检索结果-内蒙古大学图书馆

11th International Conference on Information Technology, ICIT 2023

作者： Zaqebah, Abdullah Al Almomani, Omar Almomani, Malek Alsaaidah, Adeeb Abu-Shareha, Ahmad Adel Althunibat, Ahmad The World IslamicSciences and Education University Department of Computer Science Amman Jordan The World Islamic Sciences and Education University Department of Information System and Networks Amman Jordan Software Engineering The World Islamic Sciences and Education University Amman Jordan Al-Ahliyya Amman University Department of Networks and Information Security Amman Jordan Al-Ahliyya Amman University Department of Data Science and Artificial Intelligence Amman Jordan Al-Zaytoonah University of Jordan Department of Software Engineering Amman11733 Jordan

ISBN: (纸本)9798350320060

Low Power networks are spreading worldwide, seeking to enable small devices to join wireless networks. This requires a routing mechanism that makes it possible and seamless. This research aims to create a Low Power and Lossy networks (RPL) routing protocol that balances power usage and node dependability. The main emphasis of the algorithm presented here is the choice made by network nodes (Zigbees) regarding which path to deliver the message along. Results for this routing technique seem promising. Utilizing it increased packet transfer reliability while enforcing power conservation. Furthermore, a 10% reduction in the network's overall power consumption was made. © 2023 IEEE.

关键词： Routing protocols

来源：评论

学校读者我要写书评

暂无评论

Classification of Sign language Using VGG16 and VGG19

Classification of Sign language Using VGG16 and VGG19

引用

Innovative Approaches in Smart Technologies (ISAS), International Symposium on

作者： Basel A. Dabwan Fekry A. Olayah Hanan T Halawani Aisha M Mashraqi Yahya A. Abdelrahman Ahlam F. Shamsan Department of Information System AlBaydha University AlBaydha Yemen Department of Information System Najran University Kingdom of Saudi Arabia Department of Computer Science College of Computer Science and Information Systems Najran University Kingdom of Saudi Arabia Department of Information System Applied College Najran University Najran Kingdom of Saudi Arabia

The Translation system (TS) serves as a communication bridge for individuals with disabilities to interact with others in society. As computers play a vital role in our daily lives, advancements in human-computer interaction (HCI) have particularly benefited the deaf and mute community. Our proposed system aims to develop an intelligent translator that facilitates seamless communication between individuals with speech impairments and the general population. This system seeks to establish an effective and efficient means of communication for those with speaking deficiencies and regular individuals. The proposed system aims to convert sign language into written text using pre-trained models of VGG16 and VGG19, we used The Sign Language dataset consists of 27,455 training samples and 7,172 test samples, This model gave 97.5% with VGG16 and 96% with VGG19.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Improved Fruitfly Optimization with Stacked Residual Deep Learning Based Email Classification

引用

Intelligent Automation & Soft Computing 2023年第6期36卷 3139-3155页

作者： Hala J.Alshahrani Khaled Tarmissi Ayman Yafoz Abdullah Mohamed Abdelwahed Motwakel Ishfaq Yaseen Amgad Atta Abdelmageed Mohammad Mahzari Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah22230Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16242Saudi Arabia Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharj16242Saudi Arabia

Applied linguistics means a wide range of actions which include addressing a few language-based problems or solving some language-based *** stay in the leading positions for business as well as personal *** popularity grabs the interest of individuals with malevolent inten-tions—phishing and spam email *** filtering mechanisms were developed incessantly to follow unwanted,malicious content advancement to protect the *** prevailing solutions were focused on phishing email filtering and spam and whereas email labelling and analysis were not fully ***,this study provides a solution related to email message body text automatic classification into phishing and email *** paper presents an Improved Fruitfly Optimization with Stacked Residual Recurrent Neural Network(IFFO-SRRNN)based on Applied Linguistics for Email *** presented IFFO-SRRNN technique examines the intrinsic features of email for the identification of spam *** the preliminary level,the IFFO-SRRNN model follows the email pre-processing stage to make it compatible with further ***,the SRRNN method can be useful in recognizing and classifying spam *** hyperparameters of the SRRNN model need to be effectually tuned,the IFFO algorithm can be utilized as a hyperparameter *** investigate the effectual email classification results of the IFFO-SRDL technique,a series of simulations were taken placed on public datasets,and the comparison outcomes highlight the enhancements of the IFFO-SRDL method over other recent approaches with an accuracy of 98.86%.

