检索结果-内蒙古大学图书馆

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

作者： Wanjari, Ketan Verma, Prateek Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Skin cancer is the most commonly reported type of cancer globally and one of the few cancers that can be effectively treated if detected in its early stages. Recent advancements in artificial intelligence (AI) have significantly improved skin cancer diagnosis through Machine Learning (ML) and Deep Learning (DL) models. This review explores ML and DL methodologies, including Random Forest, Convolutional Neural Networks (CNNs), Inception Networks, ResNets, and Support Vector Machines (SVMs), which have been widely used for automated diagnosis. However, existing AI-based systems face several challenges, including the need for balanced datasets, model interpretability issues, computational complexity, and difficulties in generalizing across diverse populations. Moreover, multimodal detection systems and Explainable AI (XAI) present additional challenges in making AI-driven diagnoses more reliable and transparent. This article discusses these challenges while also highlighting emerging techniques that integrate multiple data sources and individualized diagnostic tools to enhance precision. By addressing these challenges, this review aims to provide insights into the practical advancements and future directions of AI in skin cancer detection and treatment. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

computer Vision and Deep Learning-enabled Weed Detection Model for Precision Agriculture

引用

computer systems science & engineering 2023年第3期44卷 2759-2774页

作者： R.Punithavathi A.Delphin Carolina Rani K.R.Sughashinir Chinnarao Kurangit M.Nirmala Hasmath Farhana Thariq Ahmed S.P.Balamurugan Department of Information Technology M.Kumarasamy College of EngineeringKarur6391 I3India Department of Computer Science and Engineering K.Ramakrishnan College of TechnologyTiruchirapalli621112India Department of Electronics and Instrumentation Easwani Engineering CollegeTamil Nadu600089India Pondichery University Puducherry605014India Department of computer science and engineering The Oxford college of EngineeringBangalore560068India Department of Computer Science Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha UniversityChennai602105India Department of Computer and Information Science Faculty of ScienceAnnamalai University Chidambaram608002India

Presently,precision agriculture processes like plant disease,crop yield prediction,species recognition,weed detection,and irrigation can be accom-plished by the use of computer vision(CV)*** plays a vital role in influencing crop *** wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are *** the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity,this study presents a novel computer vision and deep learning based weed detection and classification(CVDL-WDC)model for precision *** proposed CVDL-WDC technique intends to prop-erly discriminate the plants as well as *** proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine(ELM)based weed *** parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization(FFO)algorithm.A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced out-comes over its recent approaches interms of several measures.

关键词： Precision agriculture smart farming weed detection computer vision deep learning

来源：评论

学校读者我要写书评

暂无评论

A Convolutional Neural Network Model for Wheat Crop Disease Prediction

引用

computers, Materials & Continua 2023年第5期75卷 3867-3882页

作者： Mahmood Ashraf Mohammad Abrar Nauman Qadeer Abdulrahman A.Alshdadi Thabit Sabbah Muhammad Attique Khan Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21577Saudi Arabia Department of Computer Science Bacha Khan University CharsaddaKhyber Pakhtunkhwa24461Pakistan Department of Computer Science Federal Urdu University of ArtsScience&TechnologyIslamabad45570Pakistan Department of Information Systems and Technology College of Computer Science and EngineeringUniversity of JeddahJeddah21577Saudi Arabia Faculty of Technology and Applied Sciences Al-Quds Open UniversityAl-Bireh1804Palestine Department of Computer Science HITEC UniversityTaxila47080Pakistan

Wheat is the most important cereal crop,and its low production incurs import pressure on the *** fulfills a significant portion of the daily energy requirements of the human *** wheat disease is one of the major factors that result in low production and negatively affects the national ***,timely detection of wheat diseases is necessary for improving *** CNN-based architectures showed tremendous achievement in the image-based classification and prediction of crop ***,these models are computationally expensive and need a large amount of training *** this research,a light weighted modified CNN architecture is proposed that uses eight layers particularly,three convolutional layers,three SoftMax layers,and two flattened layers,to detect wheat diseases *** high-resolution images were collected from the fields in Azad Kashmir(Pakistan)and manually annotated by three human *** convolutional layers use 16,32,and 64 *** filter uses a 3×3 kernel *** strides for all convolutional layers are set to *** this research,three different variants of datasets are *** variants S1-70%:15%:15%,S2-75%:15%:10%,and S3-80%:10%:10%(train:validation:test)are used to evaluate the performance of the proposed *** extensive experiments revealed that the S3 performed better than S1 and S2 datasets with 93%*** experiment also concludes that a more extensive training set with high-resolution images can detect wheat diseases more accurately.

