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COVID-19 Patient Care: A Content-Based Collaborative Filtering Using Intelligent Recommendation System 1

6th EAI International Conference on Science and Technologies for Smart Cities, SmartCity 2020

作者： Deebak, B.D. Al-Turjman, Fadi Schoool of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India Department of Artificial Intelligence Engineering Research Center for AI and IoT Near East University Nicosia Mersin 10 Turkey

ISBN: (数字)9783030760632

ISBN: (纸本)9783030760625

COVID-19 is a more transferable illness caused by a new novel coronavirus. It is highly emerging with efficient biosensors such as sensitive and selective that afford the diagnostic tools to infer the disease early. It can maintain a personalized healthcare system to evaluate the growth of disease under proper patient care. To discover as a personalized technology, the healthcare system prefers collaborative filtering. It can effectively deal with cold-start and sparse-data to conduct useful extensions. Due to the continuous expansion of scaling data in a medical scenario, content-based, collaborative filtering, and similarity metrics are preferred. It relies on the most similar social users or threats when the information is large. Many neighbors gain importance to obtain a set of users with whom a target user is likely to match. Forming communities reveal vulnerable users and also reduce the challenges of collaborative filtering like data-sparsity and cold-start problems. Thus, this framework proposes content-based collaborative filtering using intelligent recommendation systems (CCF-IRS) based on high correlation and shortest neighbor in the social community. The result is shown that the proposed CCF-IRS achieves better accuracy than the existing algorithms. © 2021, ICST institute for computer Sciences, Social informatics and Telecommunications engineering.

关键词： COVID-19

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PND-Net: plant nutrition deficiency and disease classification using graph convolutional network (vol 14, 15537, 2024)

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SCIENTIFIC REPORTS 2024年第1期14卷 1-17页

作者： Bera, Asish Bhattacharjee, Debotosh Krejcar, Ondrej Department of Computer Science and Information Systems BITS Pilani Pilani Campus Pilani Rajasthan 333031 India Department of Computer Science and Engineering Jadavpur University Kolkata West Bengal 700032 India Faculty of Informatics and Management University of Hradec Kralove Hradec Kralove Czech Republic Skoda Auto University Na Karmeli 1457 293 01 Mlada Boleslav Czech Republic Malaysia Japan International Institute of Technology (MJIIT) Universiti Teknologi Malaysia Kuala Lumpur Malaysia

Crop yield production could be enhanced for agricultural growth if various plant nutrition deficiencies, and diseases are identified and detected at early stages. Hence, continuous health monitoring of plant is very crucial for handling plant stress. The deep learning methods have proven its superior performances in the automated detection of plant diseases and nutrition deficiencies from visual symptoms in leaves. This article proposes a new deep learning method for plant nutrition deficiencies and disease classification using a graph convolutional network (GNN), added upon a base convolutional neural network (CNN). Sometimes, a global feature descriptor might fail to capture the vital region of a diseased leaf, which causes inaccurate classification of disease. To address this issue, regional feature learning is crucial for a holistic feature aggregation. In this work, region-based feature summarization at multi-scales is explored using spatial pyramidal pooling for discriminative feature representation. Furthermore, a GCN is developed to capacitate learning of finer details for classifying plant diseases and insufficiency of nutrients. The proposed method, calledPlantNutrition Deficiency andDiseaseNetwork (PND-Net), has been evaluated on two public datasets for nutrition deficiency, and two for disease classification using four backbone CNNs. The best classification performances of the proposed PND-Net are as follows: (a) 90.00% Banana and 90.54% Coffee nutrition deficiency; and (b) 96.18% Potato diseases and 84.30% on PlantDoc datasets using Xception backbone. Furthermore, additional experiments have been carried out for generalization, and the proposed method has achieved state-of-the-art performances on two public datasets, namely the Breast Cancer Histopathology Image Classification (BreakHis 40\(\times \): 95.50%, and BreakHis 100\(\times \): 96.79% accuracy) and Single cells in Pap smear images for cervical cancer classification (SIPaKMeD: 99.18% accuracy).