关键词： Email classification applied linguistics improved fruitfly optimization deep learning recurrent neural network

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-based Multiuser Physical Layer Communication Without Known Channel

Deep Learning-based Multiuser Physical Layer Communication W...

引用

IEEE Conference on Wireless Communications and Networking

作者： Jiequ Ji Zehui Xiong Kun Zhu Tony Quek Department of Information System and Design Singapore University of Technology Design Singapore College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

With the recent development of deep learning (DL), DL-based autoencoder techniques provide a novel paradigm for end-to-end physical layer optimization. In this paper, we address the dynamic interference in an end-to-end communication system with a multiuser Gaussian interference channel. In this context, the standard constellation is not optimal under high interference conditions. To address this issue, we propose an adaptive learning algorithm for learning and predicting dynamic interference. Note that existing DL-based autoencoders are unable to train end-to-end learning systems by deep learning without a known channel. Thus, we propose a generative adversarial network (GAN)-based training scheme to imitate the real channel. Simulation results show that compared with traditional PSK and QAM modulation schemes, our proposed adaptive learning-based auto encoder can achieve significantly lower block error rate (BLER) in presence of interference. Besides, the BLER performance of our proposed GAN-based training scheme is close to that of the optimal training scheme with known channel on different channel models.

关键词： Training Deep learning Adaptive learning Heuristic algorithms Simulation Modulation Prediction algorithms

来源：评论

学校读者我要写书评

暂无评论

Automated Grading of Breast Cancer Histopathology Images Using Multilayered Autoencoder

引用

computers, Materials & Continua 2022年第5期71卷 3407-3423页

作者： Shakra Mehak M.Usman Ashraf Rabia Zafar Ahmed M.Alghamdi Ahmed S.Alfakeeh Fawaz Alassery Habib Hamam Muhammad Shafiq Knowledge Unit of System and Technology University of Management&TechnologySialkot Campus51310Pakistan Department of Computer Science University of Management and TechnologySialkot Campus51310Pakistan Faculty of Computing and Technology University of Engineering&TechnologyNarowal51610Pakistan College of Computer Science and Engineering University of Jeddah21493Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia Faculty of Engineering Moncton UniversityNBE1A3E9Canada Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Breast cancer(BC)is the most widely recognized cancer in women *** 2018,627,000 women had died of breast cancer(World Health Organization Report 2018).To diagnose BC,the evaluation of tumours is achieved by analysis of histological *** present,the Nottingham Bloom Richardson framework is the least expensive approach used to grade BC *** contemplate three elements,*** count,*** formation,and *** atypia,which is a laborious process that witness’s variations in expert’s ***,some algorithms have been proposed for the detection of mitotic cells,but nuclear atypia in breast cancer histopathology has not received much *** atypia analysis is performed not only to grade BC but also to provide critical information in the discrimination of normal breast,non-invasive breast(usual ductal hyperplasia,atypical ductal hyperplasia)and pre-invasive breast(ductal carcinoma in situ)and invasive breast *** proposed a deep-stacked multi-layer autoencoder ensemble with a softmax layer for the feature extraction and classification *** classification results show the value of the multilayer autoencoder model in the evaluation of nuclear *** proposed method has indicated promising results,making them more fit in breast cancer grading.

