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A Model for Predicting Chronic Kidney Diseases Based on Medical Data Using Reinforcement Learning

SN computer science 2024年第4期5卷 353页

作者： Nramban Kannan, Senthil Kumar Aseervatham, Joshi Moholkar, Kavita Palanimuthu, Mithun Marappan, Saranya Narendran, Muthusamy Sathar, Banu Sengan, Sudhakar Department of Computer Science & amp Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamil Nadu Chennai 600123 India Department of Computer Science and Business Systems Engineering JSPM’S Rajarshi Shahu College of Engineering Savitribai Phule Pune University Pune 411033 India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Tamil Nadu Chennai 600119 India Department of Information Technology Paavai Engineering College Tamil Nadu Namakkal 637018 India Department Information Technology KCG College of Technology Tamil Nadu Chennai 600097 India Department of Master of Computer Application T. J. Institute of Technology Chennai 600100 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627451 India

Kidney diseases (KD) are a global public health concern affecting millions. Early detection and prediction are crucial for effective treatment. Artificial intelligence (AI) techniques have been used in KDP to analyze past medical records, applying patients’ Electronic Medical Record (EHR) data. However, conventional statistical analysis methods conflict with fully comprehending the complexity of EHR data. AI algorithms have helped early KDP learn and identify complex data patterns. However, challenges include training heterogeneous historical data, protecting privacy and security, and developing monitoring system regulations. This study addresses the primary challenge of training heterogeneous datasets for real-world evaluation. Early detection and diagnosis of chronic kidney disease (CKD) is crucial for improved outcomes, reduced healthcare costs, and reliable treatment. Early treatments are crucial for CKD, as it often develops without apparent symptoms. Predictive models, particularly those using reinforcement learning (RL), can identify significant trends in complex healthcare information, which standard techniques may struggle with. The study makes KDP more accurate and reliable using RL methods on clinical data. This lets doctors find diseases earlier and treat them better by looking at static and changing health measurements. Machine learning (ML) algorithms can enhance the accuracy of AI systems over time, enhancing their effectiveness in detecting and diagnosing diseases. In the current investigation, the RL-ANN model is implemented for performing enforceable CKD by assessing the outcomes of multiple neural networks, which include FNN, RNN, and CNN, according to parameters such as accuracy, sensitivity, specificity, prediction error, prediction rate, and kidney failure rate (KFR). The recommended RL-ANN method has a lower failure rate of 70% based on the KFR data. Further, the proposed approach earned 95% in PR and 70% in analysis of errors. However, the RL

关键词： Alerting systems and diagnostic assistance Chronic kidney disease Machine learning Medical history RL-ANN

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FRAUD ONLINE PAYMENT DETECTION BASED ON MACHINE LEARNING WITH BALANCING DATA TECHNIQUE

FRAUD ONLINE PAYMENT DETECTION BASED ON MACHINE LEARNING WIT...

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2023 IET International Conference on Engineering Technologies and Applications, ICETA 2023

作者： Damanik, Nurafni Liu, Chuan Ming College of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Computer Science and Information Science National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9781839539848

In today's world, online payment has become the most popular transaction method, making payments convenient for people. However, this ease of use comes with the risk of an increasing number of online fraud incidents. Conventional rule-based systems and static fraud detection approaches often struggle to keep up with the ever-evolving tactics of fraudsters. Consequently, there is a growing need for advanced fraud detection methods that can adapt to dynamic fraud patterns, which presents both importance and challenges. This study proposes a fraud online payment detection system based on Logistic Regression and Decision Tree algorithms, enhanced through the use of oversampled data techniques. The experimental evaluation reveals that the Decision Tree, when utilizing the random over-sampling (ROS) technique, outperforms other techniques in terms of accuracy, achieving a remarkable 100% accuracy rate. © The Institution of Engineering & technology 2023.

关键词： Decision trees

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Leveraging Gradient-Based Optimizer and Deep Learning for Automated Soil Classification Model

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computers, Materials & Continua 2023年第7期76卷 975-992页

作者： Hadeel Alsolai Mohammed Rizwanullah Mashael Maashi Mahmoud Othman Amani A.Alneil Amgad Atta Abdelmageed Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityP.O.box 103786Riyadh11543Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Soil classification is one of the emanating topics and major concerns in many *** the population has been increasing at a rapid pace,the demand for food also increases *** approaches used by agriculturalists are inadequate to satisfy the rising demand,and thus they have hindered soil *** comes a demand for computer-related soil classification methods to support *** study introduces a Gradient-Based Optimizer and Deep Learning(DL)for Automated Soil Clas-sification(GBODL-ASC)*** presented GBODL-ASC technique identifies various kinds of soil using DL and computer vision *** the presented GBODL-ASC technique,three major processes are *** the initial stage,the presented GBODL-ASC technique applies the GBO algorithm with the EfficientNet prototype to generate feature *** soil categorization,the GBODL-ASC procedure uses an arithmetic optimization algorithm(AOA)with a Back Propagation Neural Network(BPNN)*** design of GBO and AOA algorithms assist in the proper selection of parameter values for the EfficientNet and BPNN models,*** demonstrate the significant soil classification outcomes of the GBODL-ASC methodology,a wide-ranging simulation analysis is performed on a soil dataset comprising 156 images and five *** simulation values show the betterment of the GBODL-ASC model through other models with maximum precision of 95.64%.

