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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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A new family of q-Bernstein polynomials: probabilistic viewpoint

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Arab Journal of Basic and Applied sciences 2025年第1期32卷 42-50页

作者： Karagenc, Ayse Acikgoz, Mehmet Araci, Serkan Department of Mathematics Faculty of Science and Arts Gaziantep University Gaziantep Turkey Department of Computer Engineering Faculty of Engineering Hasan Kalyoncu University Gaziantep Turkey

In this paper, we introduce a new class of polynomials, called probabilistic q-Bernstein polynomials, alongside their generating function. Assuming (Formula presented.) is a random variable satisfying moment conditions, we use the generating function of these polynomials to establish new relations. These include connections to probabilistic Stirling numbers of the second kind and higher-order probabilistic Bernoulli polynomials associated with (Formula presented.) Additionally, we derive recurrence and differentiation properties for probabilistic q-Bernstein polynomials. Utilizing Leibniz’s formula, we give an identity for the generating function of these polynomials. In the latter part of the paper, we explore applications by choosing appropriate random variables, such as Poisson, Bernoulli, Binomial, Geometric, Negative Binomial, and Uniform distributions. This allows us to derive relationships among probabilistic q-Bernstein polynomials, Bell polynomials, Stirling numbers of the second kind, higher-order Frobenius-Euler numbers, and higher-order Bernoulli polynomials. We also present p-adic q-integral and fermionic p-adic q-integral representations for probabilistic q-Bernstein polynomials. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the University of Bahrain.

关键词： 05D40 11B73 11B83 distributions generating functions moment generating functions probabilistic q-Bernstein polynomials

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Machine Learning-Based Chronic Kidney Cancer Prediction Application: A Predictive Analytics Approach

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Biomedical Materials and Devices 2024年第2期2卷 1028-1048页

作者： Uddin, Khandaker Mohammad Mohi Nahid, Md. Nuzmul Hossain Ullah, Md. Mehedi Hasan Mazumder, Badhan Khan, Md. Saikat Islam Dey, Samrat Kumar Department of Computer Science and Engineering Dhaka International University Faculty of Computer Science and Engineering Patuakhali Science and Technology University School of Science and Technology Bangladesh Open University

Chronic Kidney Cancer (CKC) is a disease that hindrances the blood-filtering mechanism of the kidney and is increasing at an alarming rate in the recent few years. As CKC does not show any earlier symptoms, the earlier prediction will be very effective to elevate its effect. In this paper, a machine learning-based method for diagnosing CKC at an early stage is proposed. SVM, Decision Tree, Random Forest, KNN, Native Bayes, Logistic Regression, LGBM, CatBoost, and AdaBoost are employed for classification purposes, and among these algorithms, Random Forest, AdaBoost, and LGBM give 99% accuracy. Data pre-processing and feature selection help improve the accuracy of the proposed model. The outcome of the comparative analysis of our proposed work with eight existing approaches proves the robustness and supremacy of the work. Furthermore, the best model is also used to develop a user-friendly web application. © The Author(s), under exclusive licence to Springer science+Business Media, LLC 2023.

关键词： AdaBoost Chronic kidney cancer Machine learning Random forest and LGBM Web application

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Optimizing breast cancer diagnosis: Harnessing the power of nature-inspired metaheuristics for feature selection with soft voting classifiers

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International Journal of Cognitive Computing in engineering 2025年第1期6卷 1-20页

作者： Benghazouani, Salsabila Nouh, Said Zakrani, Abdelali Department of Mathematics and Computer Science Faculty of Sciences Ben M'Sick Hassan II University Casablanca Morocco Department of Computer Science Engineering ENSAM Hassan II University Casablanca Morocco

Breast cancer is a widespread and serious condition that poses a significant threat to women's health globally, contributing significantly to mortality rates. Machine learning tools play a critical role in both the effective management and early detection of this disease. Feature selection (FS) methods are essential for identifying the most impactful features to improve breast cancer diagnosis. These methods reduce data dimensionality, eliminate irrelevant information, enhance learning accuracy, and improve the comprehensibility of results. However, the increasing complexity and dimensionality of cancer data pose substantial challenges to many existing FS methods, thereby reducing their efficiency and effectiveness. To overcome these challenges, numerous studies have demonstrated the success of nature-inspired optimization (NIO) algorithms across various domains. These algorithms excel in mimicking natural processes and efficiently solving complex optimization problems. Building on these advancements, we propose an innovative approach that combines powerful feature selection methods based on NIO techniques with a soft voting classifier. The NIO techniques employed include the Genetic Algorithm, Cuckoo Search, Salp Swarm, Jaya, Flower Pollination, Whale Optimization, Sine Cosine, Harris Hawks, and Grey Wolf Optimization algorithms. The Soft Voting Classifier integrates various machine learning models, including Support Vector Machines, Gaussian Naive Bayes, Logistic Regression, Decision Tree, and Gradient Boosting. These are used to improve the effectiveness and accuracy of breast cancer diagnosis. The proposed approach has been empirically evaluated using a variety of evaluation measures, such as F1 score, precision, recall, accuracy and Area Under the Curve (AUC), for performance comparison with individual machine learning techniques. The results demonstrate that the soft-voting ensemble technique, particularly when combined with feature selection based on the Jaya

