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arXiv 2022年

作者： Masoumi, Nima Rivaz, Hassan Omair Ahmad, M. Xiao, Yiming Department of Electrical and Computer Engineering Concordia University Montreal Canada Department of Computer Science and Software Engineering Concordia University MontrealQC Canada

Purpose: Diffeomorphic image registration is essential in many medical imaging applications. Several registration algorithms of such type have been proposed, but primarily for intra-contrast alignment. Currently, efficient inter-modal/contrast diffeomorphic registration, which is vital in numerous applications, remains a challenging task. Methods: We proposed a novel inter-modal/contrast registration algorithm that leverages Robust PaTch-based cOrrelation Ratio (RaPTOR) metric to allow inter-modal/contrast image alignment and bandlimited geodesic shooting demonstrated in Fourier-Approximated Lie Algebras (FLASH) algorithm for fast diffeomorphic registration. Results: The proposed algorithm, named DiffeoRaptor, was validated with three public databases for the tasks of brain and abdominal image registration while comparing the results against three state-of-the-art techniques, including FLASH, NiftyReg, and Symmetric image normalization (SyN). Conclusions: Our results demonstrated that DiffeoRaptor offered comparable or better registration performance in terms of registration accuracy. Moreover, DiffeoRaptor produces smoother deformations than SyN in inter-modal and contrast registration. The code for DiffeoRaptor is publicly available at https://***/nimamasoumi/DiffeoRaptor. © 2022, CC BY-NC-SA.

关键词： Image registration

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Implementation of Convolutional Neural Networks deep learning approach to Classify Melanoma Skin Cancer

Implementation of Convolutional Neural Networks deep learnin...

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Wireless and Optical Technologies (GCWOT), 2020 Global Conference on

作者： Adnan Afroz Razia Zia Shaheena Noor Andres Ortiz Garcia Sarman Shams Shoaib Mughal Software Enginneering Department Sir Syed University Engineering and Technology Karachi Pakistan Electrical Engineering Department Sir Syed University Engineering and Technology Karachi Pakistan Computer Engineering Department Sir Syed University Engineering and Technology Karachi Pakistan E.T.S Ingenieria De Telecomunicacion Universidad De Malaga Malaga Spain Biomedical Engineering Department Sir Syed University Engineering and Technology Karachi Pakistan Computer Engineering Department Bahria University Karachi Campus

A kind of deadly skin tumor called melanoma is arguably very lethal since it accounts for the majority of skin cancer fatalities. Melanomas are often brown or black in color because melanocyte cells, which produce melanin, are the source of the disease. The DNA of skin cells is damaged by UV light, which is the main cause of melanomas. The analysis of the dermoscopy examination’s findings and comparison with medical sciences are frequently used in the manual diagnosis of melanoma cancer. Human subjectivity has a strong impact on manual detection, which renders it unreliable in some circumstances. In order to classify the outcomes of the dermoscopy test and to determine the results more precisely with a comparatively shorter amount of time, computer-assisted technology is required. Problem statement, planning, execution, and testing are the first steps in the creation of this application. To identify picture data, the research study combines deep machine learning approach using Convolutional Neural Network technique along with LeNet-5 architectural model. With a variable number of training and testing epochs and 44 photos from the training results, the experiment with the best success rate (93% in training and 100% in testing) required 176 images and 100 epochs of training data. programming language such as Python with utilization of Keras library, which serves as the Tensorflow back-end, were used to construct this application.

关键词： Training Wireless communication Training data Melanoma Manuals Skin Data models

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Explainable Artificial Intelligence Solution for Online Retail

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computers, Materials & Continua 2022年第6期71卷 4425-4442页

作者： Kumail Javaid Ayesha Siddiqa Syed Abbas Zilqurnain Naqvi Allah Ditta Muhammad Ahsan M.A.Khan Tariq Mahmood Muhammad Adnan Khan Department of Computer Science University of South AsiaLahore54000Pakistan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Division of Science and Technology University of EducationLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea

Artificial intelligence(AI)and machine learning(ML)help in making predictions and businesses to make key decisions that are beneficial for *** the case of the online shopping business,it’s very important to find trends in the data and get knowledge of features that helps drive the success of the *** this research,a dataset of 12,330 records of customers has been analyzedwho visited an online shoppingwebsite over a period of one *** main objective of this research is to find features that are relevant in terms of correctly predicting the purchasing decisions made by visiting customers and build ML models which could make correct predictions on unseen data in the *** permutation feature importance approach has been used to get the importance of features according to the output variable(Revenue).Five ML models i.e.,decision tree(DT),random forest(RF),extra tree(ET)classifier,Neural networks(NN),and Logistic regression(LR)have been used to make predictions on the unseen data in the *** performance of each model has been discussed in detail using performance measurement techniques such as accuracy score,precision,recall,F1 score,and ROC-AUC *** model is the bestmodel among all five chosen based on accuracy score of 90%and F1 score of 79%followed by extra tree ***,our study indicates that RF model can be used by online retailing businesses for predicting consumer buying *** research also reveals the importance of page value as a key feature for capturing online purchasing *** may give a clue to future businesses who can focus on this specific feature and can find key factors behind page value success which in turn will help the online shopping business.

