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International Conference on Information Systems and computer Networks (ISCON)

作者： Ashish Tripathi Kuldeep Kumar Anuradha Misra Brijesh Kumar Chaurasia Department of Computer Science and Engineering Amity School of Engineering & Technology Amity University Lucknow Lucknow Uttar Pradesh India Center for Data Analytics Bond Business School Bond University Gold Coast Queensland Australia Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur Uttar Pradesh India

In this paper, the classification of colon cancer tissues by means of machine learning approaches is evaluated. In today’s world, a revolutionary advancement has come in the classification and diagnosis of diseases in the medical and healthcare sectors. Deep learning classifiers and machine learning methods are now broadly applied to accurately diagnose a number of diseases. Cancer is one of the world’s most significant roots of death, appealing to the lives of one person out of every six. As per the national library of medicine, the third leading cause of death worldwide is colorectal cancer. Identifying an illness at a premature stage increases the chances of survival. Automated diagnosis and the classification of tissues from images can be completed much more quickly with the use of artificial intelligence. A publicly available IoT dataset CRC–VAL–HE–7K consisting of 7180 images, distributed among nine types of colorectal tissues: background, lymphocytes, adipose, mucus, colorectal adenocarcinoma epithelium, normal colon mucosa, debris, cancer-associated stroma, and, smooth muscle is used after preprocessing. Feature extraction is done by applying Differential-Box-Count on all blocks of images. The dataset is evaluated by these Machine Learning (ML) procedures: K-Nearest Neighbor, Support Vector Machine, Decision Tree, Random Forest, Extreme Gradient Boosting, and Gaussian Naive Bayes. Results show that the performance of Extreme Gradient Boosting is the best and most viable approach.

关键词： Deep learning Support vector machines Machine learning algorithms Transfer learning Feature extraction Boosting Colon

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Improved Dictionary Learning for FMRI data Analysis Capturing Common and Individual Activation Maps

Improved Dictionary Learning for FMRI Data Analysis Capturin...

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Asilomar Conference on Signals, Systems & computers

作者： Shuai Xu Rui Jin Seung-Jun Kim Tülay Adali Vince D. Calhoun Department of Computer Science and Electrical Engineering University of Maryland Baltimore County USA Tri-institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology and Emory University USA

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

A dictionary learning (DL)-based method for mul-tisubject functional magnetic resonance imaging (fMRI) data analysis is proposed. The method can incorporate group attributes to find the neural activation maps that are common across groups as well as those that are explanatory of the group differences. The key contribution is to significantly improve the map estimation performance by mitigating undesirable local optima obtained from a supervised DL formulation for fMRI data analysis. We introduce two novel ideas, namely, developing a variant of Fisher's discriminant cost, which is imposed on the non-discriminative features, and optimally permuting the dictionary and the sparse factor such that the solution is not trapped to unwanted local optima. Preliminary tests on synthetic and real data sets verify the effectiveness of the proposed approach.

关键词： computers data analysis Dictionaries Costs Estimation Machine learning Functional magnetic resonance imaging

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Dermatological Diagnostics: A Unified Deep Learning Framework for Skin Lesion and Cancer Classification

Dermatological Diagnostics: A Unified Deep Learning Framewor...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Vinod Kumar Isha Jain Durga Sritha Dongla Shivangi Rai Vani Rastogi Eva Kaushik Rohit Kaushik School of Engineering & Applied Science The George Washington University Washington D.C USA Computer Science & Engineering Department Dr. Akhilesh Das Gupta Institute of Technology and Management New Delhi India Computer Science & Engineering Department Gandhi Institute of Technology and Management Visakhapatnam Andhra Pradesh Data Scientist DXC Technology Noida India CS and Mathematics Department Data Analytics Program University of Illinois Springfield USA

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Skin pathologies encompass a spectrum of conditions, with malignancies such as melanoma representing a critical diagnostic urgency. This investigation delineates the deployment of Convolutional Neural Networks (CNNs) for the classification of dermatological anomalies, benchmarking CNN diagnostic fidelity against dermatological expert evaluations. The study underscores the efficacy of CNN classifiers in expediting the diagnostic workflow for various cutaneous disorders. Advocating for an automated diagnostic framework, the research introduces a CNN-based system aimed at reducing human diagnostic load, accelerating diagnostic timelines, and enhancing survival outcomes. Utilizing advanced image processing algorithms and deep learning architectures, the research presents an automated classification system for skin pathologies, addressing both benign and malignant presentations. The classification matrix includes nine dermatological conditions: actinic keratosis, basal cell carcinoma, benign keratosis, dermatofibroma, melanoma, nevus, seborrheic keratosis, squamous cell carcinoma, and vascular lesions. The objective is to engineer a CNN model with robust diagnostic performance across a diverse lesion dataset, ensuring accurate identification and categorization of dermatological conditions.

