检索结果-内蒙古大学图书馆

International Journal of Emerging Technologies in Learning 2023年第10期18卷 19-38页

作者： Sankaran, Hariesh K. Shanmugam, Raju Shah, Bhavesh Ahmad, Sultan Abdeljaber, Hikmat A. M. Nazeer, Jabeen Karnavati University Gujrat Gandhinagar India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj Saudi Arabia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan

Peer and self-assessment open opportunities to scale assessments in online classrooms. This article reports our experiences of using AsPeer-peer assessment system, with two iterations of a university online class. We observed that peer grades highly correlated with staff assigned grades. It was recorded that, the peer grade of all student submissions within range of instructor grade averaged to 21.0% and that within the next 2 ranges was 49.0%. We performed three experiments to improve accuracy of peer grading. First, we observed grading bias and introduced a data driven feedback mechanism to inform peers of it. Students aided by feedback were mindful and performed grading with better accuracy. Second, we observed that the rubric lacked efficiency in translating intent to students. Simplified guiding questions improved accuracy in assessment by 89% students. Third, we encouraged peers to provide personalized qualitative feedback along with rating. We provided them with feedback snippets that addressed common issues. 64% students responded that the snippets helped them to critically look at submissions before rating © 2023, International Journal of Emerging Technologies in *** Rights Reserved.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

A Deep Reinforcement Learning Framework for Reducing Energy Consumption of Server Cooling System 6th

A Deep Reinforcement Learning Framework for Reducing Energy ...

引用

6th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Al Munem, Abdullah Hossain, Md. Shafayat Prito, Rizvee Hassan Tuhin, Rashedul Amin Reza, Ahmed Wasif Arefin, Mohammad Shamsul Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh

ISBN: (纸本)9783031503269

Data Centers consume a tremendous amount of energy for cooling the servers. The cooling system of a data center consumes around 40–55% of the total energy consumption. Thus, it is required to reduce the energy consumption of the server cooling system to minimize the electricity cost. In this experiment, a general framework for reinforcement learning agent integrated cooling systems has been proposed to reduce the cooling cost. The proposed model has been trained and evaluated in a simulated environment. A traditional threshold-based cooling system was also evaluated in the same environment to determine the efficiency of the proposed framework. The proposed framework was able to reduce energy consumption by 21% for 36 months compared to the threshold-based cooling system. To develop the proposed model, the Deep Q-Learning algorithm has been used in this experiment. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

Early Detection and Classification of Tomato Leaf Blight Disease Using Deep Neural Networks 2

Early Detection and Classification of Tomato Leaf Blight Dis...

引用

2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Halder, Anangsha Guha Neogi, Subhrendu Dutta, Shibdas Khamaru, Koustov Bhattacharya, Arghya Roy, Shovan The Neotia University Department of Computer Science and Engineering Sarisha India Dcg Data-Core System India Department of Information Technology Kolkata India University of Engineering and Management Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350373110

The world population relies on agricultural plants for food, and illnesses reduce yield, but proper plant disease monitoring can help to resolve such issues. computer vision and machine learning methods can detect plant illnesses early, reducing their negative consequences and overcoming the drawbacks of continual human monitoring. After pre-processing of input images, the target region needs to be isolated. The proposed models analyzes images for detection and classification to identify edges, colors, and textures. This research has agricultural applications for early disease identification. This study examined ResNet, MobileNet, DenseNet, InceptionV3 and modified CNN model on the PlantVillage dataset to classify tomato illnesses. The F1 score, accuracy, sensitivity, and specificity can evaluate models. This paper evaluates these models using a diverse collection of tomato leaf pictures, contributing to automated disease detection systems and may be helpful for improving farm management and sustainability. © 2024 IEEE.

关键词： Fruits

来源：评论

学校读者我要写书评

暂无评论

Multiple Redundant Weighted Clustered Scheme with Dynamic Weights Adjustment for Damaged S-UAV 29

Multiple Redundant Weighted Clustered Scheme with Dynamic We...