关键词： Soil classification earth sciences machine learning parameter optimization metaheuristics

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Novel Algorithm for Mobile Robot Path Planning in Constrained Environment

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computers, Materials & Continua 2022年第5期71卷 2697-2719页

作者： Aisha Muhammad Mohammed A.H.Ali Sherzod Turaev Ibrahim Haruna Shanono Fadhl Hujainah Mohd Nashrul Mohd Zubir Muhammad Khairi Faiz Erma Rahayu Mohd Faizal Rawad Abdulghafor Faculty of Manufacturing Engineering Universiti Malaysia Pahang(UMP)Pekan26600Malaysia Department of Mechanical Engineering Faculty of EngineeringUniversity of Malaya50603Kuala LumpurMalaysia Department of Computer Science and Software Engineering College of Information TechnologyUnited Arab Emirates UniversityAl AinUnited Arab Emirates Faculty of Electrical and Electronics Engineering Universiti Malaysia Pahang(UMP)Pekan26600Malaysia Department of Mechatronics Engineering Faculty of TechnologyBayero UniversityKano(BUK)700241Nigeria Computer Science and Engineering Department Chalmers and University of Gothenburg41296GothenburgSweden Department of Artificial Intelligence Faculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala LumpurMalaysia Department of Computer Science Faculty of Information and Communication TechnologyInternational Islamic University Malaysia53100Kuala LumpurMalaysia

This paper presents a development of a novel path planning algorithm, called Generalized Laser simulator (GLS), for solving the mobilerobot path planning problem in a two-dimensional map with the presence ofconstraints. This approach gives the possibility to find the path for a wheelmobile robot considering some constraints during the robot movement inboth known and unknown environments. The feasible path is determinedbetween the start and goal positions by generating wave of points in alldirection towards the goal point with adhering to constraints. In simulation,the proposed method has been tested in several working environments withdifferent degrees of complexity. The results demonstrated that the proposedmethod is able to generate efficiently an optimal collision-free path. Moreover,the performance of the proposed method was compared with the A-star andlaser simulator (LS) algorithms in terms of path length, computational timeand path smoothness. The results revealed that the proposed method hasshortest path length, less computational time and the best smooth path. Asan average, GLS is faster than A∗ and LS by 7.8 and 5.5 times, respectivelyand presents a path shorter than A∗ and LS by 1.2 and 1.5 times. In orderto verify the performance of the developed method in dealing with constraints, an experimental study was carried out using a Wheeled Mobile Robot(WMR) platform in labs and roads. The experimental work investigates acomplete autonomous WMR path planning in the lab and road environmentsusing a live video streaming. Local maps were built using data from a live video streaming with real-time image processing to detect segments of theanalogous-road in lab or real-road environments. The study shows that theproposed method is able to generate shortest path and best smooth trajectoryfrom start to goal points in comparison with laser simulator.

关键词： Path planning generalized laser simulator wheeled mobile robot global path panning local path planning

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Harnessing the Power of Swarm Intelligence and Long Short-Term Memory Networks in Wearable Healthcare systems 1

Harnessing the Power of Swarm Intelligence and Long Short-Te...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Kumar, K. SriRam Suriya, S. Karpagam Institute of Technology Department of Information Technology Coimbatore India Psg College of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350359756

Current developments in cloud services, artificial intelligence (AI), and the Internet of Things (IoT) have transformed the traditional healthcare system into smart healthcare. Improved medical services can be achieved by utilising important technologies like AI and IoT. The current study article offers a novel illness detection model for smart healthcare systems that is based on the convergence of AI and IoT. The primary goal of this article is to develop a disease detection model for heart disease by combining data from smart wearable devices with convergence methodologies in artificial intelligence. The model under discussion involves many stages, such as gathering data, pre-processing, categorization, and adjusting parameters. Wearable's and other Internet of Things (IoT) devices facilitate the easy collecting of data, which is then used by artificial intelligence (AI) algorithms to diagnose diseases. For illness diagnosis, the suggested approach makes use of the Swarm Intelligence-Powered Long Short-Term Memory (SILSTM) model. Healthcare data was used to validate the SILSTM model's performance. The research revealed that the SILSTM model that was provided could diagnose cardiac disease with a maximum accuracy of 96.16%. For this reason, smart healthcare systems can make use of the suggested SILSTM model as a suitable instrument for disease diagnosis. © 2023 IEEE.