关键词： Breast cancer nuclear atypia autoencoder

来源：评论

学校读者我要写书评

暂无评论

Deep learning approaches for multi-modal sensor data analysis and abnormality detection

引用

Measurement: Sensors 2024年 33卷

作者： Jadhav, Santosh Pandurang Srinivas, Angalkuditi Dipak Raghunath, Patil Ramkumar Prabhu, M. Suryawanshi, Jaya Haldorai, Anandakumar Department of Information Technology MVPS ‘KBT College of Engineering Maharashtra Nashik India Department of Computer Science Applications Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Engineering Amrutvahini College of Engineering Maharashtra Sangamner India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Center for Future Networks and Digital Twin Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore641202 India

The information gathered within the structure health monitored (SHM) device would display a range of irregularities mainly a result of sensing defeat, noise disturbance, and different causes. This will greatly impair the structure's security evaluation. This research presents a multipurpose deeper neural network-based data-driven abnormality diagnostic system called SHM. The multipurpose deeper neural networks fuse single-dimensional as well as two-dimensional properties regarding the sensory signals to increase the detecting efficiency. Two separate Convolutional Neural Network, streams within the system are used for obtaining time-frequency characteristics from information collected by sensors (also referred to as two-dimensional-CNN medium) as well as unprocessed one-dimensional characteristics (also referred to as one-dimensional-CNN medium). Following the 2D as well as 1D streams' individual clustering and filtering processes using the sensing information, the two categories of recovered properties have been distorted through single-dimensional matrices that combined within the fusion level. The ideal framework shows the efficacy as well as potential of the suggested approach having a precision percentage of 95.10%. Considering an accurate AI-assisted electronic instrument for evaluating security in structured health management networks, the suggested approach has an exciting period ahead of it. © 2024 The Authors

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Integrated Parallel Chaos driven LiDAR system with High Interference Tolerance

Integrated Parallel Chaos driven LiDAR System with High Inte...

引用

2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Chen, Ruixuan Shen, Bitao Shu, Haowen Chang, Lin Xie, Weiqiang Liao, Wenchao Tao, Zihan Bowers, John Wang, Xingjun State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Frontiers Science Center for Nano-Optoelectronics Peking University Beijing100871 China

We introduce a chaotic microcomb based high interference-tolerance parallel LiDAR. Parallel chaos with record-high noise spectra beyond 7 GHz leads to sub-cm-level ranging accuracy. The intrinsic orthogonality permits... 详细信息

ISBN: (纸本)9781957171258

关键词： Optical radar

来源：评论

学校读者我要写书评

暂无评论

An Explainable Artificial Intelligence Framework for the Predictive Analysis of Hypo and Hyper Thyroidism Using Machine Learning Algorithms

引用

Human-Centric Intelligent systems 2023年第3期3卷 211-231页

作者： Hossain, Md. Bipul Shama, Anika Adhikary, Apurba Raha, Avi Deb Uddin, K. M. Aslam Hossain, Mohammad Amzad Islam, Imtia Murad, Saydul Akbar Munir, Md. Shirajum Bairagi, Anupam Kumar Department of Information and Communication Engineering Noakhali Science and Technology University Noakhali Bangladesh Department of System Engineering University of South Alabama Mobile USA Computer Science and Engineering Discipline Khulna University Khulna Bangladesh Mandy Dental College and Hospital Dhaka Bangladesh School of Computer Science and Computer Engineering The University of Southern Mississippi Hattiesburg USA Department of Electrical and Computer Engineering Old Dominion University Norfolk USA

The thyroid gland is the crucial organ in the human body, secreting two hormones that help to regulate the human body’s metabolism. Thyroid disease is a severe medical complaint that could be developed by high Thyroid Stimulating Hormone (TSH) levels or an infection in the thyroid tissues. Hypothyroidism and hyperthyroidism are two critical conditions caused by insufficient thyroid hormone production and excessive thyroid hormone production, respectively. Machine learning models can be used to precisely process the data generated from different medical sectors and to build a model to predict several diseases. In this paper, we use different machine-learning algorithms to predict hypothyroidism and hyperthyroidism. Moreover, we identified the most significant features, which can be used to detect thyroid diseases more precisely. After completing the pre-processing and feature selection steps, we applied our modified and original data to several classification models to predict thyroidism. We found Random Forest (RF) is giving the maximum evaluation score in all sectors in our dataset, and Naive Bayes is performing very poorly. Moreover selecting the feature by using the feature importance method RF provides the best accuracy of 91.42%, precision of 92%, recall of 92% and F1-score of 92%. Further, by analyzing the characteristics and behavior of the dataset, we identified the most important features (TSH, T3, TT4, and FTI) of the dataset. In terms of accuracy and other performance evaluation criteria, this study could advocate the use of effective classifiers and features backed by machine learning algorithms to detect and diagnose thyroid disease. Finally, we did some explainability analysis of our best classifier to understand the internal black-box of our machine learning model and datasets. This study could further pave the way for the researcher as well as healthcare professionals to analyze thyroid disease in real time applications.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Natural Language Processing Studies in Humancomputer Interaction: A Bibliometric Analysis and Future Research Framework

Natural Language Processing Studies in HumanComputer Interac...