引用

29th IEEE International Workshop on computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2024

作者： Khayat, Grace Mavromoustakis, Constandinos X Batalla, Jordi Mongay Pitsillides, Andreas Department of Computer Science Nicosia Cyprus Warsaw University of Technology Warsaw Poland University of Cyprus Department of Computer Science Cyprus Department of Electrical & Electronic Engineering Science South Africa

ISBN: (纸本)9798350377644

Swarm of UAVs (S-UAVs) refers to an assembly of unmanned aerial vehicles (UAVs) working together to accomplish prearranged missions. In emergency scenarios, such as a fire, any UAV is susceptible to damage. Among the routing algorithms that S-UAVs utilize the most frequently is clustering, where UAVs are divided into clusters. Each cluster consists of a cluster head (CH) and cluster members (CM). The selection of the CH is a topic of continuous research because of its crucial significance in inter-cluster communication. We propose a clustered weighted method with dynamic weight modification and redundancy to ensure end-to-end communication despite non-functional CH. Interspace, speed, and performance indicators are combined into a weighted metric that is used to select the CH, redundant CHs, and CMs. The suggested technique optimizes UAV role selection by dynamically and autonomously adjusting the weights. Based on the results of the executed simulation, this is a promising strategy that minimizes data loss in an emergency situation and minimizes delay. © 2024 IEEE.

关键词： Vehicle routing

来源：评论

学校读者我要写书评

暂无评论

Explainable Artificial Intelligence(XAI)Model for Cancer Image Classification

引用

computer Modeling in engineering & sciences 2024年第10期141卷 401-441页

作者： Amit Singhal Krishna Kant Agrawal Angeles Quezada Adrian Rodriguez Aguiñaga Samantha Jiménez Satya Prakash Yadav Department of Computer Science&Engineering Raj Kumar Goel Institute of TechnologyGhaziabad201017India Department of School of Computing Science and Engineering Galgotias UniversityGreater Noida203201India Tecnológico Nacional de México Campus Tijuana Baja California22414México Universidad Autónoma de Baja California TijuanaBaja California22414México Department of Computer Science and Engineering G.LBajaj Institute of Technology and Management(GLBITM)Affiliated to Dr.A.P.J.Abdul Kalam Technical UniversityLucknow201306India School of Computer Science Engineering and Technology(SCSET) Bennett UniversityGreater Noida201310India

The use of Explainable Artificial Intelligence(XAI)models becomes increasingly important for making decisions in smart healthcare *** is to make sure that decisions are based on trustworthy algorithms and that healthcare workers understand the decisions made by these *** models can potentially enhance interpretability and explainability in decision-making processes that rely on artificial ***,the intricate nature of the healthcare field necessitates the utilization of sophisticated models to classify cancer *** research presents an advanced investigation of XAI models to classify cancer *** describes the different levels of explainability and interpretability associated with XAI models and the challenges faced in deploying them in healthcare *** addition,this study proposes a novel framework for cancer image classification that incorporates XAI models with deep learning and advanced medical imaging *** proposed model integrates several techniques,including end-to-end explainable evaluation,rule-based explanation,and useradaptive *** proposed XAI reaches 97.72%accuracy,90.72%precision,93.72%recall,96.72%F1-score,9.55%FDR,9.66%FOR,and 91.18%*** will discuss the potential applications of the proposed XAI models in the smart healthcare *** will help ensure trust and accountability in AI-based decisions,which is essential for achieving a safe and reliable smart healthcare environment.

关键词： Explainable artificial intelligence artificial intelligence XAI healthcare cancer image classification

来源：评论

学校读者我要写书评

暂无评论

Security model to identify block withholding attack in blockchain

Security model to identify block withholding attack in block...

