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AI-Enhanced Lung Cancer Prediction: A Hybrid Model's Precision Triumph

IEEE Journal of Biomedical and Health Informatics 2024年 PP卷 1-14页

作者： Kesiku, Cyrille Yetuyetu Garcia-Zapirain, Begonya University of Deusto Department of Computer Science Electronics and TelecommunicationsEngineering Faculty Bilbao Spain

Lung cancer is considered one of the most dangerous cancers, with a 5-year survival rate, ranking the disease among the top three deadliest cancers globally. Effectively combating lung cancer requires early detection for timely targeted interventions. However, ensuring early detection poses a major challenge, giving rise to innovative approaches. The emergence of artificial intelligence offers revolutionary solutions for predicting cancer. While marking a significant healthcare shift, the imperative to enhance artificial intelligence models remains a focus, particularly in precision medicine. This study introduces a hybrid deep learning model, incorporating Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory Networks (BiLSTM), designed for lung cancer detection from patients' medical notes. Comparative analysis with the MIMIC IV dataset reveals the model's superiority, achieving an MCC of 96.2% with an Accuracy of 98.1%, and outperforming LSTM and BioBERT with an MCC of 93.5 %, an accuracy of 97.0% and MCC of 95.5 with an accuracy of 98.0% respectively. Another comprehensive comparison was conducted with state-of-the-art results using the Yelp Review Polarity dataset. Remarkably, our model significantly outperforms the compared models, showcasing its superior performance and potential impact in the field. This research signifies a significant stride toward precise and early lung cancer detection, emphasizing the ongoing necessity for Artificial Intelligence model refinement in precision medicine. Authors

关键词： Lung cancer

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Neutrosophic Fuzzy Power Management (NFPM): Tackling Uncertainty in Energy Harvesting for Sensor Networks

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Neutrosophic Sets and Systems 2025年 76卷 99-114页

作者： Alzubi, Musallam M. Mohamed, Mohamed A. Amer, Hanan M. Salama, A.A. Electronics and Communications Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt

This paper presents a novel approach known as Neutrosophic Fuzzy Power Management (NFPM) aimed at addressing the critical challenge of uncertain energy availability in Energy Harvesting Sensor Networks (EHWSNs). The main objective of this research is to enhance the management of energy resources within these networks, which traditional fuzzy logic methods often fail to do, leading to power failures and reduced reliability. NFPM utilizes neutrosophic logic to effectively model uncertainty by representing the degrees of truth, indeterminacy, and falsity of both harvested and residual energy levels. Through a fuzzy inference system, NFPM dynamically allocates energy budgets for each time slot based on these neutrosophic sets, resulting in more adaptive and conservative energy distribution. The results are validated through numerical examples and extensive simulations, demonstrating NFPM's superiority over traditional fuzzy logic, with significant improvements such as a 25% reduction in power failures, 95% enhanced network connectivity, a 15% increase in data transmission success rates, and overall improvements in energy efficiency and robustness to fluctuations and noise. This research establishes NFPM as a promising solution to the uncertainties inherent in EHWSNs. Future research directions include exploring the integration of NFPM with machine learning algorithms for predictive energy management, assessing its scalability in larger networks, and examining its applicability in other domains requiring energy management under uncertainty. © 2025, University of New Mexico. All rights reserved.

关键词： Budget control

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Fine-Tuned Attribute Weighted Naïve Bayes with Modified Partial Instances Reduction for Gaming Disorder Classification

HighTech and Innovation Journal

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HighTech and Innovation Journal 2025年第1期6卷 67-80页

作者： Latubessy, Anastasya Wardoyo, Retantyo Musdholifah, Aina Kusrohmaniah, Sri Doctoral Program Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Informatics Engineering Faculty of Engineering Universitas Muria Kudus Indonesia Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Psychology Faculty of Psychology Universitas Gadjah Mada Yogyakarta55281 Indonesia

