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Automated system utilizing non-invasive technique mammograms for breast cancer detection

BMC MEDICAL IMAGING 2024年第1期24卷 1页

作者： Ammar, Hazem M. Tammam, Ashraf F. Selim, Ibrahim M. Eassa, Mohamed Thebes Higher Inst Engn Comp Sci Dept Cairo Egypt Arab Acad Sci Technol Maritime Transport AASTMT Coll Engn & Technol Comp Engn Dept Cairo Egypt Sadat City Univ Fac Comp & Artificial Intelligence Sadat City Egypt October 6 Univ Fac Informat Syst & Comp Sci Comp Sci Dept Giza Egypt

In order to increase the likelihood of obtaining treatment and achieving a complete recovery, early illness identification and diagnosis are crucial. Artificial intelligence is helpful with this process by allowing us to rapidly start the necessary protocol for treatment in the early stages of disease development. Artificial intelligence is a major contributor to the improvement of medical treatment for patients. In order to prevent and foresee this problem on the individual, family, and generational levels, Monitoring the patient's therapy and recovery is crucial. This study's objective is to outline a non-invasive method for using mammograms to detect breast abnormalities, classify breast disorders, and identify cancerous or benign tumor tissue in the breast. We used classification models on a dataset that has been pre-processed so that the number of samples is balanced, unlike previous work on the same dataset. Identifying cancerous or benign breast tissue requires the use of supervised learning techniques and algorithms, such as random forest (RF) and decision tree (DT) classifiers, to examine up to thirty features, such as breast size, mass, diameter, circumference, and the nature of the tumor (solid or cystic). To ascertain if the tissue is malignant or benign, the examination's findings are employed. These features are mostly what determines how effectively anything may be categorized. The DT classifier was able to get a score of 95.32%, while the RF satisfied a far higher 98.83 percent.

关键词： Breast cancer Decision tree supervised learning algorithms Classification Random forest

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Detection of denial of service using a cascaded multi-classifier

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2021年第4期24卷 405-416页

作者： Dhingra, Avneet Sachdeva, Monika IK Gujral Punjab Tech Univ Dept Comp Sci & Engn Kapurthala Punjab India

The paper proposes a cascaded multi-classifier two-phase intrusion detection (TP-ID) approach that can be trained to monitor incoming traffic for any suspicious data. It addresses the issue of efficient detection of intrusion in traffic and further classifies the suspicious traffic as a DDoS attack or flash event. Features portraying the behaviour of normal, DDoS attack, and flash event are extracted from historical data obtained after merging CAIDA'07, SlowDoS2016, CIC-IDS-2017, and WorldCup 1998 benchmark datasets available online along with the commercial dataset for e-shopping assistant website. Information gain is applied to rank and select the most relevant features. TP-ID applies supervised learning algorithms in the two phases. Each phase tests the set of classifiers, the best of which is chosen for building a model. The performance of the system is evaluated using the detection rate, false-positive rate, mean absolute percentage error, and classification rate. The proposed approach classifies the traffic anomalies with a 99% detection rate, 0.43% FPR, and 99.51% classification rate.

关键词： distributed denial of service DDoS attack flash event detection rate network security detection system supervised learning algorithms machine learning extra tree classifier ETC random forest

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Damage Location by Maximum Entropy Method on a Civil Structure 1

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34th IMAC Conference and Exposition on Structural Dynamics

作者： Villalpando, Pastor Meruane, Viviana Boroschek, Ruben Orchard, Marcos Univ Chile Dept Civil Engn Santiago Chile Univ Chile Dept Mech Engn Santiago Chile Univ Chile Dept Elect Engn Santiago Chile

ISBN: (数字)9783319297514

ISBN: (纸本)9783319297514;9783319297507

This article presents the application of a new damage assessment method based on a supervised learning algorithm that uses the principle of maximum entropy. The proposed algorithm employs, as reference information, the natural frequencies and mode shapes obtained with a numerical model of the structure in an undamaged condition and under different damage scenarios. The algorithm is validated with experimental tests on a laboratory six-levels-structure that is 2 m tall. The structure was anchored to a horizontal vibrating shaker table and it was excited at the support level by different seismic records and colored noise. The change in the structure from a normal condition to a damaged one is made by three consecutive reductions in the cross-section of a column. Additionally, a perturbation is introduced as a 0.5 % increase in the total mass of the structure. The experimental modal properties are identified by the Stochastic Subspace and Multivariable Output Error State sPace (MOESP) methods and they are compared with those obtained with a numerical model. The identification, location and quantification of damage that has been obtained with the proposed algorithm in the different test conditions, agrees well with the experimental damage, even for the conditions were few sensors are located in the structure.

