检索结果-内蒙古大学图书馆

REAL-TIME GLUCOSE MONITORING system AND DIETARY RECOMMENDATIONS FOR DIABETES MELLITUS USING FOG COMPUTING

Telecommunications and Radio Engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 55-67页

作者： Sathish, N. Elangovan, D. Nagalakshmi, R. Suresh, G. Chitra, Devi D. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Saveetha Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Artificial Intelligence and Data Science Kings Engineering College Sriperumbudur India Department of Computer Science And Engineering S.A. Engineering College Chennai India

This systematic review gave special attention to diabetes and the advancements in food and nutrition needed to prevent or manage diabetes in all its forms. There are two main forms of diabetes mellitus: Type 1 (T1D) and Type 2 (T2D). The loss of beta cells brought on by an autoimmune reaction causes the pancreas to be unable to produce enough insulin, which leads to Type 1 diabetes. A deficiency of insulin could arise as the illness worsens. The most frequent cause is a combination of excessive body weight and inadequate exercise. Currently, 90–95% of patients have Type 2 diabetes mellitus (T2DM). Age-specific normal blood sugar ranges exist and knowing them is crucial for determining a target blood sugar level that is both healthy and appropriate. The main takeaway is that diet regimens ought to be tailored to the patient’s requirements and should account for their capacity for change. Carbohydrates are the nutrients that have the biggest impact on the rise in blood glucose. In this paper, we have proposed a novel method for glucose monitoring and diet recommendations for diabetic patients using fog computing. A paradigm known as fog computing makes use of the benefits that come from using both cloud and edge devices to support modern computing systems by offering high-quality services, cutting latency, facilitating mobility, supporting multi-tenancy, and many other features. © 2025 by Begell House, Inc.

关键词： Fog computing

来源：评论

学校读者我要写书评

暂无评论

Target Group Influence Maximization using Reinforcement Learning Approach 10

Target Group Influence Maximization using Reinforcement Lear...

引用

10th International Conference on Web Research, ICWR 2024

作者： Binesh, Neda Haddadi, Mahdieh Arazkhani, Niloofar Semnan University Faculty of Mathematics Statistics and Computer Science Semnan Iran Department of Computer Engineering & Information Technology Tehran Iran

ISBN: (纸本)9798350394986

In social networks groups play a crucial role and making decisions based on majority consensus. Which influencer nodes should we select if our goal is to broadcast a subject in a target group and increase the number of active nodes in this group? Here, we study a new influence maximization (IM) problem that focuses on individuals in a target group who are activated by some relevant topic or information. Target Group Influence Maximization (TGIM) aims to select k influencer nodes in such a way that the number of activated nodes in the target group is maximized. In this paper, we study TGIM and focus on activating the majority of nodes in the target group. We propose an algorithm named Reinforcement Learning for Target Group (RLTG) based on the analysis of the influence of nodes on the target group. The algorithm uses the reinforcement learning approach to learn the optimal path from each target node to some candidate influencers. The experimental results indicate that the recommended approach outperforms known methods. © 2024 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Automated Autism Spectral Disorder Classification Using Optimal Machine Learning Model

引用

computers, Materials & Continua 2023年第3期74卷 5251-5265页

作者： Hanan Abdullah Mengash Hamed Alqahtani Mohammed Maray Mohamed K.Nour Radwa Marzouk Mohammed Abdullah Al-Hagery Heba Mohsen Mesfer Al Duhayyim Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial Intelligence and Unit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Autism Spectrum Disorder (ASD) refers to a neuro-disorder wherean individual has long-lasting effects on communication and interaction *** information technologywhich employs artificial intelligence(AI) model has assisted in early identify ASD by using pattern *** advances of AI models assist in the automated identification andclassification of ASD, which helps to reduce the severity of the *** study introduces an automated ASD classification using owl searchalgorithm with machine learning (ASDC-OSAML) model. The proposedASDC-OSAML model majorly focuses on the identification and classificationof ASD. To attain this, the presentedASDC-OSAML model follows minmaxnormalization approach as a pre-processing stage. Next, the owl searchalgorithm (OSA)-based feature selection (OSA-FS) model is used to derivefeature subsets. Then, beetle swarm antenna search (BSAS) algorithm withIterative Dichotomiser 3 (ID3) classification method was implied for ASDdetection and classification. The design of BSAS algorithm helps to determinethe parameter values of the ID3 classifier. The performance analysis of theASDC-OSAML model is performed using benchmark dataset. An extensivecomparison study highlighted the supremacy of the ASDC-OSAML modelover recent state of art approaches.

