检索结果-内蒙古大学图书馆

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Baidya, Bodhisattwa Binu, P.K. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Applications Amritapuri India

ISBN: (纸本)9798350335095

Many applications of unmanned aerial vehicles (UAVs) has gained significant attention in recent years. However, UAVs need a reliable communication infrastructure to communicate with other devices and share information. Software-defined networking (SDN) is a promising solution for managing communication in UAV networks. In this paper, we proposed a path selection algorithm to efficiently select paths for UAV communication in a software-defined UAV network. Our algorithm utilizes the OpenFlow protocol and the STP Protocol to select the most efficient path based on various network metrics such as delay, throughput, and link quality. We also implemented our algorithm in the OMNeT++ simulation environment and evaluated its performance. Our simulation results showed that our algorithm can select the most efficient path for UAV communication in the network, reducing delay and increasing throughput. © 2023 IEEE.

关键词： Antennas

来源：评论

学校读者我要写书评

暂无评论

A Paradigm Shift in Real-Time Detection: The YOLOv7 Approach to Trainable Freebie Optimization 3

A Paradigm Shift in Real-Time Detection: The YOLOv7 Approach...

引用

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

作者： Vinitha, A. Jeevitha, S. Kumar, N.B. Mahesh Sathish Kumar, L. Saravanan, D. Karpagam Academy of Higher Education Department of Computer Applications Tamilnadu Coimbatore India Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil Tamilnadu Virudhunagar India Hindusthan Institute of Technology Computer Science and Engineering Tamil Nadu Coimbatore India Madhya Pradesh Sehore India

ISBN: (纸本)9798331509675

Significant progress has been made in real-time object detection, with YOLOv7 emerging as a state-of-the-art model that achieves the highest levels of accuracy and speed. This study combines Bag-of-Freebies (BoF) strategies with YOLOv7 to improve performance without incurring extra computational expenses. Our tests show that adding BoF preserves real-time inference capabilities while increasing mAP@0.5 by 3.5%. The accuracy and FPS of YOLOv7 with BoF are superior to those of the current YOLO-based models (YOLOv4, YOLOv5, YOLOv6). According to the results, YOLOv7 with BoF sets a new standard for real-time object detection by achieving a mAP@0.5 of 55.3% and 83 FPS on the COCO dataset. © 2025 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Hyperspectral Images for Crop Prediction using Machine and Deep Learning Models 3

Hyperspectral Images for Crop Prediction using Machine and D...

引用

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

作者： Mahalakshmi, R. Kumutha, K. Sakthivanitha, M. Lakshmi, R. Bagavathi Sirajudeen, Mohamed Padmanabhan, Sankar Vels Institute of Science Technology & Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Computer Application Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Information Technology Tamil Nadu Chennai India Nilgiri College of Arts and Science Pg and Research Department of Computer Science Tami Nadu The Nilgiris India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

Crop classification is essential for local and national governments to make informed agricultural decisions. Remote sensing technology has made it possible to employ high-resolution hyperspectral images (HSIs) for land cover classification for decades. Machine Learning (ML) and Deep Learning (DL) are becoming more popular for HSI categorization due to advances in processing capacity and technology. HSIs can accurately identify crop types due to their narrow and continuous spectral band reflection. This study compares traditional machine learning (ML) models like Support Vector Machines (SVM) and Random Forest (RF) to deep learning (DL) models like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for hyperspectral image classification (HSI). The HSI categorization was evaluated using performance criteria such as accuracy, precision, recall, and the F1-Score. CNN achieved the highest accuracy of 96%, demonstrating that it is the most reliable solution for complex HSI classification tasks because it successfully captures spatial and spectral data. © 2025 IEEE.

关键词： Classification Deep Learning(DL) Hyperspectral Images(HSI) Machine Learning(ML)

来源：评论

学校读者我要写书评

暂无评论

Mediation analysis in recursive systems of distributed-lag linear regressions

引用

Quality and Quantity 2022年第3期56卷 1535-1555页

作者： Magrini, Alessandro Department of Statistics Computer Science Applications University of Florence Florence Italy

Recursive systems of linear regressions is a consolidated methodology for mediation analysis, allowing to determine causal effects of interest in a closed form based on the regression coefficients. In a dynamic perspective, distributed-lags can be added to each regression in order to represent causal effects persisting over several periods. However, mediation analysis in the dynamic case is challenging, because causal effects depend on the time lag, and a general procedure to compute their lag distribution based on the regression coefficients is currently missing. In this paper, we formalize the rules to perform mediation analysis in recursive systems of distributed-lag linear regressions, here called Distributed-lag Linear Recursive Models (DLRMs). Firstly, mediation analysis is based on the Directed Acyclic Graph (DAG) representation of the DLRM, then a DAG-free algorithm is proposed to improve computational efficiency. Our DAG-free algorithm is applied to a DLRM representing the impact pathways of agricultural research expenditure towards poverty reduction in rural areas. © 2021, The Author(s).

关键词： Dynamic causal effects Impact pathways Lag attribution Linear Markovian models Multivariate time series

来源：评论

学校读者我要写书评

暂无评论

A Novel Hybrid Genetic Algorithm for Deadline Based Cost Optimization in Cloud Environment 4

A Novel Hybrid Genetic Algorithm for Deadline Based Cost Opt...