关键词： Agriculture Cancer classification Convolutional neural network Graph convolutional network Nutrition deficiency Plant disease Spatial pyramid pooling

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EDOLAB: An Open-Source Platform for Education and Experimentation with Evolutionary Dynamic Optimization Algorithms

arXiv

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arXiv 2023年

作者： Peng, Mai Yazdani, Delaram She, Zeneng Yazdani, Danial Luo, Wenjian Li, Changhe Branke, Juergen Nguyen, Trung Thanh Gandomi, Amir H. Yang, Shengxiang Jin, Yaochu Yao, Xin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Research Institute Faculty of Engineering and Technology Liverpool John Moores University United Kingdom School of Computer Science and Technology Harbin Institute of Technology China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Peng Cheng Laboratory China School of Artificial Intelligence Anhui University of Sciences & Technology China Information Systems Management and Analytics in Warwick Business School University of Warwick United Kingdom Faculty of Engineering & Information Technology University of Technology Sydney Australia Obuda University Hungary School of Computer Science and Informatics De Montfort University United Kingdom Department of Artificial Intelligence School of Engineering Westlake University China School of Computer Science University of Birmingham United Kingdom

Many real-world optimization problems exhibit dynamic characteristics, posing significant challenges for traditional optimization techniques. Evolutionary Dynamic Optimization Algorithms (EDOAs) are designed to address these challenges effectively. However, in existing literature, the reported results for a given EDOA can vary significantly. This inconsistency often arises because the source codes for many EDOAs, which are typically complex, have not been made publicly available, leading to error-prone re-implementations. To support researchers in conducting experiments and comparing their algorithms with various EDOAs, we have developed an open-source MATLAB platform called the Evolutionary Dynamic Optimization LABoratory (EDOLAB). This platform not only facilitates research but also includes an educational module designed for instructional purposes. The education module allows users to observe: a) a 2-dimensional problem space and its morphological changes following each environmental change, b) the behaviors of individuals over time, and c) how the EDOA responds to environmental changes and tracks the moving optimum. The current version of EDOLAB features 25 EDOAs and four fully parametric benchmark generators. © 2023, CC BY-NC-SA.

关键词： Optimization algorithms

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Adaptive latent fingerprint image segmentation and matching using Chan-vese technique based on EDTV model 2

Adaptive latent fingerprint image segmentation and matching ...

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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Hilles, Shadi M S Liban, Abdilahi Altrad, Abdullah Mahmoud Miaikil, Othman A.M. El-Ebiary, Yousef A. Baker Contreras, Jennifer Hilles, Mohanad M Istanbul OKAN University Software Engineering Department Istanbul Turkey Hargeisa University Computer Science Department Hargeisa Somalia Al-Madinah International University Computer Science Department Kuala Lumpur Malaysia Universiti Sultan Zainal Abidin Faculty of Informatics and Computing Trengganu Malaysia FEU Institute of Technology Department of Information Technology Manila Philippines University College of Applied Science Information Technology Department Gaza Palestine

ISBN: (纸本)9781665432221

Biometrics such as face, fingerprint, iris, voice and palm prints are the most widely used, and as well the fingerprints are one of the most frequently used biometrics to identify individuals and authenticate their identity. commonly categorized into three different categories which are rolled, plain and latent fingerprints. The reliability of image segmentation for latent fingerprint which is used in criminal issues still challenges, The difficulty of latent fingerprint image segmentation mainly lies in the poor quality of fingerprint patterns and the presence of the noise in the background, This research has investigated the fingerprint segmentation and matching based on EDTV and presented Chan-vese active contour segmentation technique, in addition, presented NIST SD27 for grayscale dataset of latent fingerprint which is standard by National institute of Standard and technology, where is dataset have varieties of fingerprint image samples, a total about 258 of latent fingerprint, those samples collected from crime scenes and matching fingerprint and shown the performance of matching accuracy ROC and CMC curves, To evaluate the performance of the matching ROC and CMC curves has been deployed, The area under curve (AUC) of the ROC of the good images performance is 72% with CMC rank1-idnetification of 42% and rank-20 identification of 79%. the result shows that the latent fingerprint method performance is better for good latent fingerprint images compare to bad and ugly images, while there is no much difference for bad and ugly image. © 2021 IEEE.

关键词： Biometrics

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Latent fingerprint enhancement and segmentation technique based on hybrid edge adaptive DTV model 2

Latent fingerprint enhancement and segmentation technique ba...

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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Hilles, Shadi M S Liban, Abdilahi Miaikil, Othman A.M. Mahmoud Altrad, Abdullah Baker El-Ebiary, Yousef A. Hilles, Mohanad M Contreras, Jennifer Istanbul OKAN University Software Engineering Department Istanbul Turkey Hargeisa University Computer Science Department Hargeisa Somalia Al-Madinah International University Computer Science Department Kuala Lumpur Malaysia Universiti Sultan Zainal Abidin Faculty of Informatics and Computing Trengganu Malaysia University College of Applied Science Information Technology Department Gaza Palestine FEU Institute of Technology Department of Information Technology Manila Philippines