关键词： Deep learning Pathology Accuracy Melanoma computer architecture Benchmark testing Skin Convolutional neural networks Lesions Medical diagnosis

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A Novel Hybrid AI Framework for Real-Time Prediction and Risk Assessment of Vector-Borne Epidemics using Spatiotemporal data

A Novel Hybrid AI Framework for Real-Time Prediction and Ris...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Rajesh Munirathnam Meenakshi Maindola Bhargav H K Prachi Ramesh Patil Pooja Bhatt Natrayan L Data & Analytics Division TechnoGen Inc. New Jersey USA Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering Shridevi Institute of Engineering and Technology Industry Institute Interaction Cell(IIIC) Tumakuru India Department of Electronics and Telecommunication Engineering Jagdambha College of Engineering and Technology Yavatmal India Department of Computer science and Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Vadodara India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Vector-borne epidemics constitute major threats to global health that call for improved modeling systems for managing diseases. In order to incorporate such factors into an actual model, a traditional forecasting process may fail to grasp the nature of disease transmission adequately. We present a hybrid AI system based on LSTM and GCN for forest bug outbreak detection in real time. The framework also integrated a spatial-temporal analysis of climate variability, vector population fluctuations, and human mobility with climate variables across 15 regions over a five-year period (2019–2023). The hybrid framework also produced superior results, achieving a 94.2% prediction rate for health risk disease outbreaks, compared to a 17.3% improvement in traditional machine learning. When used to distinguish between high-risk and low-risk zones, the model demonstrated the following metrics: sensitivity of 92.8% and specificity of 95.1%. Real-time risk assessment demonstrated 89.6% effectiveness in indicating early warning and the best prediction lead time average of 21 days. This framework significantly enhances the prediction of vector-borne diseases by offering a dependable, real-time platform for disease surveillance and risk assessment. Its high accuracy and early warning capabilities make the system a useful tool in epidemiological and public health campaigns and control measures.

关键词： Epidemics Accuracy Surveillance Real-time systems Vectors Spatiotemporal phenomena Risk management Public healthcare Long short term memory Diseases

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Study of deep learning techniques for medical image analysis: A review

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Materials Today: Proceedings 2022年 56卷 209-214页

作者： Ayush Singhal Manu Phogat Deepak Kumar Ajay Kumar Mamta Dahiya Virendra Kumar Shrivastava Department of Computer Science Engineering Faculty of Engineering and Technology Shree Guru Gobind Singh Tricentenary University Gurugram 122505 Haryana India Department of Mechanical Engineering Faculty of Engineering & Technology Shree Guru Gobind Singh Tricentenary University Gurugram 122505 Haryana India Department of Big Data Analytics Adani Institute of Digital Technology Management Gandhinagar Gujarat 382423 India

Now, there is a lot of research going on in the field of medical image analysis by using deep convolutional networks. Deep learning uses various models to extract the information from the images provided to deep learning model. The deep learning is now widely used in the field of medical to detect and diagnose the disease and after diagnosing classifying it into particular category of the disease. The most widely model used for medical image analysis is Convolutional neural network. So, this review paper focusses on how deep learning uses deep networks to detect the disease by retrieving or extracting the information from the images provided to the network and also give information about the clinical applications in the medical fields and the limitations of deep learning in image analysis process is also highlighted.

关键词： Deep learning Medical image analysis Convolutional neural network

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Classification and Contrast of Supervised Machine Learning Algorithms

Classification and Contrast of Supervised Machine Learning A...

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Artificial Intelligence and Smart Communication (AISC), International Conference on

作者： Ramakrishnan Raman Rejuwan Shamim Shaik Vaseem Akram Lalit Thakur Biju G. Pillai R. Ponnusamy Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science and Engineering with Data Science Maharishi University of Information Technology Noida Uttaranchal Institute of Technology Uttaranchal University Department of CIVIL Parul University Department of IT & Business Analytics BIIB & Dean - Faculty of Management Sri Balaji University Pune (SBUP) Department of Computer Science & Engineering Chennai Institute of Technology