关键词： Internet of things

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Magnificent Frigatebird Optimization: A New Bio-Inspired Metaheuristic Approach for Solving Optimization Problems

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computers, Materials & Continua 2024年第8期80卷 2721-2741页

作者： Tareq Hamadneh Khalid Kaabneh Ibraheem AbuFalahah Gulnara Bektemyssova Galymzhan Shaikemelev Dauren Umutkulov Sayan Omarov Zeinab Monrazeri Frank Werner Mohammad Dehghani Department of Mathematics Al Zaytoonah University of JordanAmman11733Jordan Faculty of Information Technology Al-Ahliyya Amman UniversityAmman19328Jordan Department of Mathematics Faculty of ScienceThe Hashemite UniversityP.O.Box 330127Zarqa13133Jordan Department of Computer Engineering International Information Technology UniversityAlmaty050000Kazakhstan Department of Big Data Analytics and Data Science Software Development Company《QazCode》Almaty050063Kazakhstan Department of Electrical and Electronics Engineering Shiraz University of TechnologyShiraz***Iran Faculty of Mathematics Otto-von-Guericke UniversityP.O.Box 4120Magdeburg39016Germany

This paper introduces a groundbreaking metaheuristic algorithm named Magnificent Frigatebird Optimization(MFO),inspired by the unique behaviors observed in magnificent frigatebirds in their natural *** foundation of MFO is based on the kleptoparasitic behavior of these birds,where they steal prey from other *** this process,a magnificent frigatebird targets a food-carrying seabird,aggressively pecking at it until the seabird drops its *** frigatebird then swiftly dives to capture the abandoned prey before it falls into the *** theoretical framework of MFO is thoroughly detailed and mathematically represented,mimicking the frigatebird’s kleptoparasitic behavior in two distinct phases:exploration and *** the exploration phase,the algorithm searches for new potential solutions across a broad area,akin to the frigatebird scouting for vulnerable *** the exploitation phase,the algorithm fine-tunes the solutions,similar to the frigatebird focusing on a single target to secure its *** evaluate MFO’s performance,the algorithm is tested on twenty-three standard benchmark functions,including unimodal,high-dimensional multimodal,and fixed-dimensional multimodal *** results from these evaluations highlight MFO’s proficiency in balancing exploration and exploitation throughout the optimization *** studies with twelve well-known metaheuristic algo-rithms demonstrate that MFO consistently achieves superior optimization results,outperforming its competitors across various *** addition,the implementation of MFO on four engineering design problems shows the effectiveness of the proposed approach in handling real-world applications,thereby validating its practical utility and robustness.

关键词： Optimization metaheuristic magnificent frigatebirds exploration exploitation

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Hesitant Fuzzy-Sets Based Decision-Making Model for Security Risk Assessment

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computers, Materials & Continua 2022年第2期70卷 2297-2317页

作者： Ahmed S.Alfakeeh Abdulmohsen Almalawi Fawaz Jaber Alsolami Yoosef B.Abushark Asif Irshad Khan Adel Aboud S.Bahaddad Alka Agrawal Rajeev Kumar Raees Ahmad Khan Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025Uttar PradeshIndia Department of Computer Applications Shri Ramswaroop Memorial UniversityLucknow-Deva RoadBarabanki225003Uttar PradeshIndia

Security is an important component in the process of developing healthcare web *** need to ensure security maintenance;therefore the analysis of healthcare web application’s security risk is of utmost *** must be considered to minimise the security ***,security risk management activities are revised,prepared,implemented,tracked,and regularly set up efficiently to design the security of healthcare web *** the security risk of a healthcare web application must be considered as the key *** is,in specific,seen as an add-on during the development process of healthcare web applications,but not as the key *** must ensure that security is taken into account right from the earlier developmental stages of the healthcare web *** this row,the authors of this study have used the hesitant fuzzy-based AHP-TOPSIS technique to estimate the risks of various healthcare web applications for improving *** approach would help to design and incorporate security features in healthcare web applications that would be able to battle threats on their own,and not depend solely on the external security of healthcare web ***,in terms of healthcare web application’s security-durability,the security risk variable is measured,and vice ***,the findings of our study will also be useful in improving the durability of several web applications in healthcare.