ISBN: (纸本)9781665432221

Image enhancement and segmentation is widely used for fingerprint identification and authorization in biometrics devices, criminal scene is most challenges due to low quality of fingerprint, the most significant efforts is to develop algorithm for latent fingerprint enhancement which become challenging problem due to the complex and existing problem for instance, developing algorithms of latent fingerprint is able to extract features of image blocks and removing overlapping and isolate the poor and noisy background. however, it's still challenging and interested problem specifically latent fingerprint enhancement and segmentation. The aim study of this paper is to propose latent fingerprint enhancement and segmentation based on hybrid model and Chan-Vese method for segmentation, in order to reduce low image quality and increase the accuracy of fingerprint. The desired characteristics of intended technique are adaptive, effective and accurate, hybrid model of edge adaptive direction achieves accurate latent fingerprint enhancement and segmentation, the target needs to improve feature detection and performance, this research has proposed system architecture of research method in fingerprint enhancement and segmentation where is the method content two stages, the first is normalization and second is reconstruction, using EDTV model is required for adaptive noise, in addition Chan-vase technique contributed for identification of fingerprint image features, the result and testing using RMSE with three categories of fingerprint images good, bad and ugly show better performance for all three categories, as well RMSE shows the average of good latent fingerprint before and after enhancement. Latent Fingerprint Enhancement and Segmentation Technique Based on Hybrid Model Edge Adaptive Directional Total Variation © 2021 IEEE.

关键词： Image enhancement

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Examine the Impact of Visual Content when Used in Mixed Media Applications

Examine the Impact of Visual Content when Used in Mixed Medi...

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International Conference on Computing and Networking technology (ICCNT)

作者： N Rajiv Gandhi Shitij Goyal Ashutosh Pattanaik Jayashree Tamkhade T. Kuppuraj Noopor Pandey Department of Civil Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Quantum University Research Center Quantum University Mechaical Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka E&TC Vishwakarma Institute of Information Technology Pune Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Visible content in mixed media programs may have a large effect on its customers. By combining both visible and auditory elements, it permits users to have an extra immersive revel in terms of eating the content. Visible content material consisting of pics, films, diagrams, and so forth. It can all be used to in addition enhance enjoyment while used as a complement or number one source of records. Visuals can simplify complicated ideas and provide a less complicated way to navigate through products or services. It may be specifically useful in academic contexts. Moreover, visible content material can be used to increase Engagement via telling a story or thrillingly providing information. It helps create a connection between the consumer and the content material and makes the revel extra memorable. In the end, visuals can encourage user interplay with interactive elements along with buttons, menus, and bureaucracy, all of which can decorate the user experience. The use of visual content in combined media applications may have a full-size effect on the person’s enjoyment. Through the incorporation of visuals, customers can benefit from a higher knowledge of the context and cause of the utility, in addition to an immersive experience that similarly complements the content material offered.

关键词： Visualization Navigation Films Immersive experience Media User experience

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Cloud-Powered Smart Rehabilitation and Assistive Robotics for Stroke Recovery with Recurrent Neural Networks

Cloud-Powered Smart Rehabilitation and Assistive Robotics fo...

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Data Intelligence and Cognitive informatics (ICDICI), International Conference on

作者： M Maria Sampoornam Mothiram Rajasekaran Visumathi J Balachandra Pattanaik S. Kalaichelvi S. Sujatha Department of Information Technology J.J College of Engineering and Technology Tiruchirappalli Tamil Nadu India Pine Candle Way Saint Augustine Florida USA Department of Information Technology Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electrical and Computer Engineering Wollega University Nekemte Ethiopia PG and Research Department of Computer Science and Applications Vivekanandha College of Arts and Sciences for Women (Autonomous) Elayampalayam Tiruchengode Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Innovative rehabilitation treatments are needed since stroke is a major cause of long-term impairment globally. In this paper, it presents a smart robotics-assisted rehabilitation system for stroke patients that uses cloud computing and is enhanced using Recurrent Neural Networks (RNNs). The system utilizes a combination of wearable sensors, robotic equipment, and cloud computing to create individualized rehabilitation plans for each patient. To quickly evaluate patients' progress and adjust rehabilitation programs, RNNs are used for real-time processing of continuous sensor data streams. Cloud computing allows for easy scaling, universal accessibility, and compatibility with preexisting healthcare systems. Initial results show promise in enhancing stroke recovery outcomes, highlighting the potential of the proposed method to transform rehabilitation techniques in the smart healthcare realm. The urgent need for effective and tailored stroke rehabilitation programs is something this framework intends to solve, which should lead to better results and a higher quality of life for patients.

