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PD-YOLO:Colon Polyp Detection Model Based on Enhanced Small-Target Feature Extraction

computers, Materials & Continua 2025年第1期82卷 913-928页

作者： Yicong Yu Kaixin Lin Jiajun Hong Rong-Guei Tsai Yuanzhi Huang New Engineering Industry College Putian UniversityPutian351100China Engineering Research Center of Big Data Application in Private Health Medicine(Putian University) Fujian Province UniversityPutian351100China Computer College Guangdong University of Science&TechnologyDongguan523668China

In recent years,the number of patientswith colon disease has increased *** polyps are the precursor lesions of colon *** not diagnosed in time,they can easily develop into colon cancer,posing a serious threat to patients’lives and health.A colonoscopy is an important means of detecting colon ***,in polyp imaging,due to the large differences and diverse types of polyps in size,shape,color,etc.,traditional detection methods face the problem of high false positive rates,which creates problems for doctors during the diagnosis *** order to improve the accuracy and efficiency of colon polyp detection,this question proposes a network model suitable for colon polyp detection(PD-YOLO).This method introduces the self-attention mechanism CBAM(Convolutional Block Attention Module)in the backbone layer based on YOLOv7,allowing themodel to adaptively focus on key information and ignore the unimportant *** help themodel do a better job of polyp localization and bounding box regression,add the SPD-Conv(Symmetric Positive Definite Convolution)module to the neck layer and use deconvolution instead of *** results indicate that the PD-YOLO algorithm demonstrates strong robustness in colon polyp *** to the original YOLOv7,on the Kvasir-SEG dataset,PD-YOLO has shown an increase of 5.44 percentage points in AP@0.5,showcasing significant advantages over other mainstream methods.

关键词： Polyp detection YOLOv7 SPD-Conv CBAM deconvolution

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Comparative study of IoT- and AI-based computing disease detection approaches

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Data Science and Management 2025年第1期8卷 94-106页

作者： Rhmann, Wasiur Khan, Jalaluddin Khan, Ghufran Ahmad Ashraf, Zubair Pandey, Babita Khan, Mohammad Ahmar Ali, Ashraf Ishrat, Amaan Alghamdi, Abdulrahman Abdullah Ahamad, Bilal Shaik, Mohammad Khaja Department of Computer Application Lovely Professional University Punjab Phagwara144411 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur522502 India School of Computing Science and Engineering VIT Bhopal University Kothrikalan MP Sehore466114 India Babasaheb Bhimrao Ambedkar University Lucknow226025 India College of Commerce and Business Administration Dhofar University Salalah211 Oman Faculty of Computer Studies Arab Open University 18211 Bahrain Department of Computer Application Shri Ramswaroop Memorial University Lucknow225003 India College of Computing and Information Technology Shaqra University Shaqra11961 Saudi Arabia

The emergence of different computing methods such as cloud-, fog-, and edge-based Internet of Things (IoT) systems has provided the opportunity to develop intelligent systems for disease detection. Compared to other machine learning models, deep learning models have gained more attention from the research community, as they have shown better results with a large volume of data compared to shallow learning. However, no comprehensive survey has been conducted on integrated IoT- and computing-based systems that deploy deep learning for disease detection. This study evaluated different machine learning and deep learning algorithms and their hybrid and optimized algorithms for IoT-based disease detection, using the most recent papers on IoT-based disease detection systems that include computing approaches, such as cloud, edge, and fog. Their analysis focused on an IoT deep learning architecture suitable for disease detection. It also recognizes the different factors that require the attention of researchers to develop better IoT disease detection systems. This study can be helpful to researchers interested in developing better IoT-based disease detection and prediction systems based on deep learning using hybrid algorithms. © 2024 Xi'an Jiaotong University

关键词： Adversarial machine learning

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Investigating the Impact of Public-Key Infrastructure in Information Security Models for Networked applications 5th

Investigating the Impact of Public-Key Infrastructure in Inf...

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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Shree, Ritu Saxena, Kumud Mehta, Deepak Pandeya, Megha Department of Computer Science and Application Vivekananda Global University Jaipur India Department of Computer Science Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Public-key infrastructure (PKI) is poised to play a crucial function in the use of secure data technology systems. With the increasing complexity of these dais’s networks and packages, PKI gives a reliable way to electronically affirm and securely exchange critical records. This paper investigates the effect of PKI on modern-day facts security models for networked applications. An assessment of the literature is conducted to discover the limitations of various PKI‐enabled architectures and packages. The research seems at the usage of cryptography and digital certificates inside the implementation of PKI, and evaluates the different varieties of authentication, authorization, and encryption strategies in shielding networked structures. Furthermore, the paper discusses the advantages and demanding situations associated with the deployment of PKI for distinctive use instances. The findings exhibit that during order for PKI to efficiently defend networked structures, a sound authentication system need to be blended with sturdy and consumer-friendly encryption strategies. Moreover, the paper shows strategies for overcoming the demanding situations related to enforcing PKI in a relaxed and efficient way. Public-key infrastructure (PKI) is an important device in data security fashions for networked packages. PKI is a security architecture that entails the use of public and private interchangeable cryptographic keys to ensure authenticated and comfortable verbal exchange among entities. Through developing a hierarchical accept as true with device, virtual certificate are used to cozy communications and the sender and authenticity and integrity of records. This architecture gives a at ease foundation for at ease dispensed computing across multiple sites located in numerous geographical places, as it enhances confidentiality, integrity, and authentication of exchanged information. In addition, PKI can facilitate cozy statistics sharing, alleviate potential privateness issues,