引用

International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Keshta, Ismail Reegu, Faheem Ahmad Ahmad, Adeel Saxena, Archana Chandan, Radha Raman Mahalakshmi, V. Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia Department of Computer Applications Invertis University UP Bareilly India Department of Computer Science and Engineering Shambhunath Institute of Engineering and Technology College Jhalwa Prayagraj India Department of Computer Science College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia

ISBN: (纸本)9781032669663

Block interception attack, also known as block withholding attack, is an attack method in the blockchain. The attacker penetrates the target mining pool for passive mining to destroy the target mining pool. This paper briefly introduces the mining mechanism and the working principle of block interception attack, summarizes several models of block interception attack, studies the attack methods and benefits of the existing block interception attack models, and analyses their attack effects. A block interception attack model is constructed to increase the rate of return while increasing the rate of return. The completed model is verified through simulated mining experiments. The typical and made models are compared and analyzed based on the rate of return. Finally, the applicable environments of different attack models are given based on the experimental results. © 2024 The Author(s).

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

A trust-based clustering approach to form stable clusters in vehicular ad hoc networks

引用

Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16209-16228页

作者： Singh, Dinesh Maurya, Ashish Kumar Ranvijay Yadav, Rama Shankar Department of Computer Science and Engineering Motilal Nehru National Institute of Technology Allahabad Uttar Pradesh Prayagraj211004 India

Over the last two decades, vehicular ad hoc networks (VANETs) have evolved to disseminate real-time traffic information, emergency information, and multimedia data to vehicles on highways and urban roads. Due to vehicles’ high-speed mobility, the topology in VANETs changes dynamically and causes instability in networks. It further degrades the performance of vehicular applications and consumes network resources. Clustering is one of the approaches in VANETs that is utilized to achieve stability. This paper proposes a Trust-based Stable Clustering (TSC) approach to form stable clusters in VANETs. This approach encompasses a significant cluster head election, cluster joining, and cluster leaving algorithms. The cluster head election algorithm forms stable clusters based on several parameters such as vehicles’ trust value, degree, position, remaining travel time, and speed. The cluster joining and cluster leaving algorithms improve the stability of formed clusters by reducing the cluster reformation frequency and selecting alternate cluster heads. The performance of the TSC is compared with the well-known clustering methods on different parameters such as average cluster head duration, number of state changes, and number of cluster formation messages. The results show that the TSC outperforms than the compared approaches. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Vehicular ad hoc networks

来源：评论

学校读者我要写书评

暂无评论

A Recurrent Neural Network for Multimodal Anomaly Detection by Using Spatio-Temporal Audio-Visual Data

引用

computers, Materials & Continua 2024年第11期81卷 2493-2515页

作者： Sameema Tariq Ata-Ur-Rehman Maria Abubakar Waseem Iqbal Hatoon S.Alsagri Yousef A.Alduraywish Haya Abdullah AAlhakbani Department of Electrical Engineering University of Engineering and TechnologyLahore54890Pakistan Department of Electrical Engineering National University of Science and TechnologyNational University of Sciences and TechnologyIslamabad24090Pakistan Department of Business and Computing Ravensbourne University LondonRavensbourne UniversityLondonSE100EWEngland Electrical and Computer Engineering Department College of EngineeringSultan Qaboos UniversityMuscat123Oman College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11673Saudi Arabia

