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Towards Early Diagnosis and Intervention: An Ensemble Voting Model for Precise Vital Sign Prediction in Respiratory Disease

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IEEE Journal of Biomedical and Health Informatics 2023年 PP卷 1-13页

作者： Ahmed, Usman Lin, Jerry Chun-Wei Srivastava, Gautam Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Dept. of Math and Computer Science Brandon University Canada

Worldwide, cardiovascular and chronic respiratory diseases account for approximately 19 million deaths annually. Evidence indicates that the ongoing COVID-19 pandemic directly contributes to increased blood pressure, cholesterol, as well as blood glucose levels. Timely screening of critical physiological vital signs benefits both healthcare providers and individuals by detecting potential health issues. This study aims to implement a machine learning-based prediction and classification system to forecast vital signs associated with cardiovascular and chronic respiratory diseases. The system predicts patients' health status and notifies caregivers and medical professionals when necessary. Utilizing real-world data, a linear regression model inspired by the Facebook Prophet model was developed to predict vital signs for the upcoming 180 seconds. With 180 seconds of lead time, caregivers can potentially save patients' lives through early diagnosis of their health conditions. For this purpose, a Naïve Bayes classification model, a Support Vector Machine model, a Random Forest model, and genetic programming-based hyper tunning were employed. The proposed model outdoes previous attempts at vital sign prediction. Compared with alternative methods, the Facebook Prophet model has the best mean square in predicting vital signs. A hyperparameter-tuning is utilized to refine the model, yielding improved short- and long-term outcomes for each and every vital sign. Furthermore, the F-measure for the proposed classification model is 0.98 with an increase of 0.21. The incorporation of additional elements, such as momentum indicators, could increase the model's flexibility with calibration. The findings of this study demonstrate that the proposed model is more accurate in predicting vital signs and trends. IEEE

关键词： Decision trees

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Centralized and Decentralized Federated Learning in Autonomous Swarm Robots: Approaches, Algorithms, Optimization Criteria and Challenges : The Sixth Edition of International Conference on Pattern Analysis and Intelligent Systems (PAIS'24) 6

Centralized and Decentralized Federated Learning in Autonomo...

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6th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2024

作者： Hafid, Aicha Hocine, Riadh Guezouli, Lahcene Abdessemed, Mohamed. R University of Batna2 Dept. Math And Computer Science Batna05000 Algeria

ISBN: (纸本)9798350350265

Fault tolerance and energy consumption optimization are critical issues in swarm robotics. This study examines recent approaches to address these challenges, focusing on a comparative analysis between Centralized Federated Learning (CFL) and Decentralized Federated Learning (DFL). CFL requires a centralized access point for model aggregation, while DFL eliminates the need for a central server, enabling aggregation at each node according to a specific architecture. The analysis of the results reveals that other approaches, such as Hybrid Federated Learning (HFL), more effectively meet the needs of intelligent agents (swarm robots). This effectiveness is particularly enhanced when HFL is combined with Deep Reinforcement Learning (DRL), resulting in Deep Hybrid Federated Reinforcement Learning (DHFRL). The results demonstrate that, although DFL eliminates the necessity of a central server, hybrid approaches are more efficient, especially when combined with Deep Reinforcement Learning (DRL), thus forming Deep Hybrid Federated Reinforcement Learning (DHFRL). © 2024 IEEE.