关键词： Machine learning wheat crop disease prediction convolutional neural network artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Credit card default prediction using ML and DL techniques

引用

Internet of Things and Cyber-Physical systems 2024年第1期4卷 293-306页

作者： Wahab, Fazal Khan, Imran Sabada, Sneha Corvit Systems Peshawar Pakistan Department of Informatics University of Sussex United Kingdom Department of Engineering and Computer Science University of Hertfordshire United Kingdom

The banking sector is widely acknowledged for its intrinsic unpredictability and susceptibility to risk. Bank loans have emerged as one of the most recent services offered over the past several decades. Banks typically serve as intermediaries for loans, investments, short-term loans, and other types of credit. The usage of credit cards is experiencing a steady increase, thereby leading to a rise in the default rate that banks encounter. Although there has been much research investigating the efficacy of conventional Machine Learning (ML) models, there has been relatively less emphasis on Deep Learning (DL) techniques. The application of DL approaches to credit card default prediction has not been extensively researched despite their considerable potential in numerous fields. Moreover, the current literature frequently lacks particular information regarding the DL structures, hyperparameters, and optimization techniques employed. To predict credit card default, this study evaluates the efficacy of a DL model and compares it to other ML models, such as Decision Tree (DT) and Adaboost. The objective of this research is to identify the specific DL parameters that contribute to the observed enhancements in the accuracy of credit card default prediction. This research makes use of the UCI ML repository to access the credit card defaulted customer dataset. Subsequently, various techniques are employed to preprocess the unprocessed data and visually present the outcomes through the use of exploratory data analysis (EDA). Furthermore, the algorithms are hypertuned to evaluate the enhancement in prediction. We used standard evaluation metrics to evaluate all the models. The evaluation indicates that the AdaBoost and DT exhibit the highest accuracy rate of 82 % in predicting credit card default, surpassing the accuracy of the ANN model, which is 78 %. © 2024 The Authors

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

引用

Research on Biomedical engineering 2025年第2期41卷 1-19页

作者： A, Padmashree Sankar, P. Alkhayyat, Ahmad Muniyandy, Elangovan Department of Computer Science and Business Systems Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Applied Science Research Center Applied Science Private University Amman Jordan

Purpose: The primary objective of this research is to develop a comprehensive framework for the analysis of brain tumor images, addressing the complexities of detection, segmentation, and classification. Given the intricate nature of brain anatomy and tumor variability, the study aims to enhance the accuracy and efficiency of brain tumor diagnosis using advanced imaging and processing techniques. Methods: The framework utilizes Brain Tumor Segmentation (BraTS) 2019 and 2020 datasets, applying an AMW-PM filter to enhance image quality. Tumor segmentation employs a slimmable transformer with hybrid axial attention for precision. Classification uses similarity navigated graph neural networks (SNGNNs), optimized with the crayfish algorithm (CFO) to minimize loss and enhance performance. Results: The framework’s effectiveness was validated using key performance metrics, showing significant improvements in tumor detection and classification. The combination of advanced segmentation techniques and the SNGNN classifier, optimized by the CFO, led to a fivefold increase in diagnostic accuracy. These improvements demonstrated a substantial enhancement in the model's ability to correctly identify tumor types and provide reliable outputs for clinical use. Conclusion: The proposed framework delivers a robust solution for brain tumor analysis, integrating cutting-edge techniques for image processing, segmentation, and classification. The results not only highlight improvements in diagnostic accuracy but also contribute to better clinical decision-making in managing brain tumors. This framework sets the foundation for future advancements in brain tumor diagnostics. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Beyond p-y method:A review of artificial intelligence approaches for predicting lateral capacity of drilled shafts in clayey soils

引用

Journal of Rock Mechanics and Geotechnical engineering 2024年第9期16卷 3812-3840页

作者： M.E.Al-Atroush A.E.Aboelela Ezz El-Din Hemdan Civil and Environmental Engineering Program Engineering Management DepartmentCollege of EngineeringPrince Sultan UniversityRiyadhSaudi Arabia Department of Structural Engineering Faculty of EngineeringAin Shams UniversityCairoEgypt Structure and Materials Research Lab Prince Sultan UniversityRiyadhSaudi Arabia Computer Science and Engineering Department Faculty of Electronic EngineeringMenoufia UniversityMenoufiaEgypt