In video surveillance,anomaly detection requires training machine learning models on spatio-temporal video ***,sometimes the video-only data is not sufficient to accurately detect all the abnormal ***,we propose a novel audio-visual spatiotemporal autoencoder specifically designed to detect anomalies for video surveillance by utilizing audio data along with video *** paper presents a competitive approach to a multi-modal recurrent neural network for anomaly detection that combines separate spatial and temporal autoencoders to leverage both spatial and temporal features in audio-visual *** proposed model is trained to produce low reconstruction error for normal data and high error for abnormal data,effectively distinguishing between the two and assigning an anomaly *** is conducted on normal datasets,while testing is performed on both normal and anomalous *** anomaly scores from the models are combined using a late fusion technique,and a deep dense layer model is trained to produce decisive scores indicating whether a sequence is normal or *** model’s performance is evaluated on the University of California,San Diego Pedestrian 2(UCSD PED 2),University of Minnesota(UMN),and Tampere University of technology(TUT)Rare Sound Events datasets using six evaluation *** is compared with state-of-the-art methods depicting a high Area Under Curve(AUC)and a low Equal Error Rate(EER),achieving an(AUC)of 93.1 and an(EER)of 8.1 for the(UCSD)dataset,and an(AUC)of 94.9 and an(EER)of 5.9 for the UMN *** evaluations demonstrate that the joint results from the combined audio-visual model outperform those from separate models,highlighting the competitive advantage of the proposed multi-modal approach.

关键词： Acoustic-visual anomaly detection sequence-to-sequence autoencoder reconstruction error late fusion regularity score

来源：评论

学校读者我要写书评

暂无评论

Price Optimization in Smart Grids through Blockchain in Cloud Computing based on Collocation Game Theory 24

Price Optimization in Smart Grids through Blockchain in Clou...

引用

13th International Conference on Software and computer Applications, ICSCA 2024

作者： Jatoth, Chandrashekar Patil, Sourabh Department Of Information Technology National Institute Of Technology Raipur India Department Of Computer Science And Engineering National Institute Of Technology Hamirpur India

ISBN: (纸本)9798400708329

A smart grid provides two-way information and energy flow between the power providers and consumers. In recent years, massive sensors and advanced metering infrastructure have been deployed, generating huge amounts of data. Cloud computing has been used to handle and compute expensive complex data. Generally, data is shared through a centralized entity, which requires expensive uploading and downloading, putting extra pressure on the wireless networks. Therefore, in the proposed work, a decentralized Blockchain network is considered. This work proposes offloading Blockchain transactions by smart grid environment to cloud resources and process allocation on cloud resources to optimize resource prices with a process collocation game-theoretic approach. We also present simulation results demonstrating optimized prices incurred to cloud users with the proposed solution. © 2024 ACM.

关键词： Game theory

来源：评论

学校读者我要写书评

暂无评论

Secure RFID-Assisted Authentication Protocol for Vehicular Cloud Computing Environment

引用

IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 12528-12537页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Tariq, Tayyaba Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Yunlin Douliu64002 Taiwan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

Vehicular network technology has made substantial advancements in recent years in the field of Intelligent Transportation Systems. Vehicular Cloud Computing (VCC) has emerged as a novel paradigm with a substantial increase in data exchange within Vehicular ad-hoc networks (VANETs). VCC integrates cloud computing, vehicular networking, and Internet of Things (IoT) technologies. It enables Infrastructure-to-Vehicle (I2V), Vehicle-to-Vehicle (V2V), and Vehicle-to-Device (V2D) communication. VCC optimizes vehicle, cloud infrastructure, and IoT resources while addressing significant communication security and vehicle-user privacy challenges. To address these issues, we developed an RFID-based authentication protocol for VCC based on a Henon map using a hash function. In addition, we also incorporated a Physical Unclonable Function (PUF) to resist physical tampering attacks. We validate the protocol's security formally and informally. The formal security analysis is conducted through a widely used RoR model. We use the Scyther simulation tool to verify the proposed protocol's security against various attacks. Moreover, we compare the performance of our protocol with similar existing protocols across important performance parameters such as communication and computation overheads and security attributes. The proposed protocol yields substantial improvements, demonstrating a 40.74% reduction in computation overhead and a 13.03% decrease in communication overhead as compared to related protocols, delivering both enhanced performance and resource efficiency. The analysis demonstrates its capacity to support secure communication in the VCC environment and satisfy desirable security attributes. © 2024 IEEE.

关键词： Authentication

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：