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A Systematic Review of Computational Intelligence Approaches for Prediction of Chronic Kidney Disease

A Systematic Review of Computational Intelligence Approaches...

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International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

作者： M.B.L. Prasanna G. Lavanya Devi Dept. of Computer Science and Systems Engineering Andhra University College of Engineering Visakhapatnam India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

CKD is a global health issue with rising prevalence and severe morbidity and mortality. Detecting and addressing diseases early is crucial to minimize disease progression and enhance patient outcomes. In the field of chronic kidney disease (CKD) prediction, computational intelligence (CI) algorithms have shown significant potential as effective tools, with the assurance of non-invasive and precise diagnosis. The use of CI methodologies, such as machine learning and deep learning techniques, for the prediction of chronic kidney disease (CKD) is examined in this systematic review. The advancement of CKD is linked to a number of significant effects such as an increased incidence of various ailments, Anemia, and death. There is a risk of hyperlipidaemia, nerve damage, pregnancy difficulties, and possibly renal failure. It kills millions of individuals each year. Diagnosis of CKD is difficult because there are no obvious symptoms to diagnose the illness. However, some results may be overlooked when the diagnosis is chronic. This study investigated CI approaches for CKD prediction using clinical data, biomarkers, or imaging aspects to diagnose CKD. Age, glucose level, number of red blood cells, and other symptoms were included when training the algorithm with 500 participants. The experimental results show that the suggested model excels at categorization. The objective is to develop computational intelligence (CI) algorithms for learning from attribute reports in datasets and effectively detect CKD that is more accessible. Eligible studies used CI to predict CKD based on clinical data, biomarkers, or imaging characteristics. This systematic investigation contributes to a better understanding of the landscape of CI applications in predicting CKD and provides light on potential future advances in the field.

关键词： Deep learning Machine learning algorithms Computational modeling Signal processing algorithms Imaging Prediction algorithms Chronic kidney disease Computational intelligence Diseases Systematic literature review

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Quality Obstacles of Distance Learning: Experiences from Students of the University of Benghazi 3

Quality Obstacles of Distance Learning: Experiences from Stu...

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3rd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Suweel, Osamah Sulayman Yahya Maatuk, Abdelsalam M. Abdalla, Mohamed A E Benghazi University Faculty of Information Technology Dept. of Information Systems Benghazi Libya Libyan International Medical University Faculty of Information Technology Benghazi Libya Benghazi University Faculty of Information Technology Dept. of Software Engineering Benghazi Libya

ISBN: (纸本)9798350319897

Adopting distance learning with a high level of quality in universities and educational institutions is a real challenge. Therefore, several studies and organizations have provided different standards to evaluate the quality of long-distance and to measure the obstacles. This study examines the quality obstacles of the long-distance learning techniques applied at the Faculty of Information Technology at the University of Benghazi, Libya. The British Quality Assurance Agency (BQAA) standards are utilized and presented as a questionnaire to the students at the faculty. The total participant number is 223 respondents. The study findings show that the students face severe challenges in all categories of the BQAA of technical, social, and organizational obstacles. The level of challenges is represented in the negativity of the response 69.1%, 60.1%, and 59.44% of the determined categories. © 2023 IEEE.

关键词： Quality assurance

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Privacy Protection for Recommendation systems using Federated Learning and ADMM

Privacy Protection for Recommendation Systems using Federate...

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International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

作者： Gowri Priya Thota Lavanya Devi G Dept. of Computer Science and Systems Engineering Andhra University College of Engineering Visakhapatnam India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Federated Learning (FL) represents a significant advancement in machine learning, focusing on collaborative data processing across decentralized systems while ensuring user privacy. This research explores the application of an inexact Alternating Direction Method of Multipliers (ADMM) algorithm within the context of movie recommendation systems. The inexact ADMM method improves computational efficiency and scalability by easing the strict convergence criteria. Our results demonstrate that this approach maintains high recommendation accuracy and significantly reduces communication overhead and computational costs. Additionally, we compare the inexact ADMM with the exact ADMM, highlighting the advantages and trade-offs of each method. Our study illustrates that the inexact ADMM method effectively balances local computation and global aggregation, promoting the development of more efficient and user-centric recommendation systems.

