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Cancer Regions in Mammogram Images Using ANFIS Classifier Based Probability Histogram Segmentation Algorithm

Intelligent Automation & Soft computing 2023年第7期37卷 707-726页

作者： V.Swetha G.Vadivu Department of Data Science and Business Systems School of ComputingSRM Institute of Science and TechnologyKattankulathur CampusChennai603203India

Every year,the number of women affected by breast tumors is increasing ***,detecting and segmenting the cancer regions in mammogram images is important to prevent death in women patients due to breast *** conventional methods obtained low sensitivity and specificity with cancer region segmentation *** high-resolution standard mammogram images were supported by conventional methods as one of the main *** conventional methods mostly segmented the cancer regions in mammogram images concerning their exterior pixel *** drawbacks are resolved by the proposed cancer region detection methods stated in this *** mammogram images are clas-sified into normal,benign,and malignant types using the Adaptive Neuro-Fuzzy Inference System(ANFIS)approach in this *** mammogram classification process consists of a noise filtering module,spatial-frequency transformation module,feature computation module,and classification *** Gaussian Filtering Algorithm(GFA)is used as the pixel smooth filtering method and the Ridgelet transform is used as the spatial-frequency transformation *** statistical Ridgelet feature metrics are computed from the transformed coefficients and these values are classified by the ANFIS technique in this ***,Probability Histogram Segmentation Algo-rithm(PHSA)is proposed in this work to compute and segment the tumor pixels in the abnormal mammogram *** proposed breast cancer detection approach is evaluated on the mammogram images in MIAS and DDSM *** the extensive analysis of the proposed tumor detection methods stated in this work with other works,the proposed work significantly achieves a higher *** methodologies proposed in this paper can be used in breast cancer detection hospitals to assist the breast surgeon to detect and segment the cancer regions.

关键词： Mammogram cancer gaussian filter ridgelet classification

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Machine learning-based gene expression biomarkers to distinguish Zika and Dengue virus infections: implications for diagnosis

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VirusDisease 2024年第3期35卷 446-461页

作者： Zeba, Ayesha Rajalingam, Aruna Sekar, Kanagaraj Ganjiwale, Anjali Department of Life Science Bangalore University Laboratory for Structural Biology and Bio-Computing Department of Computational and Data Sciences Indian Institute of Science

Zika virus (ZIKV) and Dengue virus (DENV) infections cause severe disease in humans and are significant socio-economic burden worldwide. These flavivirus infections are difficult to diagnose serologically due to antigenic overlap. The phylogenetic analysis shows that ZIKV clusters with DENVs at a higher node of the phylogenetic tree with significant genomic and structural similarity. Our study aims to identify gene biomarkers for the classification of Dengue and Zika viral infections using machine learning algorithms and bioinformatics analysis. The gene expression count matrix for single-cell RNA sequencing dataset GSE110496 was analyzed using binary classifiers, namely Logistic regression, Support Vector Machines, Random Forest, and Decision trees. The GSE110496 dataset represents a unique study of the transcriptional and translational dynamics of DENV and ZIKV infections at 4-, 12-, 24-, and 48-h time points for human hepatoma (Huh7) cells. Out of which 24-h time point has been analyzed in this study, at the optimal threshold of viral molecules. Feature selection was performed using two different approaches Random Forest Classifier (RFC) for gene ranking and Recursive Feature Elimination (RFE). Out of which RFE, showed more accuracy and precision. The classification accuracy of 89.4% and the precision of 90% were obtained using selected 10 gene features. SCY1 Like Pseudokinase 3 (SCYL3), Chromosome 1 Open Reading Frame 112 (C1orf112), Complement factor H (CFH), Heme-binding protein 1 (HEBP1), Cadherin 1 (CDH1), Nibrin (NBN), Histone deacetylase 5 (HDAC5), nuclear receptor subfamily 0, group B, member 2 (NR0B2), Annexin A9 (ANXA9) and Alcohol dehydrogenase 6 (ADH6) are the proposed gene biomarkers in this study. The functional analysis of the reported biomarkers was performed using KEGG and GO with the WEB-based Gene SeT AnaLysis Toolkit (WebGestalt). The relationship of the selected biomarkers with DENV and ZIKV infections analyzed using a gene–gene interaction n

关键词： Biomarkers Dengue Machine learning Single cell RNA sequencing Zika

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Optimizing ASReview Simulations:A generic Multiprocessing Solution for“Light-data'and“Heavy-data'Users

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data Intelligence 2024年第2期6卷 320-343页

作者： Sergei Romanov Abel Soares Siqueira Jonathan de Bruin Jelle Teijema Laura Hofstee Rens van de Schoot Applied Data Science Department of Information and Computing ScienceFaculty of ScienceUtrecht University Netherlands eScienceCenter AmsterdamNL Department of Research and Data Management Services Information Technology ServicesUtrecht UniversityUtrechtthe Netherlands Department of Methodology and Statistics Faculty of Social and Behavioral SciencesUtrecht University