关键词： Explainable artificial intelligence online retail neural network random forest regression

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Examining the Potential of Machine Learning in Reducing Prescription Drug Costs

Examining the Potential of Machine Learning in Reducing Pres...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Sachin C. Patil Sairam Madasu Krishna J Rolla Ketan Gupta N. Yuvaraj Senior Software Engineer USA Senior Cloud Software Engineer Microsoft Charlotte North Carolina USA Department of Information Technology University of The Cumberlands Williamsburg KY USA Computer Science and Engineering Sri Shakthi Institute of Engineering and Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The ability of the system to gain knowledge for lowering prescription drug prices has been increasingly studied in recent years. Gadget-gaining knowledge is a branch of synthetic intelligence that enables machines and PC systems to enhance their overall performance on specific obligations by means of mastering beyond facts. Within the context of prescription drug costs, gadget learning may be used to find patterns and developments in drug pricing and usage, decide disparities in getting admission to and usage, and have a look at pricing strategies hired by pharmaceutical businesses. The use of gadgets to gain knowledge can help to discover elements using prescription drug costs, inclusive of the pricing and utilization of individual capsules or the relationships among drug costs and effects. Moreover, AI-primarily based algorithms may be used to use massive datasets in an effort to apprehend the role that healthcare vendors, insurers, and pharmaceutical organizations have on prescription drug ***, AI-based total fashions can also be used to become aware of and degree cost elements associated with specific drug combinations. Sooner or later, machine studying may be leveraged to examine the effects of policy changes and healthcare reforms on prescription drug costs and to become aware of goal populations with high prescription drug prices. By combining gadget studying with conventional healthcare price analysis gear, researchers can gain insights into the drivers of prescription drug charges and make public coverage decisions. In sum, a device gaining knowledge can be a powerful tool.

关键词： Drugs Costs Accuracy Machine learning algorithms software algorithms Pricing Medical services Organizations Machine learning software

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Mitigating Electrical Losses Through a programmable Smart Energy Advanced Metering Infrastructure System

Mitigating Electrical Losses Through a Programmable Smart En...

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International Conference on Prognostics and Health Management, PHM

作者： Daniel O. Williams Zhaojun Steven Li Afsaneh Ghanavati Industrial Engineering Department Western New England University Springfield MA USA Electrical and Computer Engineering Program School of Engineering Wentworth Institute of Technology Boston MA USA

With the current alarming exponential increase in global energy demand chiefly due to population growth, electrification, and the issues associated with fossil generation, utilities are reinvesting their returns in alternative ways of clean power generation. Although, finding alternative ways to provide clean energy and to advance the power grid are of the main interest globally, many countries face power theft as a frequent problem. in Ghana, power losses in the distribution system cost the nation over a billion Ghana Cedis in the country's total annual revenue, of which power theft plays a predominant role. This paper presents an electricity theft mitigation technique through a programmable smart energy meter. The proposed method is such that interruptions are added to the smart energy meters in order to detect input signals from an added current sensor placed at the terminal point of the service line, from where in-between the sensor and the meter, illegal connections are made. The proposed Advanced Metering Infrastructure (AMI) system will provide smart services, including calculating consumed energy in kWh and generating a bill sent to the utility station. After which, the AMI system will disconnect the power supply from the meter.

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A Hybrid Transformer Topology for Distribution Network Voltage Regulation

A Hybrid Transformer Topology for Distribution Network Volta...

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2021 IEEE Southern Power Electronics Conference, SPEC 2021

作者： Power, Rupert Mithani, Alykhan Madawala, Udaya Baguley, Craig The University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand Auckland University of Technology Department of Engineering Computing and Mathematical Sciences Auckland New Zealand

ISBN: (纸本)9781665436236

Worldwide, distributed generation sources are being installed in increasing numbers within distribution networks. This poses new challenges for distribution network operators, including voltage fluctuations beyond permissible limits that can differ in different parts of the same distribution network. The Hybrid Transformer (HT) is an effective solution that can mitigate voltage fluctuations continuously, dynamically and in a local manner, through combining a standard distribution transformer with a power electronic converter. In this paper a retrofittable HT topology is proposed. It utilises a second, fractionally-rated line frequency transformer (LFT) to enable the use of low-voltage power electronics with low power ratings for reduced costs and complexity. The proposed topology is modelled and a scaled hardware prototype is used to verify theory and simulation results. The simulation and experimental results show the efficacy of the proposed topology, its applicability to mitigate voltage fluctuations, and its benefits over alternative approaches. © 2021 IEEE.

关键词： Distributed power generation

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Generative Adversarial Networks for Robust Anomaly Detection in Noisy IoT Environments

Generative Adversarial Networks for Robust Anomaly Detection...