关键词： Support vector machines

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Enhancing MRI-Based Brain Tumor Classification Accuracy through EfficientNetB3 and Advanced Image Data Augmentation Techniques 2

Enhancing MRI-Based Brain Tumor Classification Accuracy thro...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Vanitha, K. Mahesh, R.T. Aafiya Thahaseen, A. Manivannan, B. Anitha, K. Thakur, Arastu Faculty of Engineering Department of Computer Science and Engineering Coimbatore India Department of Computer Science and Engineering Karnataka Bangalore India Al-Ameen Engineering College Department of Computer Science and Engineering Erode India Vivekanandha College of Engineering for Women Department of Computer Science and Engineering India CSI College of Engineering Department of Information Technology Tamilnadu Ooty India

ISBN: (纸本)9798331534950

This work analyzes the possibilities of the EfficientNetB3 architecture, reinforced by modern image data augmentation methods, in the classification of brain cancers from MRI scans. Our key objective was to greatly boost the accuracy and reliability of brain tumor diagnosis by combining the advantages of a pre-trained EfficientNetB3 model, along with a custom-designed data augmentation strategy geared at effectively resolving the inherent variabilities in MRI images. To achieve this, comprehensive preprocessing protocols were applied to the MRI datasets designated for training, validation, and testing. These protocols included the resizing and normalization of images to ensure uniformity and compatibility with the neural network. Further, our approach utilized real-time image augmentation techniques such as rotations, shifts, and flips during the training process, thereby enhancing the model's exposure to a wide range of imaging scenarios and increasing its robustness against variations not present in the training set. The outcome of this research was a notable enhancement in the model's classification accuracy, achieving up to 99% accuracy in identifying various types of brain tumors. These results not only demonstrate the effectiveness of integrating sophisticated convolutional network architectures with extensive image augmentation in medical diagnostics but also suggest potential pathways for setting new standards in the accuracy and reliability of medical image analysis, particularly in the critical domain of brain tumor diagnosis. © 2024 IEEE.

关键词： Deep learning

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A Deep Learning Based System to Predict the Plant Disease Using Streamlit 4

A Deep Learning Based System to Predict the Plant Disease Us...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Warbhe, Mohan K. Bore, Joy Jordan Chaudari, Shiv Nath Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Sawangi Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering MaharashtraSawangi Wardha442001 India

ISBN: (纸本)9798331523923

The proposed web application for tomato leaf disease detection exemplifies the transformative power of Artificial Intelligence and computer Vision in modern agriculture. Addressing the critical issue of early and accurate disease detection, this solution bridges the gap between advanced technological innovations and the practical needs of farming. The application is designed using Streamlit and Python to combine the usability of a friendly interface with the power of robust analytical capabilities in CNN, offering real-time reliable diagnosis and actionable insights to farmers. This application provides the highest possible accuracy and adaptability through the thorough training and evaluation of models and is a good asset for disease management. Deployment on scalable cloud platforms ensures access, reliability, and global reach. Future improvements, including extending disease detection to other crops, multilingual support, real-time recommendations, and integration with IoT devices, promise to expand its reach and utility. This initiative is a shining example of how AI-based solutions can transform agricultural practices by enhancing productivity, reducing losses, and ensuring sustainability. The application empowers farmers and contributes to a resilient agricultural ecosystem, addressing the pressing need for innovative and sustainable solutions in a rapidly evolving world. © 2025 IEEE.

关键词： Smart agriculture

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Application of AI and ML in Wound Healing and Skin Regeneration 4

Application of AI and ML in Wound Healing and Skin Regenerat...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Petkar, Taniya G. Kumar, Praveen Sarate, Kirtiksha U. Faculty of Engineering and Technology Department of Computer Science & Medical Engineering Maharashtra Sawangi Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Sawangi Wardha442001 India

ISBN: (纸本)9798331523923

By enabling precise, individualized, and effective treatments, the integration of artificial intelligence (AI) and machine learning (ML) into wound and skin healing is revolutionizing healthcare. Artificial intelligence-controlled tools improve wound assessment, anticipate healing, and collaborate with regular examinations using cutting-edge dressings and imaging technologies. AI computations work on the management of express data to enhance treatment systems, while advancements in nanomaterials like marine collagen and therapies employing young microorganisms further accelerate skin recovery. Despite encouraging findings, there are still problems, such as potential overfitting and dataset limitations. In general, machine learning is changing the way that injuries are treated by providing proactive, tailored solutions that aim to understand outcomes and lower the cost associated with clinical outcomes. © 2025 IEEE.