Fine Tuning Attribute Weighted Naïve Bayes (FTAWNB) is a reliable modified Naïve Bayes model. Even though it is able to provide high accuracy on ordinal data, this model is sensitive to outliers. To improve the performance of FTAWNB, this research modified the Partial Instances Reduction (PIR) technique to make the FTAWNB more adaptive to outliers. Nevertheless, in contrast to the original PIR technique, which substitutes missing values for data values deemed outliers, the PIR technique suggested in this study replaces data values deemed outliers using a Naïve Bayes weighting approach. The attribute values from the outlier data are replaced with the highest probability values for the attributes in the actual class. This PIR technique is referred to as modified PIR. The FTAWNB model with modified PIR has been evaluated using the Gaming Disorder dataset. Replacing the four attributes with the least amount of information resulted in accuracy gains of 99.74%, an increase of 1.53% over the FTAWNB model. The experimental result shows that adding the modified PIR technique to the FTAWNB model can handle the outlier in the data, proving it by increasing the performance in terms of accuracy, precision, and recall without pruning the dataset used. © 2025, Ital Publication. All rights reserved.

关键词： Attribute Weighted Classification Fine-Tune Gaming Disorder Instances Reduction Naïve Bayes

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Impact of Approximate QRS Identification on RR Interval-Based Arrhythmia Detection and Classification 17

Impact of Approximate QRS Identification on RR Interval-Base...

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17th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2024

作者： Nagatomo, Taiki Sato, Toshinori Graduate School of Engineering Fukuoka University Department of Electronics Engineering and Computer Science Japan Faculty of Engineering Fukuoka University Department of Electronics Engineering and Computer Science Japan

ISBN: (纸本)9798331507398

Wearable devices for arrhythmia diagnosis are battery-powered and require improved power efficiency. In our previous study, we applied approximate computing, which is effective for power reduction, to QRS identification algorithm and achieved significant power reduction. However, the impact of this approximation on arrhythmia detection and classification was not fully investigated. In this study, we investigate how the approximation - - specifically, the use of two approximate multipliers and eight approximate squarers applied to the Pan-Tompkins algorithm - - affects RR interval-based arrhythmia detection and classification. We use 48 samples from the MIT-BIH Arrhythmia Database to evaluate the accuracy of arrhythmia detection and classification. It was found that the approximate multiplier mul16s_GQU is the best choice, achieving nearly 100% accuracy and less than 1 % false negatives in abnormal/normal classification. Additionally, the most power-efficient approximate squarer, E10C1, achieves relatively high accuracy with less than 5 % false negatives. © 2024 IEEE.

关键词： approximate computing arrhythmia biomedical electronics electrocardiogram QRS complex

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Cost-effective Alternatives for Squarer in Pan-Tompkins Algorithm 24

Cost-effective Alternatives for Squarer in Pan-Tompkins Algo...

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6th International electronics Communication Conference, IECC 2024

作者： Nagatomo, Taiki Sato, Toshinori Department of Electronics Engineering and Computer Science Fukuoka University Graduate School of Engineering Japan Department of Electronics Engineering and Computer Science Fukuoka University Faculty of Engineering Japan

ISBN: (纸本)9798400717598

Reducing power consumption in battery-powered medical edge devices is highly required. This study aims to enhancing power efficiency in the Pan-Tompkins algorithm used for electrocardiogram (ECG) feature detection by adopting approximate computing. This paper specifically focuses on the squaring step within the algorithm, investigating eight alternative approaches to replace the multiplier with a dedicated squarer. We evaluate power and area reduction rates, as well as the feature detection accuracy of the proposed approximate squarers. The best candidate achieves a 99.40% power reduction and a 99.47% area reduction while maintaining a 99.67% detection accuracy. © 2024 ACM.