关键词： Structural damage assessment supervised learning algorithms Maximum entropy principle Linear approximation Subspace based system identification

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Fuzzy motion adaptive algorithm for video de-interlacing

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10th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems

作者： Brox, P. Baturone, I. Sanchez-Solano, S. Gutierrez-Rios, J. Fernandez-Hernandez, F. IMSE CNM CSIC Seville 41012 Spain Univ Politecn Madrid Dpto Tecn Eoton Madrid 28660 Spain

ISBN: (纸本)3540465375

A motion adaptive algorithm for video de-interlacing is presented in this paper. It is based on a fuzzy inference system, which performs an interpolation between two linear techniques as a function of the motion level. Fuzzy systems with different number of 'if-then' rules have been analyzed and compared in terms of complexity as well as efficiency in de-interlacing benchmark video sequences.

关键词： video de-interlacing motion adaptive fuzzy inference systems supervised learning algorithms

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Identification of Fake Reviews Using New Set of Lexical and Syntactic Features 15

Identification of Fake Reviews Using New Set of Lexical and ...

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6th International Conference on Computer & Communication Technology (ICCCT-2015)

作者： Dewang, Rupesh Kumar Singh, A. K. CSED MNNIT Allahabad Allahabad Uttar Pradesh India

ISBN: (纸本)9781450335522

The services and products of E-Commerce portals in this digital age are heavily reviewed by the users. These reviews provide useful insights on the quality/usage of these products. Due to such importance of reviews, they can be faked to give false opinions about products and subsequently mislead the users. In this paper we are proposing new set of lexical and syntactic features set and applying supervised algorithms for performing classification on fake reviews dataset (gold standard). We focus on the writing style, that include type of punctuation mark, Part-of- Speech (POS) etc., that are helpful for detection of reviews spam. The final results give promising accuracy 91.51% for detecting fake reviews.

关键词： Opinion mining reviews spam lexical and syntactic features supervised learning algorithms

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Applying the Binary Classification Methods for Discovering the Best Friends on an Online Social Network 14

Applying the Binary Classification Methods for Discovering t...

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14th International Conference on Telecommunications (ConTEL)

作者： Stupalo, Maja Ilic, Juraj Humski, Luka Skocir, Zoran Pintar, Damir Vranic, Mihaela Ericsson Nikola Tesla Dd Krapinska 45 Zagreb 10000 Croatia Univ Zagreb Fac Elect Engn & Comp Unska 3 Zagreb 10000 Croatia

ISBN: (纸本)9789531842235

Online social networks (OSN) are one of the most widely adapted services of the Internet infrastructure, Facebook being one of the most popular among them. Facebook models connections between its users through the concept of "friendship". However, the type and intensity of these connections between different people on Facebook vary significantly. In most cases, friends on Facebook correspond to mere acquaintances in real-life, with only a smaller subset representing actual close friends. The aim of research presented in this paper is to provide a method for estimating the intensity of Facebook friendships, i.e., to distinguish connections representing close friends from others. The study was performed by analyzing Facebook interactions between users (e.g. number of mutual likes, comments, shared photos, etc.) using supervised learning algorithms for binary classification of data. Among the chosen algorithms, the best results were gained by using random forest algorithm - accuracy of 84.73%.

关键词： online social networks Facebook supervised learning algorithms close friends binary classification

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Modeling independent sales representative performance: application of predictive analytics in direct selling for improved outcomes

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JOURNAL OF MARKETING ANALYTICS 2023年第4期11卷 613-628页

作者： Glackin, Caroline Adivar, Murat Fayetteville State Univ 1200 Murchison Rd Fayetteville NC 28301 USA

This research supports improved salesforce outcomes for companies with independent sales representatives via the application of emerging technologies. It is framed in expectancy theory (in: Vroom, Work and motivation, Wiley, New York, 1964), Herzberg's et al. (The motivation to work, Wiley, New York, 1959) two factor theory, and the job demands and resources model (JD-R) (Bakker and Demerouti in J Manag Psychol 22:309-328, 2007). The study analyzes the 2018 National Salesforce Survey (USA) commissioned by the Direct Selling Association (Washington, DC, USA). It identifies key factors in salesforce motivation and performance theories studied within these theoretical frames and channel-specific inputs for direct selling independent representatives. While researchers apply multiple methods to salesforce motivation and performance, the power of machine learning has not been applied to independent representatives and their unique circumstances. This research employs supervised learning algorithms for predictive analytics to create models for recruits and existing independent representatives. It finds that the most crucial factors align with expectancy theory for both. Allocated time for direct selling, ability to find new customers, gender, and adopting direct selling as a career play key roles in predicting sales success for individuals entering direct selling. The highest-performing representatives are characterized by time invested, experience of direct selling, recruitment, tenure, and use of technology. By predicting representative success factors, organizations can tailor recruitment, training, and incentives to maximize performance. Because independent sales representatives are not employees, understanding these factors is critical to firms engaging them. Translating established theory into testing with predictive analytics to identify meaningful success factors for rarely studied independent sales representatives has the potential to change the landscape for recr