关键词： Personalized medicine

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Simulation of fractional order mathematical model of robots for detection of coronavirus using Levenberg–Marquardt backpropagation neural network

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Neural Computing and Applications 2024年第35期36卷 22417-22428页

作者： Sabir, Zulqurnain Ali, Mohamed R. Sadat, R. Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Basic Engineering Mathematics Department Benha Faculty of Engineering Benha University Benha Egypt Faculty of Engineering Benha National University Obour Campus11835 Egypt Department of Mathematics Zagazig Faculty of Engineering Zagazig University Zagazig Egypt

This research motive is to present more realistic results through the fractional order derivatives of the Robots mathematical system (FORMS), which is used to detect the coronavirus-positive cases. This nonlinear FORMS is useful to prevent individuals from the coronavirus and its spread. The classification of mathematical system is categorized into two dynamics, infected and Robots. The designed FORMS has never been solved before through the stochastic Levenberg–Marquardt backpropagation (LBMBP) neural networks (NNs), i.e., LBMBP-NNs. The solution of three cases of the FORMS is presented along with the statistics of 76% training, 12% authorization and 12% testing. To observe the exactness of LBMBP-NNs, a reference dataset is constructed using the Adams scheme. For the validation and capability of LBMBP-NNs, the illustrations are drawn based on the state transition values, regression measures, correlation performances and error histograms. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Coronavirus

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AI-Driven Learning Management Systems:Modern Developments, Challenges and Future Trends during theAge of ChatGPT

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computers, Materials & Continua 2024年第8期80卷 3289-3314页

作者： Sameer Qazi Muhammad Bilal Kadri Muhammad Naveed Bilal AKhawaja Sohaib Zia Khan Muhammad Mansoor Alam Mazliham Mohd Su’ud Department of Computer Science Faculty of EngineeringScience and TechnologyIQRA UniversityKarachi75500Pakistan College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Engineering Department of Electrical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of Engineering Department of Mechanical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Computing and Informatics(FCI)Multimedia University Cyberjaya63100Malaysia Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyNSW 2007Australia

COVID-19 pandemic restrictions limited all social activities to curtail the spread of the *** foremost and most prime sector among those affected were schools,colleges,and *** education system of entire nations had shifted to online education during this *** shortcomings of Learning Management Systems(LMSs)were detected to support education in an online mode that spawned the research in Artificial Intelligence(AI)based tools that are being developed by the research community to improve the effectiveness of *** paper presents a detailed survey of the different enhancements to LMSs,which are led by key advances in the area of AI to enhance the real-time and non-real-time user *** AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources,including AI-based *** layer enhancements are also required,such as AI-based online proctoring and user authentication using *** extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of *** also needs the support of the Networking layer for IP-based geolocation features,the Virtual Private Network(VPN)feature,and the support of Software-Defined Networks(SDN)for optimum Quality of Service(QoS).Finally,in addition to these,non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.

关键词： Learning management systems chatbots ChatGPT online education Internet of Things(IoT) artificial intelligence(AI) convolutional neural networks natural language processing

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Behavior Based Group Recommendation from Social Media Dataset by Using Deep Learning and Topic Modeling

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SN computer science 2024年第6期5卷 712页

作者： Mukta, Md. Saddam Hossain Ahmed, Jubaer Raiaan, Mohaimenul Azam Khan Fahad, Nur Mohammad Islam, Muhammad Nazrul Imtiaz, Nafiz Islam, Md. Adnanul Ali, Mohammed Eunus Azam, Sami LUT School of Engineering Sciences Lappeenranta-Lahti University of Technology Department of Computer Science and Engineering United International University Department of Computer Science and Engineering Military Institute of Science and Technology Department of HCC Action Lab Monash University Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Faculty of Science and Technology Charles Darwin University

In this digital era, users frequently share their thoughts, preferences, and ideas through social media, which reflect their Basic Human Values. Basic Human Values (aka values) are the fundamental aspects of human behavior, which define what we consider important, and worth having and pursue them. Existing studies identify the values of individuals from different social network usages such as Facebook and Reddit. However, discovering the similarity (or diversity) of value priorities among the members in a group is important since we can reveal many interesting insights such as finding a set of target customers, identifying the chain of misdeed groups, searching for similar acquaintances in workplaces, etc. In this paper, a graph dataset is compiled using the strongest correlation among the features and then we apply a graph clustering technique to identify a suitable hedonist group (i.e., one dimension of values) for users’ recommendations. Then, we also propose a behavior based (i.e., value) group recommendation technique by analyzing users’ contextual and psychological attributes. Finally, we validate those group members in real life by introducing two hypotheses. In particular, we analyze the tweets of a total of 1140 users collected from Twitter. We obtain a substantial intra-cluster correlation coefficient (ICC) and silhouette clustering coefficient (SCC) scores of 65% and 76%, respectively, among the members in our discovered group. © The Author(s) 2024.

关键词： Graph neural network (GNN) Group recommendation Psychological attributes Social media Topic modeling Twitter Values

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