引用

Student Conference on Research and Development (SCOReD)

作者： Farah Syazwani Binti Mohd Rashid Amirrudin Bin Kamsin Nurul Fazmidar Binti Mohd Noor Department of Computer System Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Selangor Malaysia

ISBN: (数字)9798331510077

ISBN: (纸本)9798331510084

This study conducts a bibliometric analysis of Natural Language Processing (NLP) within Human-computer Interaction (HCI) to identify trends, challenges, and future directions. Analyzing 1,710 SCOPUS-indexed documents (1983-2024) using a PRISMA flowchart, the results show that the United States and China are leading contributors. Key developments include emotion recognition, chatbot interfaces, and speech processing, highlighting NLP’s role in user-centered technologies. Despite growing applications, challenges such as reliability and ethical concerns persist. This analysis emphasizes the need for ethical frameworks and technological advancements to address deployment issues and align NLP innovations with the United Nations Sustainable Development Goals (SDGs). By mapping global research trends, this study provides insights into the transformative potential of NLP in HCI for developing inclusive and responsive systems.

关键词： Ethics Technological innovation Flowcharts Emotion recognition Bibliometrics Market research Reliability Sustainable development Speech processing Research and development

来源：评论

学校读者我要写书评

暂无评论

Enhancing Energy Efficiency in WSN Through Multiple Cluster Head Selection and Fuzzy Logic

Enhancing Energy Efficiency in WSN Through Multiple Cluster ...

引用

2024 International Conference on Advances in Modern Age Technologies for Health and Engineering science, AMATHE 2024

作者： Jagadeesh, S. Pandiarajan, P. Suresh Kumarappan, Jayaraman Karthika Karthikayan, P.N. Devaraj, Rajesh Saveetha University Department Of Computer Science Engineering Chennai India Ece P. S R R. College Of Engineering Sivakasi India Birla Institute Of Technology And Science Pilani- Dubai Campus Academic City Dubai United Arab Emirates Panimalar Engineering College Department Of It Chennai India St Joseph's Institute Of Technology Department Of Information Technology Omr Chennai600119 India Controlled Networks Solution Doddathogur Bangalore560100 India

ISBN: (纸本)9798350371567

Recent developments in microelectronics have made possible the widespread use of wireless sensor networking (WSN). Timely and dependable delivery of event data is crucial in WSN applications, allowing for prompt reaction and corrective measures to be implemented. However, there is a communication lag among the sensor terminal and the base station due to the restricted power supply. The time- and energy-saving benefits of WSNs can be further enhanced by constructing a routing algorithm using clustering techniques. However, since the terminals have different power needs, the WSN is the most efficient and effective routing option. In this work, we present a novel routing protocol for WSNs based on the principles of energy efficiency and fuzzy logic: the Fuzzy Based Energy Efficient Multiple Cluster Head Selection Routing Protocol. Clustering nodes and choosing a Cluster Head (CH) from each cluster to relay data to the Cluster Head Leader (CHL) are both steps in the routing process. After then, data is compiled by the cluster leaders and sent to the BS. Fuzzy logic is used for selecting cluster heads and cluster head leaders, and the shortest energy channel is used for data transmission. The Bacterial Foraging Optimization (BFO) is a method used to identify and select the most optimal route between a source node, referred to as CH, and a destination node, referred to as BS (Base Station). This algorithm aims to find the route that minimizes certain criteria, such as distance, energy consumption, or latency. By utilizing various optimization techniques, BFO efficiently determines the best path for data transmission between CH and BS in a network. The simulation results demonstrated that the proposed methodology exhibited superior energy consumption performance. © 2024 IEEE.

关键词： Routing protocols

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：