关键词： Independent sales representatives Salesforce performance Direct selling Predictive analytics supervised learning algorithms Expectancy theory

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Early Prediction of Diabetic Using Data Mining

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SN Computer Science 2023年第2期4卷 169页

作者： Jaber, Fayzeh Abdulkareem James, Joy Winston University of Technology Bahrain Salmabad Manama Bahrain

The World Health Organization (WHO) reports that in 2018, 422 million people throughout the globe are living with diabetes, making it one of the most widespread chronic life-threatening conditions. Early diagnosis is often favoured for clinically relevant findings due to the comparatively longer asymptomatic period associated with diabetes. It is estimated that around 50% of people with diabetes go undiagnosed because of the length of time it takes for symptoms to appear. The appropriate evaluation of both common and less common sign symptoms, which may be present at various times between the onset of the illness and diagnosis, is essential for early detection of diabetes. Researchers have relied heavily on data mining-based categorization algorithms for illness risk prediction models. To estimate a person’s risk of developing diabetes, it is required to have access to data on people who have recently developed diabetes or who are at high risk of developing diabetes. A dataset of 768 instances was provided to us via Kaggle and was created by the National Institute of Diabetes and Digestive and Kidney Diseases. This set of examples was narrowed down from a bigger database using a variety of criteria. All our female patients are at least 21 years old and are indigenous Pima. We performed statistical analysis on the dataset using the Naïve–Bayes Algorithm, the Logistic Regression Algorithm, and the Random Forest Algorithm. We found that Random Forest provided the best accuracy for this dataset when evaluated using both ten-fold Cross- Validation and the percentage split method. The National Institute of Diabetes and Digestive and Kidney Diseases is the original source of this data. The goal is to diagnose a patient and then forecast whether they have diabetes based on those results. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Data mining Dataset Diabetes risk Evaluation model Mining tools supervised learning algorithms Symptom Unsupervised learning algorithms

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Smart Machine Box with Early Failure Detection for Automatic Tool Changer Subsystem of CNC Machine Tool in the Production Line 47

Smart Machine Box with Early Failure Detection for Automatic...

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47th Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Lin, Shang-Chih Su, Shun-Feng Huang, Yennun Yeong Chin Machinery Ind Co Ltd Intelligent Mfg Dev Ctr Taichung Taiwan Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei Taiwan Acad Sinica Res Ctr Informat Technol Innovat Taipei Taiwan

ISBN: (纸本)9781665435543

This research aims to propose an innovative smart system we developed for early failure detection of Automatic Tool Change (ATC) systems. Input data is the system's tool magazine door open/close signals. Then, 41 indicators from 26 machines are obtained from statistics-based feature extraction methods. Under the guidance of predefined risk levels, nine high-ranking top level indicators are selected using correlation and regression analysis. In addition, some lightweight supervised learning algorithms are used to build and train the model to solve the classification problem of the system states, such as Normal, Caution, and Danger. The experimental results confirm that the high-ranking indicators can achieve the most prominent and stable performance under a series of tests. Under 10-fold cross-validation, the average accuracy is 89.43 %, which is 19 similar to 38 % higher than those of other feature groups. Among them, the Naive Bayes algorithm obtains the best accuracy of 94.2 %. This proves that the proposed smart system can effectively grasp the health status of the ATC systems.

关键词： CNC machine tool Automatic tool changer Early failure detection supervised learning algorithms

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Determining Twitter Trending Hashtags and Sentiments Associated via Machine learning Approaches

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SN Computer Science 2024年第8期5卷 1002页

作者： Bisht, Tanvi Singh, Muskan Singhal, Shweta Indira Gandhi Delhi Technical University for Women Delhi India

Social media platforms such as Twitter serve as a powerful tool for real-time information dissemination and worldwide communication, shaping public opinions and providing a platform for diverse voices to be heard. The current study proposes methods to identify trending hashtags on Twitter over a six-month period (June to October 2021) and analyze the sentiment dynamics linked to these hashtags. In order to carry out trend analysis three classification algorithms—Naive Bayes, Gradient Boosting Decision Tree, and Random Forest—were evaluated. The results indicate that the Random Forest model outperformed the other two algorithms in accurately identifying trending hashtags. In addition to this, a comprehensive sentiment analysis was conducted using both rule-based and lexicon-based approaches. The VADER sentiment analysis was utilized as the foundation for the Support Vector Machine classifier which achieved an accuracy of 91.84%, while the TextBlob library provided additional sentiment insights. The majority of research results, leaned towards neutrality, indicating challenges in capturing sentiment complexities present in Twitter conversations. The findings highlight the effectiveness of machine learning approaches, while also recognizing the potential for deep learning techniques to enhance sentiment analysis in future research. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Predictive modeling Sentiment analysis Social media analytics supervised learning algorithms Trending hashtags

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