关键词： Autism spectral disorder machine learning owl search algorithm feature selection id3 classifier

来源：评论

学校读者我要写书评

暂无评论

Optimized Two-Level Ensemble Model for Predicting the Parameters of Metamaterial Antenna

引用

computers, Materials & Continua 2022年第10期73卷 917-933页

作者： Abdelaziz A.Abdelhamid Sultan R.Alotaibi Department of Computer Science College of Computing and Information TechnologyShaqra UniversitySaudi Arabia Department of Computer Science College of Science and HumanitiesShaqra UniversitySaudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityEgypt

Employing machine learning techniques in predicting the parameters of metamaterial antennas has a significant impact on the reduction of the time needed to design an antenna with optimal parameters using simulation *** this paper,we propose a new approach for predicting the bandwidth of metamaterial antenna using a novel ensemble *** proposed ensemble model is composed of two levels of regression *** first level consists of three strong models namely,random forest,support vector regression,and light gradient boosting *** the second level is based on the ElasticNet regression model,which receives the prediction results from the models in the first level for refinement and producing the final optimal *** achieve the best performance of these regression models,the advanced squirrel search optimization algorithm(ASSOA)is utilized to search for the optimal set of hyper-parameters of each *** results show that the proposed two-level ensemble model could achieve a robust prediction of the bandwidth of metamaterial antenna when compared with the recently published ensemble models based on the same publicly available benchmark *** findings indicate that the proposed approach results in root mean square error(RMSE)of(0.013),mean absolute error(MAE)of(0.004),and mean bias error(MBE)of(0.0017).These results are superior to the other competing ensemble models and can predict the antenna bandwidth more accurately.

关键词： Ensemble model parameter prediction metamaterial antenna machine learning model optimization

来源：评论

学校读者我要写书评

暂无评论

A Review on the Use of Smart Agriculture systems to Protect Crops 3

A Review on the Use of Smart Agriculture Systems to Protect ...

引用

3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Kumar, Gulshan Gourshettiwar, Palash Suman, Saurabh Kumar, Shravan Kumar, Dhiraj Raushan, Rahul Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331509675

Smart agriculture systems leverage the possibilities offered by cutting-edge technologies such as IoT, AI, and remote sensing to revolutionize conventional farming by enhancing resource utilization, production, and crop damage mitigation. Real-time monitoring of soil and crop health, predictive analytics, pest control, and precision irrigation measures are all enabled by these systems. They are able to address major Indian agriculture issues, consequently boosting yield and profitability and promoting environmental sustainability. The largescale deployment of intelligent agriculture systems will change the agriculture landscape in India and will assure long-term food security for an ever-growing population. Challenges include adequate research and future studies in order to better install and achieve smart agricultural systems to protect crops. Intelligent agriculture involves all advanced research, including science and innovations, in national development through space technologies to enhance soil quality, conserve water, and facilitate agriculture information. Space ventures will undergo improved modernization through the introduction of crop sprayers, precision gene editors, epigenetics, big data analytics, IoT, wind and photovoltaic smart energy, AI-enabled robotic applications, and wide-scale desalination technologies. Implementing digital farming systems in developing economies will help their sectors as 85 percent of the global population is set to live in developing countries by 2030. Automation will prove to be necessary since food scarcity is on the rise along with resource wastage. Control strategies such as the IoT, aerial imagery, machine learning, and artificial intelligence will boost production and prevent soil degradation. These advanced technologies are also able to alleviate such issues as plant disease detection, pesticide management, and water application. The introduction of the Internet of Things in the agricultural research world has started