In 2023,pivotal advancements in artificial intelligence(AI)have significantly *** that in mind,traditional methodologies,notably the p-y approach,have struggled to accurately model the complex,nonlinear soil-structure interactions of laterally loaded large-diameter drilled *** study undertakes a rigorous evaluation of machine learning(ML)and deep learning(DL)techniques,offering a comprehensive review of their application in addressing this geotechnical challenge.A thorough review and comparative analysis have been carried out to investigate various AI models such as artificial neural networks(ANNs),relevance vector machines(RVMs),and least squares support vector machines(LSSVMs).It was found that despite ML approaches outperforming classic methods in predicting the lateral behavior of piles,their‘black box'nature and reliance only on a data-driven approach made their results showcase statistical robustness rather than clear geotechnical insights,a fact underscored by the mathematical equations derived from these ***,the research identified a gap in the availability of drilled shaft datasets,limiting the extendibility of current findings to large-diameter *** extensive dataset,compiled from a series of lateral loading tests on free-head drilled shaft with varying properties and geometries,was introduced to bridge this *** paper concluded with a direction for future research,proposes the integration of physics-informed neural networks(PINNs),combining data-driven models with fundamental geotechnical principles to improve both the interpretability and predictive accuracy of AI applications in geotechnical engineering,marking a novel contribution to the field.

关键词： Laterally loaded drilled shaft load transfer and failure mechanisms Physics-informed neural networks(PINNs) P-y curves Artificial intelligence(AI) Dataset

来源：评论

学校读者我要写书评

暂无评论

Deep neural network for emojis-based sentiment classification

引用

International Journal of Information Technology (Singapore) 2025年 1-8页

作者： Balaji, Prasanalakshmi Alsid, Linda Elzubir Gasm Sagar, Kalpna Prakasam, Shanmugapriya Department of Computer Science College of Computer Science King Khalid University Abha Saudi Arabia Department of Computer Science KIET Group of Institutions Delhi-NCR Ghaziabad India Department of Computer Science and Business Systems Rajalakshmi Engineering College Thandalam India

Sentiment classification using emojis on social media has become increasingly crucial in recent years. Social media commonly uses emojis to convey feelings, emotions, and moods. Hence, in this article, a Jellyfish Algorithm based on a Deep Convolution Neural Network (JA-DCNN) is developed for the sentiment classification of the emojis. Initially, the emoji images are collected from the open-source system. The proposed sentiment classification process uses a Jellyfish Algorithm (JA) optimized Deep Convolution Neural Network (DCNN). In DCNN, the hyperparameters are selected with the help of JA to improve accuracy and efficiency. The proposed method aims to improve the training features, maintain the resolution, reduce memory consumption, and improve the system’s accuracy. The proposed method decreases the computation cost and memory consumption, which empowers the system’s performance in emojis-based sentiment classification. The proposed method is implemented in MATLAB, and performance metrics of similarity and quality measurements evaluate performances. To validate the performance of the proposed methodology, it is compared with conventional methods such as MobilenetV2, ResNet18, and ResNet50, which have been proven to obtain better performance metrics. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Classification Deep convolution neural network Emojis Jellyfish algorithm Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Exploring Innovation: Artificial Intelligence (AI) in Pharmacy 7

Exploring Innovation: Artificial Intelligence (AI) in Pharma...

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Raymond, David Mankar, Divyani Gudadhe, Amit Faculty of Engineering And Technology Department of Computer Science And Medical Engineering Maharashtra Wardha India Faculty of Engineering And Technology Department of Basic Science And Humanities Maharashtra Wardha India

ISBN: (纸本)9798350359299

Artificial Intelligence (AI) has emerged as a disruptive force in the pharmaceutical industry, driving unprecedented breakthroughs across various domains. This article provides a comprehensive overview of the cutting-edge applications of AI in pharmacy, spanning medication safety, clinical decision support, personalized medicine, and drug discovery. AI is revolutionizing drug development processes, enabling precision pharmacy techniques, and enhancing clinical decision-making through Machine Learning, Deep Learning, and Natural Language Processing. Despite the potential for transformation, challenges such as moral dilemmas and legal restrictions persist. Nevertheless, the integration of AI will revolutionize pharmacy practice, leading to improved patient outcome and ushering in a new era of healthcare. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Hybrid Optimisation with Black Hole Algorithm for Improving Network Lifespan