关键词： Cloud computing Technological innovation Recurrent neural networks Transforms Stroke (medical condition) Robot sensing systems Real-time systems Wearable sensors Streams research and development

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research on Self-balancing Two Wheels Mobile Robot Control System Analysis

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Electrical Science & engineering 2022年第1期4卷 7-20页

作者： Hla Myo Tun Myat Su Nwe Zaw Min Naing Maung Maung Latt Devasis Pradhan Prasanna Kumar Sahu Faculty of Electrical and Computer Engineering Yangon Technological UniversityYangonMyanmar Department of Mechanical Engineering King Lauk Phya Institute of Technology MyaungmyaMyaungmyaMyanmar Department of Research and Innovation Ministry of Science and TechnologyYangonMyanmar Department of Electronic Engineering Yangon Technological UniversityYangonMyanmar Department of Electronics&Communication Engineering Acharya Institute of TechnologyBengaluruIndia Department of Electrical Engineering National Institute of TechnologyRourkelaOdishaIndia

The paper presents the research on self-balancing two-wheels mobile robot control system analysis with experimental *** research problem in this work is to stabilize the mobile robot with self-control and to carry the sensitive things without failing in a long span *** main objective of this study is to focus on the mathematical modelling of mobile robot from laboratory scale to real world *** numerical expression with mathematical modelling is very important to control the mobile robot system with *** fundamental concepts of dynamic system stability were utilized for maintaining the stability of the constructed mobile robot *** controller design is also important for checking the stability and the appropriate controller design is proportional,integral,and derivative-PID controller and Linear Quadratic Regulator(LQR).The steady state error could be reduced by using such kind of PID *** simulation of numerical expression on mathematical modeling was conducted in MATLAB *** confirmation results from the simulation techniques were applied to construct the hardware design of mobile robot system for practical *** results from simulation approaches and experimental approaches are matched in various kinds of *** constructed mobile robot system was designed and analyzed in the control system design laboratory of Yangon Technological University(YTU).

关键词： Mobile robot Self-balancing robot Control system design PID controller Dynamic control system analysis

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Detecting Anomalous Insiders Using Network Analysis 1

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9th International Conference on Communications, Signal Processing, and Systems, CSPS 2020

作者： Dai, Lei Zhang, Liwei Li, Limin Chen, You Beijing Institute of Technology Beijing China China Academy of Electronics and Information Technology Beijing China Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Electrical Engineering & Computer Science School of Engineering Vanderbilt University NashvilleTN United States

ISBN: (数字)9789811584114

ISBN: (纸本)9789811584107

Collaborative information systems (CISs) can improve efficiency and quality of services, because they enable users to coordinate and collaborate on common works with a large scale. In recent decades, increasing number of hospitals start to adopt CIS to improve efficiency and quality of health care treatment, and CIS is becoming an important platform for hospital employees to treat patients. CIS is rich in resources of relations among users, however, people seldom study networks of users existing in the CIS, especially in health information systems. In this paper, we construct collaborative weighted networks of users from two typical CIS: one from health care respect, Vanderbilt University STARPANEL system;and the other from a more general respect, Wikipedia talk system. We learn characteristics of those two networks, and find both networks follow degree-strength relations, which could be used for detecting anomalous behaviors of users in the CIS, for instance, anomalous behaviors of hospital employees and wiki editors. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Hospitals

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Increasing Fairness via Combination with Learning Guarantees

arXiv

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arXiv 2023年

作者： Bian, Yijun Zhang, Kun Qiu, Anqi Chen, Nanguang The Department of Biomedical Engineering College of Design and Engineering National University of Singapore Singapore119276 Singapore School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China The Department of Biomedical Engineering National University of Singapore Singapore Research Institute National University of Singapore Suzhou China The Department of Biomedical Engineering The Johns Hopkins University BaltimoreMD United States The Department of Health Technology and Informatics The Hong Kong Polytechnic University Hung Hom Hong Kong

The concern about underlying discrimination hidden in machine learning (ML) models is increasing, as ML systems have been widely applied in more and more real-world scenarios and any discrimination hidden in them will directly affect human life. Many techniques have been developed to enhance fairness including commonly-used group fairness measures and several fairness-aware methods combining ensemble learning. However, existing fairness measures can only focus on one aspect-either group or individual fairness, and the hard compatibility among them indicates a possibility of remaining biases even if one of them is satisfied. Moreover, existing mechanisms to boost fairness usually present empirical results to show validity, yet few of them discuss whether fairness can be boosted with certain theoretical guarantees. To address these issues, we propose a fairness quality measure named discriminative risk to reflect both individual and group fairness aspects. Furthermore, we investigate the properties of the proposed measure and propose first- and second-order oracle bounds to show that fairness can be boosted via ensemble combination with theoretical learning guarantees. The analysis is suitable for both binary and multi-class classification. A pruning method is also proposed to utilise our proposed measure and comprehensive experiments are conducted to evaluate the effectiveness of the proposed methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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