关键词： Smart agriculture

来源：评论

学校读者我要写书评

暂无评论

The Application of Artificial Intelligence in Alzheimer’s Research

引用

Tsinghua science and technology 2024年第1期29卷 13-33页

作者： Qing Zhao Hanrui Xu Jianqiang Li Faheem Akhtar Rajput Liyan Qiao Faculty of Information Technology Beijing University of TechnologyBeijing 100124China School of Medicine Tsinghua UniversityBeijing 100084China Department of Computer Science Sukkur IBA UniversitySukkur 65200Pakistan Department of Neurology Tsinghua University Yuquan HospitalBeijing 100040China

Alzheimer's disease(AD)is an irreversible and neurodegenerative disease that slowly impairs memory and neurocognitive function,but the etiology of AD is still *** the explosive growth of electronic health data,the application of artificial inteligence(Al)in the healthcare setting provides excellent potential for exploring etiology and personalized treatment approaches,and improving the disease's diagnostic and prognostic *** paper first briefly introduces Al technologies and applications in medicine,and then presents a comprehensive review of Al in *** simple,it includes etiology discovery based on genetic data,computer-aided diagnosis(CAD),computer-aided prognosis(CAP)of AD using multi-modality data(genetic,neuroimaging and linguistic data),and pharmacological or non-pharmacological approaches for treating ***,some popular publicly available AD datasets are introduced,which are important for advancing Al technologies in AD ***,core research challenges and future research directions are discussed.

关键词： Alzheimer's disease artificial intelligence etiology discovery computer-aided diagnosis computer-aided prognosis treatment

来源：评论

学校读者我要写书评

暂无评论

Modified Bat Algorithm: a newly proposed approach for solving complex and real-world problems

引用

Soft Computing 2024年第13-14期28卷 7983-7998页

作者： Umar, Shahla U. Rashid, Tarik A. Ahmed, Aram M. Hassan, Bryar A. Baker, Mohammed Rashad Department of Software College of Computer Science and Information Technology University of Kirkuk Kirkuk Iraq Computer Science and Engineering Department School of Science and Engineering University of Kurdistan Hewler KRI Erbil Iraq Department of Computer Science College of Science Charmo University Sulaimani Chamchamal46023 Iraq

Bat Algorithm (BA) is a nature-inspired metaheuristic search algorithm designed to efficiently explore complex problem spaces and find near-optimal solutions. The algorithm is inspired by the echolocation behavior of bats, which acts as a signal system to estimate the distance and hunt prey. Although the BA has proven effective for various optimization problems, it exhibits limited exploration ability and susceptibility to local optima. The algorithm updates velocities and positions based on the current global best solution, causing all agents to converge toward a specific location, potentially leading to local optima issues in optimization problems. On this premise, this paper proposes the Modified Bat Algorithm (MBA) as an enhancement to address the local optima limitation observed in the original BA. MBA incorporates the frequency and velocity of the current best solution, enhancing convergence speed to the optimal solution and preventing local optima entrapment. While the original BA faces diversity issues, both the original BA and MBA are introduced. To assess MBA’s performance, three sets of test functions (classical benchmark functions, CEC2005, and CEC2019) are employed, with results compared to those of the original BA, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Dragonfly Algorithm (DA). The outcomes demonstrate the MBA’s significant superiority over other algorithms. In addition, MBA successfully addresses a real-world assignment problem (call center problem), traditionally solved using linear programming methods, with satisfactory results. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Swarm intelligence

来源：评论

学校读者我要写书评

暂无评论

Explainable AI-Based DDoS Attacks Classification Using Deep Transfer Learning

引用

computers, Materials & Continua 2024年第9期80卷 3785-3802页

作者： Ahmad Alzu’bi Amjad Albashayreh Abdelrahman Abuarqoub Mai A.M.Alfawair Department of Computer Science Jordan University of Science and TechnologyIrbid22110Jordan Department of Computer Science The University of JordanAmman11942Jordan Cardiff School of Technologies Cardiff Metropolitan UniversityCardiffCF52YBUK Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied UniversitySalt19117Jordan