引用

Intelligent Automation & Soft Computing 2023年第2期35卷 1873-1887页

作者： S.Siamala Devi Chandrakala Kuruba Yunyoung Nam Mohamed Abouhawwash Department of Computer Science and Engineering Sri Krishna College of TechnologyCoimbatore641042India Department of Computer Science and Engineering Vignan Nirula Institute of Technology and ScienceGuntur522009India Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Wireless sensor networks(WSNs)are projected to have a wide range of applications in the *** fundamental problem with WSN is that it has aﬁnite *** a network is a common strategy for increasing the life-time of WSNs and,as a result,allowing for faster data *** cluster-ing algorithm’s goal is to select the best cluster head(CH).In the existing system,Hybrid grey wolf sunﬂower optimization algorithm(HGWSFO)and optimal clus-ter head selection method is *** does not provide better competence and out-put in the ***,the proposed Hybrid Grey Wolf Ant Colony Optimisation(HGWACO)algorithm is used for reducing the energy utilization and enhances the lifespan of the *** hole method is used for selecting the cluster heads(CHs).The ant colony optimization(ACO)technique is used toﬁnd the route among origin CH and *** open cache of nodes,trans-mission power,and proximity are used to improve the CH *** grey wolf optimisation(GWO)technique is the most recent and well-known optimiser module which deals with grey wolves’hunting activity(GWs).These GWs have the ability to track down and encircle *** GWO method was inspired by this hunting *** proposed HGWACO improves the duration of the net-work,minimizes the power consumption,also it works with the large-scale *** HGWACO method achieves 25.64%of residual energy,25.64%of alive nodes,40.65%of dead nodes also it enhances the lifetime of the network.

关键词： Energy efﬁciency power consumption lifespan of the network black hole method ant colony optimisation routing and cluster heads(CHs)

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Enabled Novel Real-Time IoT Targeted DoS/DDoS Cyber Attack Detection System

引用

computers, Materials & Continua 2024年第9期80卷 3655-3683页

作者： Abdullah Alabdulatif Navod Neranjan Thilakarathne Mohamed Aashiq Department of Cybersecurity College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of ICT Faculty of TechnologyUniversity of ColomboColombo00700Sri Lanka Department of Computer Science and Engineering Faculty of EngineeringSouth Eastern University of Sri LankaOluvil32360Sri Lanka

The increasing prevalence of Internet of Things(IoT)devices has introduced a new phase of connectivity in recent years and,concurrently,has opened the floodgates for growing cyber *** the myriad of potential attacks,Denial of Service(DoS)attacks and Distributed Denial of Service(DDoS)attacks remain a dominant concern due to their capability to render services inoperable by overwhelming systems with an influx of *** IoT devices often lack the inherent security measures found in more mature computing platforms,the need for robust DoS/DDoS detection systems tailored to IoT is paramount for the sustainable development of every domain that IoT *** this study,we investigate the effectiveness of three machine learning(ML)algorithms:extreme gradient boosting(XGB),multilayer perceptron(MLP)and random forest(RF),for the detection of IoTtargeted DoS/DDoS attacks and three feature engineering methods that have not been used in the existing stateof-the-art,and then employed the best performing algorithm to design a prototype of a novel real-time system towards detection of such DoS/DDoS *** CICIoT2023 dataset was derived from the latest real-world IoT traffic,incorporates both benign and malicious network traffic patterns and after data preprocessing and feature engineering,the data was fed into our models for both training and validation,where findings suggest that while all threemodels exhibit commendable accuracy in detectingDoS/DDoS attacks,the use of particle swarmoptimization(PSO)for feature selection has made great improvements in the performance(accuracy,precsion recall and F1-score of 99.93%for XGB)of the ML models and their execution time(491.023 sceonds for XGB)compared to recursive feature elimination(RFE)and randomforest feature importance(RFI)*** proposed real-time system for DoS/DDoS attack detection entails the implementation of an platform capable of effectively processing and analyzing network traffic in *** inv

关键词： Machine learning Internet of Things(IoT),DoS,DDoS cybersecurity intrusion prevention network security,feature optimization,sustainability

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：