Supervised Classification (SML) is the pursuit of systems that reasoning from externally given instances to generate broad hypotheses, which subsequently generate predictions for future instances. One of the jobs performed by intelligent systems most commonly is supervised classification. Based on the data set, number of occurrences and variables in this study, the most efficient classification algorithm is selected. It is comprehensively described and contrasted with various supervised learning methods. Seven distinct machine learning algorithms were taken into consideration utilizing the Waikato Environment for data Analysis (WEDA) machine learning tool. For the identification process, the data set was used with 780 instances, eight independent variables (quality), and one dependent variable (variable). According to the data, SVM was the technique with the highest degree of precision and accuracy. Accordingly, the next accurate classification algorithms after SVM were determined to be Naive Bayes and Random Forest. The study demonstrates that precision (accuracy) and model construction time are two factors, whereas kappa statistics and mean error percentage (MAE) are two other factors. Therefore, controlled predictive machine learning requires precision, accuracy, and minimal error in ML algorithms.

关键词： Support vector machines Machine learning algorithms Supervised learning Training data Prediction algorithms Time measurement Classification algorithms

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Hybrid genetic-discretized algorithm to handle data uncertainty in diagnosing stenosis of coronary arteries

Hybrid genetic-discretized algorithm to handle data uncertai...

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作者： Alizadehsani, Roohallah Roshanzamir, Mohamad Abdar, Moloud Beykikhoshk, Adham Khosravi, Abbas Nahavandi, Saeid Plawiak, Pawel Tan, Ru San Acharya, U Rajendra Deakin University GeelongVIC Australia Department of Engineering Fasa Branch Islamic Azad University Fars Fasa Iran Centre for Pattern Recognition and Data Analytics Deakin University GeelongVIC Australia Department of Information and Communications Technology Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 st. F-3 31-155 Krakow Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland National Heart Centre Singapore Singapore Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

Coronary artery disease (CAD) is the leading cause of morbidity and death worldwide. Invasive coronary angiography is the most accurate technique for diagnosing CAD, but is invasive and costly. Hence, analytical methods such as machine learning and data mining techniques are becoming increasingly more popular. Although physicians need to know which arteries are stenotic, most of the researchers focus only on CAD detection and few studies have investigated stenosis of the right coronary artery (RCA), left circumflex (LCX) artery and left anterior descending (LAD) artery separately. Meanwhile, most of the datasets in this field are noisy (data uncertainty). However, to the best of our knowledge, there is no study conducted to address this important problem. This study uses the extension of the Z-Alizadeh Sani dataset, containing 303 records with 54 features. A new feature selection algorithm is proposed in this work. Meanwhile, by discretization of data, we also handle the uncertainty in CAD prediction. To the best of our knowledge, this is the first study attempted to handle uncertainty in CAD prediction. Finally, the genetic algorithm (GA) is used to determine the hyper-parameters of the support vector machine (SVM) kernels. We have achieved high accuracy for the stenosis diagnosis of each main coronary artery. The results of this study can aid the clinicians to validate their manual stenosis diagnosis of RCA, LCX and LAD coronary arteries. © 2020 John Wiley & Sons, Ltd.

关键词： Feature Selection

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Enhancing Global Maritime Traffic Network Forecasting with Gravity-Inspired Deep Learning Models

arXiv

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arXiv 2024年

作者： Song, Ruixin Spadon, Gabriel Pelot, Ronald Matwin, Stan Soares, Amilcar Department of Computer Science Memorial University of Newfoundland St. John’sNL Canada Institute for Big Data Analytics Dalhousie University HalifaxNS Canada Industrial Engineering Department Dalhousie University HalifaxNS Canada Institute of Computer Science Polish Academy of Sciences Warsaw Poland Department of Computer Science and Media Technology Linnaeus University Växjö Sweden

Aquatic non-indigenous species (NIS) pose significant threats to biodiversity, disrupting ecosystems and inflicting substantial economic damages across agriculture, forestry, and fisheries. Due to the fast growth of global trade and transportation networks, NIS has been introduced and spread unintentionally in new environments. This study develops a new physics-informed model to forecast maritime shipping traffic between port regions worldwide. The predicted information provided by these models, in turn, is used as input for risk assessment of NIS spread through transportation networks to evaluate the capability of our solution. Inspired by the gravity model for international trades, our model considers various factors that influence the likelihood and impact of vessel activities, such as shipping flux density, distance between ports, trade flow, and centrality measures of transportation hubs. Accordingly, this paper introduces transformers to gravity models to rebuild the short- and long-term dependencies that make the risk analysis feasible. Thus, we introduce a physics-inspired framework that achieves an 89% binary accuracy for existing and non-existing trajectories and an 84.8% accuracy for the number of vessels flowing between key port areas, representing more than 10% improvement over the traditional deep-gravity model. Along these lines, this research contributes to a better understanding of NIS risk assessment. It allows policymakers, conservationists, and stakeholders to prioritize management actions by identifying high-risk invasion pathways. Besides, our model is versatile and can include new data sources, making it suitable for assessing international vessel traffic flow in a changing global landscape. Copyright © 2024, The Authors. All rights reserved.