In the era of the Internet of Things(IoT),the proliferation of connected devices has raised security concerns,increasing the risk of intrusions into diverse *** the convenience and efficiency offered by IoT technology,the growing number of IoT devices escalates the likelihood of attacks,emphasizing the need for robust security tools to automatically detect and explain *** paper introduces a deep learning methodology for detecting and classifying distributed denial of service(DDoS)attacks,addressing a significant security concern within IoT *** effective procedure of deep transfer learning is applied to utilize deep learning backbones,which is then evaluated on two benchmarking datasets of DDoS attacks in terms of accuracy and time *** leveraging several deep architectures,the study conducts thorough binary and multiclass experiments,each varying in the complexity of classifying attack types and demonstrating real-world ***,this study employs an explainable artificial intelligence(XAI)AI technique to elucidate the contribution of extracted features in the process of attack *** experimental results demonstrate the effectiveness of the proposed method,achieving a recall of 99.39%by the XAI bidirectional long short-term memory(XAI-BiLSTM)model.

关键词： DDoS attack classification deep learning explainable AI cybersecurity

来源：评论

学校读者我要写书评

暂无评论

IRS-Enabled Interference Elimination and Fairness Enhancement in D2D Communication Networks

IRS-Enabled Interference Elimination and Fairness Enhancemen...

引用

2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Dai, Xin Chen, Xin Jiao, Libo Han, Bingjie Pan, Yizheng Yin, Tong School of Computer Science Beijing Information Science & Technology University Beijing China Beijing University of Technology Faculty of Information Technology Beijing China

ISBN: (纸本)9781665410205

In order to cope with the increasing data traffic, we try to enable Intelligent Reflecting Surface (IRS) interference elimination in Device-to-Device (D2D) communication networks to improve the Signal Interference Noise Ratio (SINR). We build the system model and divide the original problem into two subproblems: IRS reflection parameter adjustment and IRS allocation. We use the the Cross Entropy Global Optimization Method (CEGOM) to solve the first subproblem. For the second subproblem, in order to ensure the fairness of user rates and avoid user starvation, we propose a heuristic algorithm based on the Max-Min Fairness Method (MMFM) to solve the problem. Simulation results demonstrate the superiority of the proposed algorithms, which improves Jain's fairness index by 70%, 72% and 13% and reduces the blocking probability by 92%, 90% and 82%, respectively, when compared to the random, shortest distance, and traditional MMFM strategies. © 2024 IEEE.

关键词： Heuristic algorithms

来源：评论

学校读者我要写书评

暂无评论

Enhancing Image Security with Rossler Attractor-Based Encryption 2

Enhancing Image Security with Rossler Attractor-Based Encryp...

引用

2nd International Conference on Cyber Resilience, ICCR 2024

作者： Al-Najjar, Hazem Al-Rousan, Nadia University Of Petra Faculty Of Information Technology Department Of Computer Science Amman Jordan German Jordanian University Faculty Of Electrical Engineering And Information Systems Department Of Computer Engineering Jordan

ISBN: (纸本)9798350394962

we introduced image encryption algorithms with high sensitivity, such that even a single alteration in a plain-text image would result in a complete transformation of the ciphered image. The first algorithm employed pixel values in a substitution approach, whereas the second algorithm utilized pixel values to generate chaotic function keys. Additionally, we employed a Rossler chaotic function with pixel data to generate chaotic values for the shuffling and substitution processes. After subjecting these algorithms to rigorous testing using standard metrics, our experiments revealed the efficiency of the algorithms in image encryption. Notably, even a minute change in the original image resulted in a vastly different ciphered image. These proposed algorithms underscored the effectiveness of integrating chaotic functions with pixel information, significantly enhancing image security. © 2024 IEEE.

关键词： Cryptography

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：