关键词： Privacy Accuracy Federated learning Signal processing algorithms Signal processing Motion pictures Convex functions Computational efficiency Protection Recommender systems

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Explainable Email Spam Detection: A Transformer-based Language Modeling Approach

Explainable Email Spam Detection: A Transformer-based Langua...

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International Conference on computer and Information Technology (ICCIT)

作者： Mohammad Amaz Uddin Md. Mahiuddin Al Akram Chowdhury Dept. of Computer Science and Engineering International Islamic University Chittagong Chattogram Bangladesh Dept. of Software Engineering Metropolitan University Sylhet Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Email spam, also known as junk email, is any unwanted or irrelevant message that is delivered to a large number of recipients without the recipient's consent. Email communication is undoubtedly efficient and convenient, but there are serious risks associated with its rise in unwanted spam emails. These risks include resource waste and potential cybersecurity breaches. It also serves as a backdoor for malware infections, phishing attempts, and other cyber threats. Transformer-based models have a positive effect on the cybersecurity sector in order to address those cybersecurity concerns. In this research, we use a Bidirectional Encoder Representations from Transformers (BERT) variant, the DistilBERT model, to detect spam emails. For this purpose, we utilize the benchmark Enron spam email dataset, applying several preprocessing techniques to address the noisy data. Our model demonstrates high accuracy, achieving 99.18%. Additionally, to interpret the model's decision-making process, we used Transformers Interpret. This explainability tool provides insights into how our proposed model works and makes its decisions throughout the experiment.

关键词： Unsolicited e-mail Large language models Phishing Decision making Benchmark testing Transformers Robustness Malware Noise measurement Information technology

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Learning Adaptive Safety for Multi-Agent systems

Learning Adaptive Safety for Multi-Agent Systems

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Luigi Berducci Shuo Yang Rahul Mangharam Radu Grosu Institute of Computer Engineering TU Wien Dept. of Electrical and Systems Engineering University of Pennsylvania

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Ensuring safety in dynamic multi-agent systems is challenging due to limited information about the other agents. Control Barrier Functions (CBFs) are showing promise for safety assurance but current methods make strong assumptions about other agents and often rely on manual tuning to balance safety, feasibility, and performance. In this work, we delve into the problem of adaptive safe learning for multi-agent systems with CBF. We show how emergent behaviour can be profoundly influenced by the CBF configuration, highlighting the necessity for a responsive and dynamic approach to CBF design. We present ASRL, a novel adaptive safe RL framework, to fully automate the optimization of policy and CBF coefficients, to enhance safety and long-term performance through reinforcement learning. By directly interacting with the other agents, ASRL learns to cope with diverse agent behaviours and maintains the cost violations below a desired limit. We evaluate ASRL in a multi-robot system and competitive multi-agent racing, against learning-based and control-theoretic approaches. We empirically demonstrate the efficacy of ASRL, and assess generalization and scalability to out-of-distribution scenarios.

关键词： Adaptation models Costs Uncertainty Scalability Reinforcement learning Safety Observability

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FORTUNATE: Decrypting and Classifying Malware by Variable Length Instruction Sequences 13

FORTUNATE: Decrypting and Classifying Malware by Variable Le...

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13th IEEE International Conference on Cloud Networking, CloudNet 2024

作者： Andrade, Cesar A. B. Rocha Filho, Geraldo P. Meneguette, Rodolfo I. Maranhao, Joao Paulo A. Sant'ana, Ricardo Duarte, Julio Cesar Serrano, Andre Luiz M. Goncalves, Vinicius P. University of Brasília Electrical Engineering Dept. Brasília Brazil State University of Southwest Bahia Exact and Technological Sciences Dept. Vitória da Conquista Brazil Institute of Mathematics and Computer Science Universityof São Paulo São Carlos Brazil Brazilian Army Systems Development Center Brasília Brazil Military Institute of Engineering Defense Engineering Dept. Rio de Janeiro Brazil Military Institute of Engineering Computing Engineering Dept. Rio de Janeiro Brazil University of Brasília Production Engineering Dept. Brasília Brazil