关键词： Reinforcement learning

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Proposed Deep Learning Approach for Plant Diseases Detection 4

Proposed Deep Learning Approach for Plant Diseases Detection

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4th International Conference on Embedded and Distributed Systems, EDiS 2024

作者： Abden, Sofiane Bendjima, Mostefa Benkrama, Soumia Gasbaoui, Mohammed El Amin Kourtiche, Ikram Tahri Mohamed University Ltit Lab Dept. of Math and Computer Science Bechar Algeria

ISBN: (数字)9798331519872

ISBN: (纸本)9798331519872

Farmers in most agricultural countries usually experience high annual losses due to crop diseases and plant infections, which threatens global food security and worsens the famine situation. Researchers have focused on trying to detect the earliest symptoms of tomato plant diseases due to the multilateral effort to find the potential cure to further prevent plant decay cases and decrease the food shortage risk worldwide. On the global market, tomatoes are one of the most grown crops and offer substantial value from sales. In this paper, we are proposing a model based on a convolutional neural network and using the Very Deeper VGG16 architecture for tomato plant disease detection and classification. We have employed our method to identified and classified ten classes, nine for various disease types and one for healthy tomato leaves, a dataset of 16,012 images of tomato leaves from PlantVillage with ten diverse categories has employed. The comparison results showed that VGG16 achieved the highest accuracy of 98%, outperforming the CNN by 2.2%. And we compared the results with some recent related works. © 2024 IEEE.

关键词： Deep learning Plant diseases Accuracy Plants (biology) Crops Globalization Food security Feature extraction Real-time systems Convolutional neural networks

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An adaptive Particle Swarm Optimization to Solve Modern Security System 7

An adaptive Particle Swarm Optimization to Solve Modern Secu...

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Ahmed, Mohammed H. Hashim, Asaad Noori Hussein, Khalid A. Mustansiriyah University Dept. Computer Science College of Education Baghdad Iraq University of Kufa College of Computer Science and Math Dept. Computer Science Najaf Iraq

ISBN: (纸本)9798350383065

Nonlinear stream ciphers hold a vital position in ensuring confidentiality within contemporary cryptographic systems. In this context, we interduce a new approach known as the Adaptive Three Sub-Swarm PSO based on Regrouping Strategy (ATSPSO-RG) to enhance the effectiveness of deciphering nonlinear stream ciphers, particularly those based on the Bruer generator system. Our method significantly reduces the complexity of cipher-only attacks, making the extraction of the correct secret key more efficient. The proposed system dividing the population size into three distinct subswarm, each incorporating its evolving strategies, namely the single score, four score, and nine score strategies. In the single score strategy, we employed, the traditional PSO evolving process. In the four scores strategy, we used three additional scores in addition to the single score of the traditional PSO to update the velocity and position of particles. lastly, the nine score strategy employed five scores to update the velocity and position, as well as the four scores of the previous score. To counteract the issue of premature convergence, we employ a regrouping strategy that periodically reinitializes the population. Experimental results vividly demonstrate the success of the ATSPSO-RG method in deciphering the Bruer cipher, even when dealing with a varying number of Linear Feedback Shift Registers (LFSRs) and polynomial degrees, spanning up to 15, and secret initial values extending up to 39. The method proves its efficiency in cryptanalysis, offering a potent solution for nonlinear stream ciphers with substantial key sizes while ensuring a reasonable timeframe for analysis. The research outcomes underscore the efficacy of ATSPSO-RG, highlighting both its accuracy and time efficiency in deciphering nonlinear stream ciphers. © 2023 IEEE.

关键词： Efficiency

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Leveraging Language Models for Human Activity Recognition for Intelligent Lighting

Leveraging Language Models for Human Activity Recognition fo...

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Sustainable Smart Lighting World Conference & Expo (LS18), IEEE

作者： Ali Mahmoudi Tanir Ozcelebi Nirvana Meratnia Math. and Computer Science Dept. Eindhoven University of Technology Eindhoven The Netherlands

ISBN: (数字)9798331540197

ISBN: (纸本)9798331540203

Recognizing human activities is necessary to achieve fully context-aware lighting in a smart home. Ambient sensors suit this task as they are ubiquitous, non-intrusive, and non-obstructive. However, each home has a unique floor plan and a unique number and placement of these sensors. This leads to heterogeneity of input data spaces for machine learning models, which hinders their use in different homes. We propose that converting the data into the semantic input space of a language model can not only unify the input space across different houses but also improve the prediction performance thanks to the model’s understanding of semantics. We compared a pre-trained BERT model with one trained from scratch to test the proposed semantic conversion on three datasets from CASAS. The results show that the pre-trained model performs better on accuracy and F1-score.