关键词： Authentication

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Efficient energy-preserving methods for the Schrödinger-Boussinesq equation

Efficient energy-preserving methods for the Schrödinger-Bou...

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作者： Yan, Jinliang Zheng, Lianghong Lu, Fuqiang Zhang, Qiongyue Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry College of Mathematics and Computer Fujian354300 China Department of Information and Computer Technology No.1 Middle School of Nanping Fujian353000 China Changzhou Institute of Technology Changzhou213032 China

In this paper, we present two novel energy-preserving schemes for the Schrödinger-Boussinesq system based on the second-order Adams prediction-correction method. We first introduce supplementary variables to the initial model simultaneous its invariants to obtain a consistent and structural stable system. Then, we discretize the extended system by directly using the Fourier pseudo-spectral method in space and second-order Adams prediction-correction method in time to construct conservative schemes. Numerical results show that the proposed schemes can reach second-order accuracy and can conserve the discrete invariants very well. © 2022 John Wiley & Sons, Ltd.

关键词： Forecasting

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Construction of PCOS Prediction Model based on BP Neural Network 6

Construction of PCOS Prediction Model based on BP Neural Net...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Hu, Xianqing Yadav, Amit Khan, Asif Sah, Abhishek Pratap Azam, Sami School of Computer and Software Chengdu Neusoft University Chengdu China Faculty of Science and Technology Charles Darwin University NT Australia Department of Computer Application Integral University Lucknow India School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331522667

Polycystic ovary syndrome (PCOS), a common endocrine-metabolic disorder affecting about 10-13% of women during reproductive age worldwide, often leads to irregular menstruation, infertility, obesity, and long-term health risks such as diabetes and cardiovascular disease. In this paper, clinical data from PCOS patients and healthy control group were collected from ten hospitals containing 37 key indicators such as age, weight, BMI, menstrual cycle, etc. BP (Back Propagation) Neural Network prediction models were constructed and compared using the collected data and the cross-validation method was used for parameter tuning. It was found that BP Neural Network performed particularly well on the test set, and both demonstrated high prediction accuracy and generalization ability which provided strong evidence for the early identification and early valuable intervention opportunities for PCOS patients. © 2025 IEEE.

关键词： Prediction models

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A Hybrid Reinforcement Learning Framework for Optimizing Node Deployment in WSNs 14

A Hybrid Reinforcement Learning Framework for Optimizing Nod...

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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Priyadarshi, Rahul Rout, Jayant Kumar Gupta, Akash Kumar Ranjan, Rakesh Verma, Amit Teja, Panduranga Ravi Faculty of Engineering and Technology ITER SOA Bhubaneswar India School of Computer Application KIIT Bhubaneswar India Dept of ECE BIT Mesra Deoghar India School of Computer Science UPES Dehradun India

ISBN: (纸本)9798331531935

Efficient node deployment in Wireless Sensor Networks (WSNs) is critical for maximizing network coverage, enhancing energy efficiency, and prolonging network lifetime. However, achieving optimal node placement is challenging due to the limited energy resources and dynamic environmental conditions in WSNs. This paper presents a novel hybrid approach that combines Reinforcement Learning (RL) with the Differential Evolution (DE) algorithm to optimize node deployment in WSNs. In this method, RL dynamically adapts node positions based on local network states, while DE performs global optimization to refine these placements and improve coverage. Simulation results demonstrate that the proposed RL-DE hybrid approach significantly outperforms traditional deployment methods, standalone RL, and DE in terms of coverage, energy conservation, and network longevity. The results suggest that the RL-DE approach provides an adaptive and robust solution for node deployment in WSNs, with potential applications in real-world sensor network deployments. © 2025 IEEE.

关键词： Energy efficiency

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Behavior Prediction and Anomaly Detection: Leveraging ML for Online Child Safety

Behavior Prediction and Anomaly Detection: Leveraging ML for...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Swati Gupta Snehal Borji Prof. Pallavi Thakur Master of Computer Application Sardar Patel Institute of Technology Mumbai India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

The rapid increase in harmful content online poses significant risks to child safety, emphasizing the urgent need for effective detection and prevention systems. This study explores the use of advanced machine learning (ML) techniques to tackle these challenges by integrating natural language processing (NLP) and computer vision models. Unlike traditional approaches, our methodology emphasizes the real-time analysis of both textual and multimedia content to detect harmful behaviors such as cyberbullying, grooming, and exposure to inappropriate material. By leveraging models like GPT-Neo, LightGBM, XGBoost, and Random Forest, this work achieves high accuracy in anomaly detection and content moderation. Additionally, the study addresses critical issues such as the scarcity of annotated datasets, language diversity, and the computational demands of real-world deployment. The findings underscore the potential of ML-driven solutions to create safer digital environments for children by providing comprehensive, scalable, and efficient protection systems.

