up Robust Features (SURF) algorithm. Machine learning with the Clustering-Based Support Vector Machines (CB-SVMs) multiclass classifier is trained using SURF features to classify the CXR image categories. The careful collection of ground truth Normal and COVID-19 CXR datasets, provided by worldwide expert radiologists, has certainly influenced the performance of the proposed CB-SVMs classifier to preserve the generalization capabilities. The high classification accuracy of 99 % demonstrates the effectiveness of the proposed method, where the accuracy is assessed on an independent test sets.

Query expansion based on modified Concept2vec model using resource description framework knowledge graphs

The enormous size of the web and the vagueness of the terms used to formulate queries still pose a huge problem in achieving user satisfaction. To solve this problem, queries need to be disambiguated based on their context. One well-known technique for enhancing the effectiveness of information retrieval (IR) is query expansion (QE). It reformulates the initial query by adding similar terms that help in retrieving more relevant results. In this paper, we propose a new QE semantic approach based on the modified Concept2vec model using linked data. The novelty of our work is the use of query-dependent linked data from DBpedia as training data for the Concept2vec skip-gram model. We considered only the top feedback documents, and we did not use them directly to generate embeddings; we used their interlinked data instead. Also, we used the linked data attributes that have a long value, e.g., “dbo: abstract”, as training data for neural network models, and, we extracted from them the valuable concepts for QE. Our experiments on the Associated Press collection dataset showed that retrieval effectiveness can be much improved when a skip-gram model is used along with a DBpedia feature. Also, we demonstrated significant improvements compared to other approaches.

Empirical Analysis of Supervised and Unsupervised Machine Learning Algorithms with Aspect-Based Sentiment Analysis

Abstract Machine learning based sentiment analysis is an interdisciplinary approach in opinion mining, particularly in the field of media and communication research. In spite of their different backgrounds, researchers have collaborated to test, train and again retest the machine learning approach to collect, analyse and withdraw a meaningful insight from large datasets. This research classifies the texts of micro-blog (tweets) into positive and negative responses about a particular phenomenon. The study also demonstrates the process of compilation of corpus for review of sentiments, cleaning the body of text to make it a meaningful text, find people’s emotions about it, and interpret the findings. Till date the public sentiment after abrogation of Article 370 has not been studied, which adds the novelty to this scientific study. This study includes the dataset collection from Twitter that comprises 66.7 % of positive tweets and 34.3 % of negative tweets of the people about the abrogation of Article 370. Experimental testing reveals that the proposed methodology is much more effective than the previously proposed methodology. This study focuses on comparison of unsupervised lexicon-based models (TextBlob, AFINN, Vader Sentiment) and supervised machine learning models (KNN, SVM, Random Forest and Naïve Bayes) for sentiment analysis. This is the first study with cyber public opinion over the abrogation of Article 370. Twitter data of more than 2 lakh tweets were collected by the authors. After cleaning, 29732 tweets were selected for analysis. As per the results among supervised learning, Random Forest performs the best, whereas among unsupervised learning TextBlob achieves the highest accuracy of 99 % and 88 %, respectively. Performance parameters of the proposed supervised machine learning models also surpass the result of the recent study performed in 2023 for sentiment analysis.

Universum parametric $$\nu $$-support vector regression for binary classification problems with its applications

AbstractUniversum data sets, a collection of data sets that do not belong to any specific class in a classification problem, give previous information about data in the mathematical problem under consideration to enhance the classifiers’ generalization performance. Recently, some researchers have integrated Universum data into the existing models to propose new models which result in improved classification performance. Inspired by these Universum models, an efficient parametric$$ \nu $$ν-support vector regression with Universum data ($$ {\mathfrak {U}} $$UPar-$$ \nu $$ν-SVR) is proposed in this work. This method, which finds two non-parallel hyperplanes by solving one optimization problem and considers heteroscedastic noise, overcomes some common disadvantages of the previous methods. The$$ {\mathfrak {U}} $$UPar-$$ \nu $$ν-SVR includes unlabeled samples that don’t belong to any class in the training process, which results in a quadratic programming problem. Two approaches are proposed to solve this problem. The first approach derives the dual formulation using the Lagrangian function and KKT conditions. Furthermore, a least squares parametric$$ \nu $$ν-support vector regression with Universum data (named LS-$$ {\mathfrak {U}} $$UPar-$$ \nu $$ν-SVR) is suggested to further increase the generalization performance. The LS-$$ {\mathfrak {U}} $$UPar-$$ \nu $$ν-SVR solves a single system of linear equations, instead of addressing a quadratic programming problem in the dual formulation. Numerical experiments on artificial, UCI, credit card, NDC, and handwritten digit recognition data sets show that the suggested Universum model and its solving methodologies improve prediction accuracy.

A High-Precision Plant Disease Detection Method Based on a Dynamic Pruning Gate Friendly to Low-Computing Platforms.

Timely and accurate detection of plant diseases is a crucial research topic. A dynamic-pruning-based method for automatic detection of plant diseases in low-computing situations is proposed. The main contributions of this research work include the following: (1) the collection of datasets for four crops with a total of 12 diseases over a three-year history; (2) the proposition of a re-parameterization method to improve the boosting accuracy of convolutional neural networks; (3) the introduction of a dynamic pruning gate to dynamically control the network structure, enabling operation on hardware platforms with widely varying computational power; (4) the implementation of the theoretical model based on this paper and the development of the associated application. Experimental results demonstrate that the model can run on various computing platforms, including high-performance GPU platforms and low-power mobile terminal platforms, with an inference speed of 58 FPS, outperforming other mainstream models. In terms of model accuracy, subclasses with a low detection accuracy are enhanced through data augmentation and validated by ablation experiments. The model ultimately achieves an accuracy of 0.94.