ISBN: (纸本)9798350376562

After identifying an artifact as malware, the additional challenge arises of correctly classifying it into a specific family. This raises the challenge of classifying malware amid growing complexity and the increasing volume of cyber threats. Therefore, we introduce FORTUNATE, a framework that employs variable-length instruction sequences to classify malware more efficiently and accurately, focusing on real and active malware. By introducing effective methods for extracting opcodes and their representation in the smallest possible vectors, FORTUNATE surpasses the limitations of previous approaches, enabling the correct identification of malware in their respective families, while avoiding redundancies in the data. The results achieved with FORTUNATE demonstrate advances in both precision and computational efficiency, consolidating it as a significant contribution to the field of cybersecurity. © 2024 IEEE.

关键词： Long short-term memory

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Interpretable Pulmonary Disease Diagnosis with Graph Neural Network and Counterfactual Explanations 2

Interpretable Pulmonary Disease Diagnosis with Graph Neural ...

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2nd International Conference on Sensing, Measurement, Communication and Internet of Things Technologies, SMC-IoT 2023

作者： Li, Jiahong Chen, Yiyuan Wang, Yichi Ye, Yiqiang Sun, Min Ren, Hao Cheng, Weibin Zhang, Haodi College of Computer and Software Engineering Guangzhou China Cornell University Dept. Computer Science New York United States Shenzhen University College of Computer and Software Engineering Shenzhen China China Mobile Information Technology Co. Limited Shenzhen China Guangdong Second Provincial General Hospital Institute for Healthcare Artificial Intelligence Guangzhou China

ISBN: (纸本)9798350373257

With recent fast development of artificial intelligence and related techniques, computer-aided diagnosis is increasingly emerging. During the process of automated diagnosis, the importance of explainable decision making has grown significantly. Traditional black-box models have often hindered the practical implementation of automated diagnosis algorithms. To address these challenges, we propose a novel framework that seamlessly integrates prototype learning with Graph Neural Networks (GNN) while also providing post-hoc explanations. On one hand, our model achieves intrinsic interpretability by reasoning based on the similarity calculations with prototypes for each disease. On the other hand, it employs counterfactual reasoning on graphs to pinpoint the most significant features for post-hoc explanations, supporting the diagnosis process. Building on these strengths, we integrate vision-language models to effectively harness multimodal patient information, thereby capturing a more detailed understanding of their medical condition. Our experiments on real Chinese EMRs of Pulmonary diseases demonstrate that our method not only delivers precise diagnoses but also accurately identifies medical findings substantiating the diagnoses. © 2023 IEEE.

关键词： Pulmonary diseases

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A Task Scheduler for Mobile Edge Computing Using Priority-based Reinforcement Learning

A Task Scheduler for Mobile Edge Computing Using Priority-ba...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Avan, Amin Kheiri, Farnaz Mahmoud, Qusay H. Azim, Akramul Makrehchi, Masoud Rahnamayan, Shahryar Computer and Software Engineering Ontario Tech University Dept. of Electrical OshawaONL1G 0C5 Canada Brock University Dept. of Engineering St. CatharinesONL2S 3A1 Canada

ISBN: (纸本)9781665430654

Edge computing offers cloud-like services closer to users and IoT devices, providing high speed and accessibility for network users. Edge computing, often called Mobile Edge Computing (MEC), is a distributed paradigm that utilizes heterogeneous computational and storage resources with well-provisioned capabilities rather than relying on the ample resources of the cloud. In addition, edge users usually refer to portable and mobile devices that connect to and disconnect from the network at will. Therefore, scheduling tasks at the appropriate time and allocating the right resources can be modeled as a multi-objective optimization problem in MEC. Moreover, each task has specific requirements, further adding to the complexity of the optimization problem. In this study, we formulate the scheduling problem as a Markov Decision Process (MDP) to schedule the tasks. The learning time of the task scheduler is minimized when it faces new users and edge servers. Subsequently, we employ the Q-learning (QL) algorithm from the Reinforcement Learning (RL) paradigm to address the optimization problem and effectively adapt the proposed scheduler to the dynamic nature of MEC. Accordingly, we designed the valid state space, action space, and reward function with appropriate conditions and proper rewards for the proposed QL-based technique. We conducted comprehensive experiments to validate the results of the proposed solution, taking into account the inherent randomness of the QL-based technique. The experimental results demonstrate that the proposed technique achieves the lowest learning time compared to Deep learning-based and Deep RL-based approaches. Furthermore, on average, the proposed technique obtains a 72% faster runtime compared to previous works, using 58% fewer computation cycles and 50% less memory. These improvements make the proposed approach an efficient and lightweight task scheduler for MEC. © 2023 IEEE.