关键词： Risk assessment

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Comparative Analysis of Outlier Elimination Algorithms on Pulsar Stars dataset

Comparative Analysis of Outlier Elimination Algorithms on Pu...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Tejaswini Katale Waidehi Gautam Aakash Chotrani Aishwarya Katale Parikshit N. Mahalle Gitanjali R. Shinde Department of Computer Engineering Vishwakarma Institute of Technology Pune Maharashtra India Department of Information Technology University of Cumberlands Williamsburg KY USA Department of Business Analytics Mercer University Atlanta Georgia USA Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Pune Maharashtra India Computer Science and Engineering Vishwakarma Institute of Information Technology Pune Maharashtra India

Outliers are uncommon observations that are dispersed from most of the other data Points. These points, which can be abnormally high or low, can significantly affect the entire analysis and forecasting of the data. The purpose of this Research Paper is to examine the implementation of Outlier Elimination Algorithms on Pulsar Stars dataset. It is attributed as an HTRU2 (High Time Resolution Universe Survey) project, which focuses on identifying new pulsar candidates based on the data from Parkes radio telescope in Australia. On this dataset, a total of 6 Outlier Elimination Algorithms are implemented; Z-Score, DBSCAN(Density-based spatial clustering of applications with noise), Mahalanobis Distance, Median Absolute Deviation, Multivariate Normal Distribution for N Dimension, Angle-based Outlier Detection (ABOD). After removal of the identified Outliers, to determine which Outlier Technique was more effective, 4 Machine Learning Techniques are deployed - Random Forest, Decision Tree, Naive Bayes and XGBoost. Furthermore, Accuracies, Recall, Precision and F1 Score values were compared. Every deployed Outlier algorithm must have a consistent quantity of Outliers in order to be evaluated for efficacy. For the same around 2600–2800 number of Outliers are eliminated for each Technique. Multivariate Normal Distribution for N Dimension Outlier Algorithm with Random Forest yields Highest Accuracy of 98.2%. ABOD with Naive Bayes gives Highest Recall Value of 0.93, Z-Score with Random Forest results in Highest Precision value of 0.96 and ABOD with Random Forest yields Highest F1-Score value of 0.92. From the information provided above, it is also clear that Random Forest produces the greatest Accuracy, Precision, and F1-Score values among all the Machine Learning algorithms that have been deployed. Therefore, for improved outcomes, the Random Forest technique should be utilized after the outliers are removed.

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A study of complex deep learning networks on high-performance, neuromorphic, and quantum computers

A study of complex deep learning networks on high-performanc...

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作者： Potok, Thomas E. Schuman, Catherine Young, Steven Patton, Robert Spedalieri, Federico Liu, Jeremy Yao, Ke-Thia Rose, Garrett Chakma, Gangotree Computational Data Analytics Group Oak Ridge National Laboratory P.O. Box 2008 Oak RidgeTN37831 United States University of Southern California Information Sciences Institute 4676 Admiralty Way Marina del ReyCA90292 United States Department of Electrical Engineering and Computer Science University of Tennessee 1520 Middle Dr KnoxvilleTN37996 United States

Current deep learning approaches have been very successful using convolutional neural networks trained on large graphical-processing-unit-based computers. Three limitations of this approach are that (1) they are based on a simple layered network topology, i.e., highly connected layers, without intra-layer connections;(2) the networks are manually configured to achieve optimal results, and (3) the implementation of the network model is expensive in both cost and power. In this article, we evaluate deep learning models using three different computing architectures to address these problems: quantum computing to train complex topologies, high performance computing to automatically determine network topology, and neuromorphic computing for a low-power hardware implementation. We use the MNIST dataset for our experiment, due to input size limitations of current quantum computers. Our results show the feasibility of using the three architectures in tandem to address the above deep learning *** show that a quantum computer can find high quality values of intra-layer connection weights in a tractable time as the complexity of the network increases, a high performance computer can find optimal layer-based topologies, and a neuromorphic computer can represent the complex topology and weights derived from the other architectures in low power memristive hardware. © 2018 ACM.

关键词： Deep learning

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