关键词： Electrocardiograms

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Early Parkinson's Disease Detection and Classification using Machine Learning and Neutrosophic Sets

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Neutrosophic Sets and Systems 2025年 82卷 873-886页

作者： Oudah, Hasan H. Metwaly, Ahmed A. Eassa, Mohamed Abdelhafeez, Ahmed Nagm, Ahmad M. Salama, Ahmed S. College of Arts Department of Journalism Ahl Al Bayt University Karbala Iraq Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Engineering and Electronics Cairo Higher Institute for Engineering Computer Science and Management New Cairo Egypt

Optimizing therapy and rehabilitation for Parkinson's disease (PD) requires early identification and precise evaluation of the illness's course. However, there is disagreement about the best way to use gait analysis to categorize the severity of motor symptoms and identify early-stage Parkinson's disease. The precision of machine learning (ML) models in identifying early and intermediate stages of Parkinson's disease was assessed in this study. Six ML models are used in this study for the prediction of PD. Different metrics are used to evaluate ML models such as accuracy, precision, recall, f1-score, and AUC score. Then we propose a multi-criteria decision-making methodology (MCDM) to evaluate the ML models and select the best one. Two MCDM methods are used such as CRITIC method to compute the criteria weights and the TOPSIS method to rank the alternatives. These methods are used under the bipolar neutrosophic sets (BNSs) to deal with uncertainty information. The results show the support vector machine is the best ML model for the prediction of PD. © 2025, University of New Mexico. All rights reserved.

关键词： Neurodegenerative diseases

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Enhancing the efficiency of lung cancer screening: predictive models utilizing deep learning from CT scans

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Neural Computing and Applications 2025年第17期37卷 11459-11477页

作者： Tawfeek, Medhat A. Alrashdi, Ibrahim Alruwaili, Madallah Shaban, Warda M. Talaat, Fatma M. Department of Computer Science Faculty of Computers and Information Menoufia University Shebin Elkom32511 Egypt Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt

Lung cancer is the most lethal form of cancer. This paper introduces a novel framework to discern and classify pulmonary disorders such as pneumonia, tuberculosis, and lung cancer by analyzing conventional X-ray and CT scan images called lung cancer risk prediction (LCRP) model. LCRP has four modules, namely data collection and preprocessing, data augmentation module, image segmentation module, and prediction module. Actually, LCRP employs three deep learning models;sequential model, functional model, and transfer model on publicly available training datasets. Convolutional neural networks (CNNs) have emerged as a highly effective field in machine learning, particularly for image datasets in the field of biomedical applications. The primary goal is to validate these models by comparing their performance with other models in order to determine their effectiveness in addressing challenging datasets. Our research has revealed a noteworthy enhancement in the efficiency of binary and multi-class classification using mask R-CNN image segmentation. During the model training process, a combination of Adam and stochastic gradient descent dual optimizers has been used to improve performance. LCRP have outperformed current pre-trained models by minimizing training parameters, computational costs, and overhead. It introduces 98.5% accuracy, 88.7% specificity, 89% sensitivity, 89.2% precision, and 89.09% F-measure. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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An Ant-colony Based Model for Load Balancing in Fog Environments

Supercomputing Frontiers and Innovations

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Supercomputing Frontiers and Innovations 2023年第1期10卷 4-20页

作者： Mirtaheri, Seyedeh Leili Azari, Mahya Greco, Sergio Arianian, Ehsan Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran Faculty of Mathematics and Computer Science Kharazmi University Tehran Iran Department of Computer Engineering Modeling Electronics and Systems University of Calabria Italy Tehran Iran

Delay-sensitive applications are becoming more and more in demand as a result of the development of information systems and the expansion of communication in cloud computing technologies. Some of these requests will be overlooked in cloud environments due to the communication delay between the processing center and the client’s request. The ‘fog-based computing paradigm’, a novel processing model, can be added to cloud computing to help with the aforementioned issues. The performance of computing systems is always influenced by latency. In this paper, we focus on balancing the load on the fog nodes to lower the latency. It is also a crucial component of fog computing devices. It necessitates the use of load-balancing algorithms to select the optimal hosts, resulting in an even distribution of the load on the available *** provide a load-balancing approach based on the Ant-colony optimization algorithm’s latency rate for responding to tasks. A random data set evaluation of this model reveals shorter response times than those of earlier strategies suggested in this field. © The Authors 2023. This paper is published with open access at ***

关键词： Fog

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A Resource-Efficient Multi-core Multi-thread RISC-V-based System-on-Chip 21

A Resource-Efficient Multi-core Multi-thread RISC-V-based Sy...