Active learning can be used for optimizing and speeding up the screening phase of systematic *** simulation studies mimicking the screening process can be used to test the performance of different machine-learning models or to study the impact of different training *** paper presents an architecture design withamultiprocessing computational strategyforrunningmanysuch simulation studiesinparallel,using the ASReview Makita workflow generator and Kubernetes software for deployment with cloud *** provide a technical explanation of the proposed cloud architecture and its *** addition to that,we conducted 1140 simulations investigating the computational time using various numbers of CPUs and RAM *** analysis demonstrates the degree to which simulations can be accelerated with multiprocessing computing *** parallel computation strategy and the architecture design that was developed in the present paper can contribute to future research with more optimal simulation time and,at the same time,ensure the safe completion of the needed processes.

关键词： Active learning Systematic review Simulation study Multiprocessing Cloud architecture

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Dental Biometrics Segmentation on Panoramic X-Ray Images using Computational Intelligence Approach 14

Dental Biometrics Segmentation on Panoramic X-Ray Images usi...

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14th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2023

作者： Sujithra, M. Rathika, J. Velvadivu, P. Abinanda, P. Gayathri, G. Rekha, V.S. Department of Computing – Data Science Coimbatore Institute of Technology Coimbatore India

Dental Biometric is a new field of study in the sector of biometrics identification. This technique can be sometimes used instead of the usual fingerprint biometric identification. In most of the cases, Dental Biometric credentials come handy to analyze the details of a dead person where, the Dental Biometrics of the person before death and the person after their death could potentially explain various reasons for their death and could justify their identity after death. Neural Networks have the potential to learn about anything using their complex construction structure. Here, x-ray copies of the dental teeth structure of a potential individual are given or fed to the network and the neural network with the help of an object detection platform such as OpenCV2 detects the teeth structure and can visualize the teeth structure with x-ray of that individual alone. As the neural is now able to see the teeth, it can learn a lot of crucial details about it from the picture it can understand. The interpretation about the teeth contours and the number of teeth is made along the way. The proposed methodology has better accuracy than the fuzzy clustering relevant methods. Also Suggested to use the appropriate values of parameters that should be opted for the algorithm. © Grenze Scientific Society, 2023.

关键词： Object detection

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A Study on Psychological Stress of Working Women in Educational Institution Using Machine Learning 13

A Study on Psychological Stress of Working Women in Educatio...

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13th International Conference on computing Communication and Networking Technologies, ICCCNT 2022

作者： Sujithra, M. Velvadivu, P. Rathika, J. Priyadharshini, R. Preethi, P. Department of Computing - Data Science India Department of Computing - Data Science Coimbatore Institute of Technology India

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

Stress is a relationship between a person and environment which is one of the most common and serious health issues worldwide. Work life balance for working women is highly desirable and if there is no job satisfaction or consistency in life it will create a dilemma for women employees. Work life balance should be in an equilibrium state between personal and professional life. Balancing both personal and professional life is a stressful job for both men and women, which causes more problems physically and mentally. Working women get more stress than men because women undergo more stress taking care of children's, family members, household works and in working environment. In India, 87% of women gets more stress due to taking care of family and working environment. This causes severe problem when corresponding measures are not taken. This paper discusses the factor influencing the women to get more stress in educational institution and divides the project based on personal and professional life. © 2022 IEEE.

关键词： Machine learning

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Hybrid Recommender System for E-Learning Platforms 14

Hybrid Recommender System for E-Learning Platforms

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14th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2023

作者： Sujithra, M. Rajarajeshwari, K. Dhamodaran, B. Department of computing – Data Science Coimbatore Institute of Technology Coimbatore India

Internet use has steadily increased over the past 25 years. Numerous online services are improving and simplifying life for people. E-learning is one of these services, which makes the learning process simpler. In this work, a recommendation system will be presented that will assist online course providers in automating course suggestion. By finding patterns in courses based on some attributes like field of interest, skill set, educational qualification, course content, etc. of an individual and courses, this recommendation system is built using hybrid machine learning recommendation techniques, i.e., combination of Collaborative (item based) and content-based recommendation and KNN classifiers. Lastly, the recommendations are pooled from the two blocks of recommended courses (which were discovered using a collaborative technique), similar courses (which is found using content-based finding). © Grenze Scientific Society, 2023.

关键词： Collaborative filtering

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Two kinds of average approximation accuracy

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 481-490页

作者： Qingzhao Kong Wanting Wang Dongxiao Zhang Wenbin Zhang Department of Science Jimei UniversityXiamenChina Digital Fujian Big Data Modeling and Intelligent Computing Institute Jimei UniversityXiamenChina Department of Computer Science Michigan Technological UniversityHoughtonMichiganUSA

Rough set theory places great importance on approximation accuracy,which is used to gauge how well a rough set model describes a target ***,traditional approximation accuracy has limitations since it varies with changes in the target concept and cannot evaluate the overall descriptive ability of a rough set *** overcome this,two types of average approximation accuracy that objectively assess a rough set model’s ability to approximate all information granules is *** first is the relative average approximation accuracy,which is based on all sets in the universe and has several basic *** second is the absolute average approximation accuracy,which is based on undefinable sets and has yielded significant *** also explore the relationship between these two types of average approximation ***,the average approximation accuracy has practical applications in addressing missing attribute values in incomplete information tables.