关键词： Training Semantics Smart homes Predictive models Prediction algorithms Data models Reliability Smart lighting Intelligent sensors Testing

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Enhancing Medical Image Quality using Neutrosophic Fuzzy Domain and Multi-Level Enhancement Transforms: A Comparative Study for Leukemia Detection and Classification

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Neutrosophic Sets and Systems 2024年 65卷 32-56页

作者： Salama, A.A. Shams, Mahmoud Y. Khalid, Huda E. Mousa, Doaa E. Dept. of Math and Computer Sci. Faculty of Science Port Said Univ. Egypt Dept. of Machine Learning Faculty of Artificial Intelligence Kafrelsheikh University Egypt Telafer University Telafer University Telafer Iraq

Medical image processing has become a critical research area due to the vast amounts of digital image data available. However, medical images often suffer from poor illumination and low visibility of significant structures, requiring image enhancement to improve image quality before processing. In this paper, we propose a technique for enhancing medical images by removing noise and improving contrast based on three different enhancing transforms. The proposed technique embeds the image into a neutrosophic fuzzy domain, where it is mapped into three different levels of trueness, falseness, and indeterminacy, and each level is processed individually using the enhancement transforms. We compare the proposed technique with four other systems for leukemia detection and classification using accuracy and T, I, and F values. The proposed system performs the best with an accuracy of 98%, outperforming the other systems in terms of accuracy, degree of indeterminacy, and falsity. The proposed system uses different algorithms and filters to process images and extract features like color and texture. The system’s classification uses k-means for segmentation and SVM for classification. The paper highlights the importance of considering T, I, and F values in evaluating the performance of different systems for leukemia detection and classification, providing a more accurate representation of the uncertainty and ambiguity involved in the evaluation process. © (2024), (Neutrosophic Sets and Systems). All Rights Reserved.

关键词： Image enhancement

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Designing Distributed Algorithms for Autonomous Robots 22

Designing Distributed Algorithms for Autonomous Robots

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23rd International Conference on Distributed Computing and Networking, ICDCN 2022

作者： Navarra, Alfredo Dept. Math. and Comput. Science University of Perugia Italy

ISBN: (纸本)9781450395601

In this tutorial, the aim is to report some basic results about computational models representing robot based computing systems. In particular, we consider what in the literature is referred to as the model. This is one of the most investigated theoretical models that concerns the minimization of the requirements/capabilities of the involved robots to accomplish a given task in a distributed way. The tutorial is divided into two parts. The first one is more introductive and concerns the specification of the model along with its possible variants. The second part is instead devoted to one of the main basic problems investigated in the literature within robot based computing systems: the Pattern Formation. We describe the known main achievements and introduce challenging research directions. © 2022 ACM.

关键词： Robots

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Revealing Modern Stream Ciphers Security: A Comprehensive Review

Revealing Modern Stream Ciphers Security: A Comprehensive Re...

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2023 International Conference on Engineering, Applied and Nano sciences, ICEANS 2023

作者： Albawi, Mohammed Hashim, Asaad N. Hussein, Khalid A. College of Education Technical Mustansiriyah University Dept. Computer Science Baghdad Iraq College of Computer Science and Math University of Kufa Dept. Computer Science Najaf Iraq College of Education Mustansiriyah University Dept. Computer Science Baghdad Iraq