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Kieu-Do-Nguyen, Binh Nguyen, Khai-Duy Binh, Nguyen The Dang, Tuan-Kiet Le, Duc-Hung Pham-Quoc, Cuong Tran, Ngoc-Thinh Pham, Cong-Kha Hoang, Trong-Thuc Department of Computer and Network Engineering Tokyo Japan Faculty of Computer Science and Engineering Hochiminh Viet Nam Faculty of Electronics and Telecommunications Hochiminh Viet Nam

ISBN: (纸本)9798350377088

In the past, a small size microcontroller was needed to control and gather data from resource-constrained electronic devices aimed at Internet of Things (IoT) applications. While the server nodes receive the requests with a high demand for calculation. However, to minimize network traffic transactions and the overall workload that the server node must handle, a portion of these operations must now be completed locally. Additionally, multiple tasks must be processed simultaneously by IoT devices. In this study, we suggested a multi-core, multi-thread enable System-on-Chip (SoC) that consume small resources. The suggested architecture achieves a notable speed increase over single-core design by utilizing the advantages of RISC-V Instruction Set Architecture (ISA). © 2024 IEEE.

关键词： System-on-chip

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Efficient Communication in Wireless Sensor Networks Using Optimized Energy Efficient Engroove Leach Clustering Protocol

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Tsinghua science and Technology 2024年第4期29卷 985-1001页

作者： N.Meenakshi Sultan Ahmad A.V.Prabu J.Nageswara Rao Nashwan Adnan Othman Hikmat A.M.Abdeljaber R.Sekar Jabeen Nazeer SRM Institute of Science and Technology KattankulathurChennai 603203India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj 11942Saudi Arabia University Center for Research and Development(UCRD) Department of Computer Science and EngineeringChandigarh UniversityGharuanMohali 140413India Department of ECE Koneru Lakshmaiah Education FoundationVaddeswaramGuntur 522502India Department of Computer Science and Engineering Lakireddy Balireddy College of Engineering(A)NTR District 521230India Department of Computer Engineering College of EngineeringKnowledge UniversityErbil 44001Iraq Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmman 11937Jordan Department of Electronics and Communication Engineering(ECE) School of EngineeringPresidency UniversityBangalore 834001India

The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human *** widespread deployment of wireless micro sensors will make it possible to conduct accurate environmental monitoring for a use in both civil and military *** make use of these data to monitor and keep track of the physical data of the surrounding environment in order to ensure the sustainability of the *** data have to be picked up by the sensor,and then sent to the sink node where they may be *** nodes of the WSNs are powered by batteries,therefore they eventually run out of *** energy restriction has an effect on the network life span and environmental *** objective of this study is to further improve the Engroove Leach(EL)protocol’s energy efficiency so that the network can operate for a very long time while consuming the least amount of *** lifespan of WSNs is being extended often using clustering and routing *** Meta Inspired Hawks Fragment Optimization(MIHFO)system,which is based on passive clustering,is used in this study to do *** cluster head is chosen based on the nodes’residual energy,distance to neighbors,distance to base station,node degree,and node *** on distance,residual energy,and node degree,an algorithm known as Heuristic Wing Antfly Optimization(HWAFO)selects the optimum path between the cluster head and Base Station(BS).They examine the number of nodes that are active,their energy consumption,and the number of data packets that the BS *** overall experimentation is carried out under the MATLAB *** the analysis,it has been discovered that the suggested approach yields noticeably superior outcomes in terms of throughput,packet delivery and drop ratio,and average energy consumption.

关键词： wireless sensor networks energy efficient engroove leach protocol meta inspired Hawks fragment optimization heuristic wing antfly optimization

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