Repetitive and restricted behaviors and interests in autism spectrum disorder: relation to individual characteristics and mental health problems

BackgroundAlthough repetitive and restricted behaviors and interests (RRBIs) may interfere with well-being and functioning in autistic individuals, research on their relation to sex, age, cognitive level, and mental health problems remains unclear. Much of the research to date has used broad categorizations rather than specific categorizations of RRBIs to examine the difference in RRBIs between individuals. The purpose of this study was to explore, in different groups of individuals, the presence of specific RRBI subtypes, and to examine the association of specific RRBI subtypes with symptoms of internalizing and externalizing behaviors.MethodsSecondary data analyses were conducted using the Simons Simplex Collection dataset, which included 2,758 participants (aged 4 to 18). Families of autistic children completed the Repetitive Behavior Scale–Revised (RBS-R) and the Child Behavior Checklist.ResultsAcross all RBS-R subtypes, results revealed no sex differences. Older children showed higher rates of Ritualistic/Sameness behaviors than younger children and adolescents, whereas younger and older children showed more Stereotypy than adolescents. Additionally, lower cognitive level groups showed higher rates of RBS-R subtypes except for Ritualistic/Sameness. After controlling for age and cognitive level, RBS-R subtypes accounted for a substantial amount of variance in internalizing and externalizing behaviors (23% and 25%, respectively). Specifically, Ritualistic/Sameness and Self-Injurious Behavior both predicted internalizing and externalizing behaviors, whereas Stereotypy only predicted internalizing behavior.ConclusionsThese findings have key clinical implications that emphasize not only the consideration of sex, age, and cognitive level, but also specific RRBIs and co-occurring mental health problems, when assessing for ASD and designing individualized interventions.

Chasing Biodiversity Off the Scientific and Conservation Tracks

The idea of conserving biodiversity has become central to the very meaning of biological conservation---in the public imagination and for conservation organizations worldwide. Identification of conservation with biodiversity conservation owes extensively to the idea that warrant for biodiversity's conservation is anchored in the empirical thesis that biodiversity causally determines ecosystem functioning, and thereby, somehow, important ecosystem services. This idea has fueled an enormous research program dedicated to producing the requisite causal evidence. This essay first reviews the data that are supposed to constitute direct evidence for biodiversity's causal influence. It proceeds by answering a heretofore unasked, yet foundational, question for causal hypotheses: Do these data meet basic requirements for credibility as causal evidence? By virtue of mistakenly reading causal significance into (i) massive numbers of causally irrelevant data points, (ii) an equation that simply equates a stipulated definition to an algebraically equivalent expression, and (iii) correlations produced by arbitrary computations over previously collected data sets, these data fall well short of meeting these requirements. Mistakes also suffuse the notion that biodiversity's supposed causal influence gives reasons to conserve it. These mistakes are exposed when the conservation argument that was supposed to proceed from the premise of biodiversity's causal influence is clearly spelled out. Once made explicit, each step is seen to rely on a questionable assumption, invalid logic, or both. The essay concludes with implications for conservation, biodiversity research, and scientific inquiry more generally.

Mutational signatures for breast cancer diagnosis using artificial intelligence

BackgroundBreast cancer is the most common female cancer worldwide. Its diagnosis and prognosis remain scanty, imprecise, and poorly documented. Previous studies have indicated that some genetic mutational signatures are suspected to lead to progression of various breast cancer scenarios. There is paucity of data on the role of AI tools in delineating breast cancer mutational signatures. This study sought to investigate the relationship between breast cancer genetic mutational profiles using artificial intelligence models with a view to developing an accurate prognostic prediction based on breast cancer genetic signatures. Prior research on breast cancer has been based on symptoms, origin, and tumor size. It has not been investigated whether diagnosis of breast cancer can be made utilizing AI platforms like Cytoscape, Phenolyzer, and Geneshot with potential for better prognostic power. This is the first ever attempt for a combinatorial approach to breast cancer diagnosis using different AI platforms.MethodArtificial intelligence (AI) are mathematical algorithms that simulate human cognitive abilities and solve difficult healthcare issues such as complicated biological abnormalities like those experienced in breast cancer scenarios. The current models aimed to predict outcomes and prognosis by correlating imaging phenotypes with genetic mutations, tumor profiles, and hormone receptor status and development of imaging biomarkers that combine tumor and patient-specific features. Geneshotsav 2021, Cytoscape 3.9.1, and Phenolyzer Nature Methods, 12:841–843 (2015) tools, were used to mine breast cancer-associated mutational signatures and provided useful alternative computational tools for discerning pathways and enriched networks of genes of similarity with the overall goal of providing a systematic view of the variety of mutational processes that lead to breast cancer development. The development of novel-tailored pharmaceuticals, as well as the distribution of prospective treatment alternatives, would be aided by the collection of massive datasets and the use of such tools as diagnostic markers.ResultsSpecific DNA-maintenance defects, endogenous or environmental exposures, and cancer genomic signatures are connected. The PubMed database (Geneshot) search for the keywords yielded a total of 21,921 genes associated with breast cancer. Then, based on their propensity to result in gene mutations, the genes were screened using the Phenolyzer software. These platforms lend credence to the fact that breast cancer diagnosis using Cytoscape 3.9.1, Phenolyzer, and Geneshot 2021 reveals high profile of the following mutational signatures: BRCA1, BRCA2, TP53, CHEK2, PTEN, CDH1, BRIP1, RAD51C, CASP3, CREBBP, and SMAD3.