关键词： computer vision Accuracy Computational modeling Diversity reception Cyberbullying Natural language processing Real-time systems Protection Random forests Anomaly detection

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An Effective Approach to Speech-Based Email Assistance for Visually Impaired People

An Effective Approach to Speech-Based Email Assistance for V...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Henwyn Shan Antony. S Sherin Eliyas Department of Computer Application Hindustan Institute of Technology and Science Chennai India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

The Speech-based Email Assistant aims to promote digital inclusivity for the blind by allowing email operations to be done hands-free via voice commands. The system is capable of uninterrupted use without any visual input, using the OpenAI Whisper model for converting speech to text and the Google Text-to-Speech (GTTS) package for providing auditory feedback. The application allows for creating, sending, retrieving, managing mailboxes, and summarizing email messages, thus providing basic Natural Language Processing (NLP) functions. Voice input is in live mode through PyAudio, and emails are sent over secure channels using SMTP and IMAP for email operations. Voice authentication alongside SHA-256 hashing is applied to enhance security, providing protection against unauthorized access while ensuring user privacy. This research aims to develop a voice-based email assistant that is user-friendly, efficient, and secure, thus promoting the digitally inclusive training of visually impaired users.

关键词： Training Visualization Authentication Natural language processing Electronic mail Text to speech Security Speech processing Usability Speech to text

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Comparative Analysis of Classification Models for Heart Disease Prediction Using Machine Learning

Comparative Analysis of Classification Models for Heart Dise...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Harish Kumar. M S. Lakshmanan Department of Computer Application Hindustan Institute of Technology and Science Chennai India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Among the most common health problems of modernity is heart disease, where early diagnosis becomes crucial for effective treatment and prevention. One cardinal responsibility of health professionals is the identification of at-risk persons to help decrease mortality through timely interventions. In this regard, a comparative analysis of machine learning classification models for heart disease prediction using a dataset with 13 medically relevant features such as gender, blood pressure, cholesterol level, family history, smoking habits, diet, treatment history, obesity, hypertension, and physical inactivity can be found in this study. The main objective of this research work is to find the most accurate and efficient classification model to predict the heart disease infallibly. Machine learning is a significant contribution to healthcare because it provides vast amounts of medical data that may be practically analyzed to derive actionable insights for clinical decision-making. Support Vector Machine (SVM) and Random Forest are the special focus among all examined models. It is found from the results that both models are doing very well in terms of predicting heart disease but Random Forest proved itself as a superior model as it achieved the highest accuracy in this study.

关键词： Heart Support vector machines Analytical models Accuracy Machine learning Medical services Predictive models Random forests Medical diagnostic imaging Diseases

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Leveraging Data from Vast Unexplored Seas: Positive Unlabeled Learning for Refining Prediction Area in Good Fishing Ground Prediction 27th

Leveraging Data from Vast Unexplored Seas: Positive Unlabel...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Konii, Haruki Nakano, Teppei Miyazawa, Yasumasa Ogawa, Tetsuji Department of Communications and Computer Engineering Waseda University Tokyo Japan Intelligent Framework Laboratory Tokyo Japan Application Laboratory Japan Agency for Marine-Earth Science and Technology Yokohama Japan

ISBN: (纸本)9783031781919

This paper proposes a Positive Unlabeled (PU) learning approach to narrow down the prediction area in the context of good fishing ground prediction. PU learning, a type of semi-supervised learning, is particularly suitable for scenarios characterized by limited positive examples and abundant unlabeled data, as often encountered in fishing ground prediction tasks, where the explored sea areas identified as productive fishing spots are treated as positive instances and the vast unexplored sea areas as unlabeled data. Conventional methods often struggle to accurately model the characteristics of good fishing grounds, resulting in overly broad prediction areas or overly restrictive constraints. To tackle this challenge, we present a PU learning-based method designed to identify negative examples from the unlabeled data and consequently refine the area predicted as positive. Specifically, we train a prediction model for fishing duration, which can be considered a surrogate indicator of good fishing grounds. Subsequently, we apply this model to predict the fishing duration for unexplored areas;those areas exhibiting short fishing durations are deemed reliable negative examples. By incorporating both past positive examples and selected negative examples into a binary classification framework for predicting good fishing grounds, we aim to fine-tune the prediction area. To the best of our knowledge, this study represents the first application of PU learning in the domain of good fishing ground prediction. Experimental comparisons conducted using data from bullet tuna trolling validate the efficacy of the proposed methodology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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