关键词： Edge Computing Machine Learning Markov Decision Process Mobile Edge Computing Reinforcement Learning Task Scheduling

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An Adaptive Multi-Stage Evolution Algorithm for High-Dimensional Expensive Problems

An Adaptive Multi-Stage Evolution Algorithm for High-Dimensi...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Zhang, Boyuan Lai, Rui Gong, Guanghong Yuan, Haitao Yang, Jinhong Zhang, Jia School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Cssc Systems Engineering Research Institute Beijing China Southern Methodist University Dept. of Computer Science DallasTX75275 United States

ISBN: (纸本)9781665410205

Recently, many studies have used evolutionary algorithms (EAs) to optimize complex problems across various fields, including mechanical structure design, robotics, and cloud computing. EAs simulate the process of evolution to improve solutions to a given problem iteratively. However, EAs encounter significant challenges when dealing with high-dimensional expensive problems (HEPs). The large solution space and high computing cost of fitness evaluations (FEs) make optimization with limited FEs particularly difficult. To tackle this problem, an Adaptive Multi-stage Evolution Algorithm named AMEA is proposed. In AMEA, an adaptively enhanced teaching-learning-based optimization algorithm is adopted to explore the search space and find potential areas quickly. Then, in the next stage, the Gaussian process surrogate model and a genetic learning particle swarm optimization algorithm are adopted for further exploitation. Besides, this work proposes an adaptive stage switching criterion and an individual screening mechanism to enhance the optimization ability. AMEA demonstrates strong optimization performance when applied to HEPs. We compare AMEA with several state-of-the-art HEP optimization algorithms through seven benchmark functions, and the results show that it performs competitively with other algorithms. Finally, we also validate AMEA's effectiveness with a real-world computation offloading problem. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Ensuring Fairness in Federated Learning Services: Innovative Approaches to Client Selection, Scheduling, and Rewards

Ensuring Fairness in Federated Learning Services: Innovative...

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International Conference on Distributed Computing systems

作者： Meiying Zhang Huan Zhao Sheldon Ebron Ruitao Xie Kan Yang Dept. of Computer Science University of Memphis USA College of Computer Science and Software Engineering Shenzhen University China

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Federated Learning (FL) Services enable customers (requesters) to outsource their FL tasks to the FL service provider, who will recruit a group of clients with appropriate datasets to complete the FL task. For a given FL task, how to select appropriate clients fairly becomes a challenging problem due to budget restrictions and client heterogeneity. In this paper, we propose a new client selection, scheduling, and rewarding scheme to ensure fairness through a three-stage process: 1) multicriteria initial client pool selection, 2) data quality-oriented perround client scheduling, and 3) performance-based rewarding. Specifically, we first define a client selection metric with multiple criteria, such as client resources, data quality, and client behaviors. Then, we formulate the initial client pool selection problem into an optimization problem that aims to maximize the overall scores of selected clients within a given budget and propose a greedy algorithm to solve it. Furthermore, we formulate the per-round client selection problem into a data quality-oriented scheduling problem that aims to improve model quality and guarantee fairness. We propose a heuristic algorithm to divide the pool into several subsets such that the federated dataset in a subset is close to an independent and identical distribution (iid) while guaranteeing each client is selected at least once in a scheduling period. In addition, we propose a performance-based payment adjustment protocol with a bonus and punishment mechanism, such that the final payment reflects the actual performance of each selected client. Our fairness analysis and experimental results show that our scheme not only can guarantee fairness but also can improve the model quality especially when data are non-iid.

关键词： Measurement Greedy algorithms Analytical models Federated learning Heuristic algorithms Data integrity Data models

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