关键词： rough sets rough set theory

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Hybrid intelligent system for channel allocation and packet transmission in CR-IoT networks

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International Journal of Hybrid Intelligent Systems 2024年第2期20卷 101-117页

作者： Asuquo, Daniel E. Umoh, Uduak A. Robinson, Samuel A. Dan, Emmanuel A. Udoh, Samuel S. Attai, Kingsley F. Department of Information Systems Faculty of Computing University of Uyo TETFund Center of Excellence in Computational Intelligence Research University of Uyo Department of Cyber Security Faculty of Computing University of Uyo Department of Computer Science Faculty of Computing University of Uyo Department of Data Science Faculty of Computing University of Uyo Department of Mathematics and Computer Science Ritman University Ikot Ekpene

The proliferation of interconnected devices is driving a surge in the demand for wireless spectrum. Meeting the need for wireless channel access for every device, while also ensuring consistent quality of service (QoS), poses significant challenges. This is particularly true for resource-limited heterogeneous devices within Internet of Things (IoT) networks. Cognitive radio (CR) technology addresses the shortcomings of traditional fixed channel allocation policies by enabling unlicensed users to opportunistically access unused spectrum belonging to licensed users. This facilitates timely and reliable transmission of mission-critical data packets. A cognitive radio-enabled IoT (CR-IoT) network is poised to better accommodate the growing demands of diverse applications and services within the smart city framework, spanning areas such as healthcare, agriculture, manufacturing, logistics, transportation, environment, public safety, and pharmaceuticals. To minimize switching delays and ensure energy and spectral efficiency, this study proposes a hybrid intelligent system for efficient channel allocation and packet transmission in CR-IoT networks. Leveraging Support Vector Machine (SVM) and Adaptive Neuro-Fuzzy Inference System (ANFIS), the system dynamically manages spectrum resources to minimize handoffs while upholding QoS. A Java-based simulation integrates system outputs with interference temperature data to accommodate service demands across 2G-4G spectrums. Evaluation reveals SVM's 98.8% accuracy in detecting spectrum holes and ANFIS's 90.4% accuracy in channel allocation. These results demonstrate significant potential for enhancing spectrum utilization in various IoT applications. © 2024-IOS Press. All rights reserved.

关键词： Cognitive radio network intelligent whitespace detection internet of things optimized channel allocation packet transmission

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Large Graphs with Small Burning Numbers

Large Graphs with Small Burning Numbers

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International Conference on Computational Engineering, ICCE 2023

作者： Rajasingh, Angelin Jemima Parthiban, N. Department of Data Science and Business System School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

In this paper, we have determined the burning number of graphs with universal vertices and graphs that are generated using the graph operation namely ‘join of graphs’. © The Author(s), under exclusive license t... 详细信息

ISBN: (纸本)9783031777639

关键词： Burning number Join of graphs Universal vertex

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A Hybrid Relational Approach Toward Stock Price Prediction and Profitability

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5844-5854页

作者： Patel, Manali Jariwala, Krupa Chattopadhyay, Chiranjoy Sardar Vallabhbhai National Institute of Technology Department of Computer Science and Engineering Surat395007 India FLAME University School of Computing and Data Sciences Pune412115 India

An accurate estimation of future stock prices can help investors maximize their profits. The current advancements in the area of artificial intelligence (AI) have proven prevalent in the financial sector. Besides, stock market prediction is difficult owing to the considerable volatility and unpredictability induced by numerous factors. Recent approaches have considered fundamental, technical, or macroeconomic variables to find hidden complex patterns in financial data. At the macro level, there exists a spillover effect between stock pairs that can explain the variance present in the data and boost the prediction performance. To address this interconnectedness defined by intrasector stocks, we propose a hybrid relational approach to predict the future price of stocks in the American, Indian, and Korean economies. We collected market data of large-, mid-, and small-capitalization peer companies in the same industry as the target firm, considering them as relational features. To ensure efficient feature selection, we have utilized a data-driven approach, i.e., random forest feature permutation (RF2P), to remove noise and instability. A hybrid prediction module consisting of temporal convolution and linear model (TCLM) is proposed that considers irregularities and linear trend components of the financial data. We found that RF2P-TCLM gave the superior performance. To demonstrate the real-world applicability of our approach in terms of profitability, we created a trading method based on the predicted results. This technique generates a higher profit than the existing approaches. © 2024 IEEE.

关键词： Profitability

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