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IEEE International Conference on Communications (ICC)

作者： Adel Abusitta Talal Halabi Ahmed Saleh Bataineh Mohammad Zulkernine Department of Computer and Software Engineering Poly technique Montreal Montreal QC Canada Department of Computer Science Université Laval Québec QC Canada School of Computing Queen's University Kingston ON Canada

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The Internet of Things (IoT) enables us to collect and process vast amounts of data in real time. However, the security of IoT devices and networks is highly susceptible to cyber attacks that threaten data integrity and service availability. Furthermore, due to the diverse nature of data collected from numerous nodes in IoT systems and the disturbances occurring within them, detecting anomalous activities and compromised nodes is considerably more challenging than in conventional computer systems. Therefore, it is crucial to develop robust and dependable anomaly detection methods to identify and remove malicious and/or unwanted data, which ensures their exclusion from IoT-powered applications and data analytics. To achieve this, this paper proposes a Generative Adverserial Networks (GAN)-based anomaly detection for IoT systems. The proposed model enables the autoencoder - using the adversarial training of GAN - to learn a better representation of IoT data, making it robust against noisy and changing environments. Based on experiments with real-world IoT datasets, the proposed framework has shown to improve the accuracy of detecting malicious traffic in IoT and surpass state-of-the-art anomaly detection models.

关键词： Training Data analysis Data integrity Generative adversarial networks Real-time systems Data models Internet of Things

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MediNET: A Deep Learning Approach to Recognize Bangladeshi Ordinary Medicinal Plants Using CNN 3rd

MediNET: A Deep Learning Approach to Recognize Bangladeshi ...

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3rd International Conference on Computing and Communication, IC3 2020

作者： Bhuiyan, Md. Rafiuzzaman Abdullahil-Oaphy, Md. Khanam, Rifa Shanzida Islam, Md. Sanzidul Department of Computer Science and Engineering Daffodil International University Dhanmondi Dhaka1207 Bangladesh Department of Software Engineering Daffodil International University Dhanmondi Dhaka1207 Bangladesh

ISBN: (纸本)9789811573934

Medicine is the only thing by which we use where we feel bad condition of our body’s physical and mental illness. Most of medicines are made of specific plants from our nature. These plants are also known as a medicinal plant. All the traditional Bangladeshi medical systems, namely Ayurveda, Unani, Homeopathy, prominently use medicinal plants. So, it is important to classify the right plant for medical preparation. The ability to identify these plants automatically is needed in recent days. For this, we proposed a renowned algorithm called convolutional neural network for recognizing the plants from leaf image. Our algorithm got 84.58% accuracy. We developed this. We believe that in the future the individuals who do not distinguish medicinal plants will recognize using this methodology. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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A Mathematical Model for the Selection of software Requirements Elicitation Techniques

A Mathematical Model for the Selection of Software Requireme...

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Virat Raj Saxena Rajeev Yadav Azra Parveen Mohd. Sadiq Department of Computer Science and Engineering Mangalayatan University Aligarh Uttar Pradesh India Indraprastha Research Laboratory Indraprastha Institute of Information Sciences Private Limited New Delhi India Software Engineering Laboratory Computer Engineering Section UPFET Jamia Millia Islamia A Central University New Delhi India

Various techniques have been developed for the identification of different types of requirements like interview, questionnaire, group elicitation techniques, attributed goal-oriented requirements analysis, fuzzy based goal-oriented requirements analysis method, non-functional requirements framework, etc. Based on the critical analysis of requirements elicitation methods, we found that single elicitation technique is not suitable to understand the need of the stakeholders. In real life applications, multiple elicitation techniques are required to elicit and select the requirements of an information system because each technique has its strength and limitations. Therefore, to address this issue, this paper presents a mathematical model for the selection of requirements elicitation techniques so that complete set of requirements along with its priority can be identified before the development of an information system. The institute examination system is considered to show the applicability of the proposed mathematical model.

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Toward Adaptive Tracking and Communication via an Airborne Maneuverable Bi-Static ISAC System

arXiv

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arXiv 2024年

作者： Wei, Mingliang Li, Ruoguang Wang, Li Xu, Lianming Han, Zhu The School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing100876 China The College of Information Science and Engineering Hohai University Changzhou213200 China School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

In this letter, we propose an airborne maneuverable bi-static integrated sensing and communication (ISAC) system where both the transmitter and receiver are unmanned aerial vehicles (UAVs). By timely forming a dynamic bi-static range based on the motion information of the target, such a system can provide an adaptive two-dimensional (2D) tracking and communication services. Towards this end, a trajectory optimization problem for both transmit and receive UAV is formulated to achieve high-accurate motion state estimation by minimizing the time-variant Cramér–Rao bound (CRB), subject to the sufficient communication signal-to-noise ratio (SNR) to maintain communication channel prediction error. Then we develop an efficient approach based on the successive convex approximation (SCA) technique and the S-procedure to address the problem. Numerical results demonstrate that our proposed airborne maneuverable bi-static ISAC system is able to obtain higher tracking accuracy compared with the static or semi-dynamic ISAC system. Copyright © 2024, The Authors. All rights reserved.

关键词： Extended Kalman filters

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