ISBN: (纸本)9798350341973

Stream ciphers are essential cryptographic primitives widely employed in various applications for efficient data protection. This paper presents a comprehensive review of attacks on the Grain, Trivium, and SNOW stream ciphers, which are considered prominent in modern cryptography. Through an extensive analysis of existing research and publications, this work systematically categorizes and evaluates known attacks, shedding light on the vulnerabilities and weaknesses exhibited by these ciphers. By examining a diverse range of attack techniques, including algebraic, statistical, and differential attacks, this work provides insights into the security landscape of Grain, Trivium, and SNOW. Furthermore, it conducts a comparative analysis of the findings across the three ciphers, identifying common patterns and vulnerabilities. The outcomes of our study have profound implications for the practical deployment and usage of these stream ciphers, emphasizing the ongoing need for research and the development of robust countermeasures. By gaining a comprehensive understanding of the weaknesses exposed by these attacks, cryptographic practitioners can make informed decisions concerning the implementation and security of these stream ciphers. © 2023 IEEE.

关键词： Cryptanalysis Cryptology Grain family LFSR SNOW Stream cipher Trivium

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Building a Real-Time System to Monitor Students Electronically Based on Digital Images of Face Movement 4

Building a Real-Time System to Monitor Students Electronical...

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4th International Conference on Advanced science and Engineering, ICOASE 2022

作者： Saeed, Nagham Tharwat Ahmed, Hasan Maher College of Education for Pure Science University of Mosul Dept. of Computer Science Mosul Iraq College of Computer Science and Math. University of Mosul Software Dept Mosul Iraq

ISBN: (纸本)9781665472227

The spread of the Corona Virus pandemic on a global scale had a great impact on the trend towards e-learning. In the virtual exams the student can take his exams online without any papers, in addition to the correction and electronic monitoring of the exams. Tests are supervised and controlled by a camera and proven cheat-checking tools. This technology has opened the doors of academic institutions for distance learning to be wide spread without any problems at all. In this paper, a proposed model was built by linking a computer network using a server/client model because it is a system that distributes tasks between the two. The main computer that acts as a server (exam observer) is connected to a group of sub-computers (students) who are being tested and these devices are considered the set of clients. The proposed student face recognition system is run on each computer (client) in order to identify and verify the identity of the student. When another face is detected, the program sends a warning signal to the server. Thus, the concerned student is alerted. This mechanism helps examinees reduce cheating cases in early time. The results obtained from the face recognition showed high accuracy despite the large number of students' faces. The performance speed was in line with the test performance requirements, handling 1,081 real photos and adding 960 photos. © 2022 IEEE.

关键词： Students

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Analysing Breast Cancer Classification Using Explainable Artificial Intelligence 12

Analysing Breast Cancer Classification Using Explainable Art...

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12th International Conference on Internet of Everything, Microwave, Embedded, Communication and Networks, IEMECON 2024

作者： Fardeen, Kazi Bhaduri, Harshadeep Hazra, Soham Mondal, Praloy Manna, Sarbajit Si, Tapas Dept. of Computer Science & Electronics Ramakrishna Mission Vidyamandira Belur Math West Bengal Howrah711202 India Dept. of Computer Science & Engineering University of Engineering & Management Rajasthan Jaipur India

ISBN: (纸本)9798350387315

Worldwide, breast cancer is becoming the most serious illness that affects women. It is believed that early diagnosis and treatment of breast cancer can increase survival rates and decrease the need for surgery. Machine Learning model is very reliant on features for their proper training. However, understanding how a prediction is being affected by specific features is very important for a model’s interpretation. Understanding what features support a prediction is important as it provides some transparency to the inner workings of the model. Gaussian Naive Bayes (GNB), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) are used to classify the breast cancer data, and accuracy, sensitivity, specificity, false-positive rate, precision, F1-score, and Geometric-Mean (GM) are used for the performance assessment. Furthermore, Multi-Criteria Decision Making (MCDM) is used to evaluate overall performance assessment based on the aforementioned performance measures and DT is found to be best among all the classifiers. Finally, an Explainable AI model namely LIME is used to interpret the predicted outcomes and impact of the different features of the data on the model’s prediction. © 2024 IEEE.

关键词： Lung cancer

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