关键词： Web applications security risk security durability hesitantbased decision-making approach

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Hierarchical Stream Clustering Based NEWS Summarization system

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computers, Materials & Continua 2022年第1期70卷 1263-1280页

作者： M.Arun Manicka Raja S.Swamynathan Department of Computer Science and Engineering RMKCollege of Engineering and TechnologyChennai602106India Department of Information Science and Technology College of Engineering GuindyAnna UniversityChennai600025India

News feed is one of the potential information providing sources which give updates on various topics of different *** updates on various topics need to be collected since the domain specific interested users are in need of important updates in their domains with organized data from various *** this paper,the news summarization system is proposed for the news data streams from RSS feeds and Google *** news stream analysis requires live content,the news data are continuously collected for our *** contributions of thiswork involve domain corpus based news collection,news content extraction,hierarchical clustering of the news and summarization of *** of the existing news summarization systems lack in providing dynamic content with domain wise *** is alleviated in our proposed systemby tagging the news feed with domain corpuses and organizing the news streams with the hierarchical structure with topic wise ***,the news streams are summarized for the users with a novel summarization *** proposed summarization system generates topic wise summaries effectively for the user and no system in the literature has handled the news summarization by collecting the data dynamically and organizing the content *** proposed system is compared with existing systems and achieves better results in generating news *** Online news content editors are highly benefitted by this system for instantly getting the news summaries of their domain interest.

关键词： News feed content similarity parallel crawler collaborative filtering hierarchical clustering news summarization

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Threat Modeling for Honeypot Deployment 10

Threat Modeling for Honeypot Deployment

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10th IEEE information technology International Seminar, ITIS 2024

作者： Silaen, Kalpin Erlangga Gaol, Ford Lumban Supangkat, Suhono Harso Ranti, Benny Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta Indonesia Institute of Technology Bandung Faculty of Digital Communication and Hotel & Tourism Bandung Indonesia Universitas Indonesia Faculty of Computer Science Depok Indonesia

ISBN: (纸本)9798331521295

The increasing complexity of cyber attacks, including 0-day vulnerabilities and APTs, has rendered traditional defenses like firewalls and IDS/IPS insufficient. Honeypots have been proposed as a solution to detect and analyze new attack types by simulating vulnerable systems and capturing malicious activities. However, the deployment of honeypots introduces unique risks, necessitating a comprehensive threat modeling approach to mitigate potential drawbacks. This paper explores the intersection of threat modeling and honeypot deployment, identifying threats, and proposing effective mitigation strategies. We applied a threat model adapted from a simplified PASTA framework based on risk-centric and proposed mitigation plans such as network segmentation, outbound traffic filtering, resource monitoring, and regular updates. Additionally, we discussed alternative honeypot deployments outside the organization's internal network to avoid arising risks. We found that deploying high-interaction honeypots carries high risks due to a broader attack flow. The novelty of this study lies in adapting the attack-flow approach for honeypot threat modeling, providing a structured method to analyze and mitigate honeypot-specific threats. Future research directions include conducting long-term studies and detailed case studies to further optimize the interaction between honeypot deployments and threat modeling for enhanced security outcomes. © 2024 IEEE.

关键词： Zero-day attack

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Deep Image Restoration Model: A Defense Method Against Adversarial Attacks

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computers, Materials & Continua 2022年第5期71卷 2209-2224页

作者： Kazim Ali Adnan N.Quershi Ahmad Alauddin Bin Arifin Muhammad Shahid Bhatti Abid Sohail Rohail Hassan Department of Information Technology University of Central PunjabLahore54000Pakistan Department of Communication Technology and Network Faculty of Computer Science and Information TechnologyUniversity Putra MalaysiaSalengor43400Malaysia Department of Computer Science Comsats University IslamabadLahore Campus54000Pakistan Othman Yeop Abdullah Graduate School of Business University Utara MalaysiaKuala Lumpur50300Malaysia

These days,deep learning and computer vision are much-growing fields in this modern world of information *** learning algorithms and computer vision have achieved great success in different applications like image classification,speech recognition,self-driving vehicles,disease diagnostics,and many *** success in various applications,it is found that these learning algorithms face severe threats due to adversarial *** examples are inputs like images in the computer vision field,which are intentionally slightly changed or *** changes are humanly *** are misclassified by a model with high probability and severely affects the performance or *** this scenario,we present a deep image restoration model that restores adversarial examples so that the target model is classified correctly *** proved that our defense method against adversarial attacks based on a deep image restoration model is simple and state-of-the-art by providing strong experimental results *** have used MNIST and CIFAR10 datasets for experiments and analysis of our defense *** the end,we have compared our method to other state-ofthe-art defense methods and proved that our results are better than other rival methods.

关键词： computer vision deep learning convolutional neural networks adversarial examples adversarial attacks adversarial defenses

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