引用

4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Bothra, Sandeep Kumar Bhatt, Devershi Pallavi Sharma, Amit Kumar S. S. Jain Subodh P.G. College Department of Computer Science Jaipur India Manipal University Jaipur Department of Computer Applications Jaipur India Manipal University Jaipur Department of Computer and Communication Engineering Jaipur India

ISBN: (纸本)9798331529635

Cloud computing is a well-established paradigm that is gaining popularity due to its high performance, elastic, and scalable computing resources over the internet. These features make it an it an appropriate platform to schedule complex scientific workflows over it. Workflow scheduling is considered NP-hard in nature. Scientists face a challenge when scheduling scientific workflow in a cloud due to its security, heterogeneity, and dynamic nature. Although there have been many studies on workflow scheduling in relation to grid and cluster computing throughout the years, not many of them have been specifically designed for the cloud. Furthermore, the earlier research did not take into account the fundamentals of cloud infrastructure, like varying performance of Virtual Machine (VM), acquire delay, virtual machine (VM) startup and shutdown times, bandwidth of channel, communication delay, and other QoS parameters. To fulfill the deadline and minimize the total computation cost, this research suggests a hybrid model based on Genetic Algorithms (GA) with appropriate heurist techniques. Heuristic techniques aid in producing high-quality seed to improve the performance of GA in its convergence rate. For this, we have applied Predict Earliest Finish Time (PEFT) and Heterogeneous Earliest Finish Time using Task Duplication (HEFT-TD) during the initialization of the population. We have used cutting-edge techniques in the genetic algorithm's crossover and mutation operators. Our hybrid paradigm considered varying performance of virtual machine, acquisition delay, booting time, and shutdown time of VMs. We performed our experiment on the simulator WorkflowSim on four types of scientific workflows. The outcomes show that the recommended approach performs workflow scheduling with the lowest costs and makespan when compared to other algorithms, demonstrating its exceptional superiority. © 2024 IEEE.

关键词： Cloud platforms

来源：评论

学校读者我要写书评

暂无评论

Performance Evaluation of Drones in FANETs using Queueing Model

Performance Evaluation of Drones in FANETs using Queueing Mo...

引用

2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Ghosh, Akash Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar Pandey, Trilok Nath Pattanayak, Binod Kumar De, Utpal Chandra School of Computer Applications Kiit Deemed to Be University Bhubaneswar India School of Computer Science and Engineering Vit Chennai India Iter Soa Deemed to Be University Department of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350313987

When conventional communication routes are unavailable or malfunctioning, mobile adhoc networks (MANETs) are essential for establishing communication. These networks can be easily and cheaply built whenever necessary because they don't require any pre-existing infrastructure. In some extreme situations (such as floods, battle zones, rescue operations, etc.), MANETs cannot be used. Lacking a fixed infrastructure in some situations, flying ad hoc networks (FANETs) can be quite useful for establishing communication. In FANETs, many drones are connected one to another directly or through middle nodes. In this research work, the arrival rate of events during the catastrophe, the drone's service rate, the waiting buffer of the system is considered and various performance measures such as the waiting time of the requests in the waiting buffer, the waiting time of the requests in the system, average queue length and the drones' idle time have all been considered. The ideal number of drones should be used to handle catastrophic occurrences and reduce casualties by adopting the queuing model. © 2023 IEEE.

关键词： Catastrophe Drones Flying ad-hoc networks Performance Measures Queueing Model

来源：评论

学校读者我要写书评

暂无评论

A Proposed Architecture of Mobile Service Provider Based Interactive Systems 8

A Proposed Architecture of Mobile Service Provider Based Int...

引用

8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Magoo, Chandni Singh, Manjeet J.C. Bose University of Science and Technology Department of Computer Science and Engineering Faridabad India Manav Rachna University Department of Computer Science and Technology Faridabad India Department of Computer Science and Applications J.C. Bose University of Science and Technology India

ISBN: (纸本)9798350334012

Chatbots are basically designed to imitate any human interaction to automate interaction and support. Numerous benefits of chatbots has led to its increased usage by different organizations to provide essential assistance to its various customers. The current interactive systems are mainly developed from two main concepts namely NLP and Machine Learning. Despite of this, there are certain challenges and limitations of current chatbots. In this paper we will review some of the current chatbots with their limitations and highlight some of the errors identified through survey analysis of different chatbots of some companies like Airtel, Vodafone and Jio etc. A survey is done to analyze the chatbots on various metrics. © 2023 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Integrated intelligent IOT forensic framework for data acquisition through open-source tools

引用

International Journal of Information Technology (Singapore) 2022年第6期14卷 3011-3018页

作者： Surange, Geetanjali Khatri, Pallavi Department of Computer Science and Applications ITM University MP Gwalior India

The study presented in this paper presents the need of an integrated intelligent IoT (Internet of Things) forensic framework. As there is increasing penetration of IoT into everyday life of a common man through smart home, smart city, smart industries etc. Thus, continuous escalation of numbers of IoT devices is generating enormous amount of very sensitive and personal data in various formats. Data, being the most important repository for any business, makes such environment susceptible to more attacks than ever to drain, steal or modify sensitive data. Hence IoT systems become prone to more cyber-attacks than other digital resources spanning a large range of attack vectors. Three important artifacts generated in any IoT environment are: memory, storage, ports used for connectivity, which are used as primary resources of data for digital forensic analysis in this study. These components generate network logs, system logs, registry entries which can be used to find out malicious activities done on the device or through the device. Due to its diversity in terms of manufacturing of IoT devices and architecture of IoT systems, the forensic analysis of artifacts collected from various resources spread across all the layers of IoT ecosystem imposes lot of challenges before investigating team. One such challenge being the need of a unified platform for collection of all artifacts from diverse devices at one place. In this work a study of heterogeneous nature of data obtained from various devices w.r.t to ecosystem of a specific IoT device has been carried out, by analysing the types of data that may be generated by an IoT device and may be forensically useful. A conceptual framework has been proposed to carryout forensic data acquisition and analysis using a unified repository of data collected from an IoT Ecosystem. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer applications and Management.

关键词： Digital forensic Fitness tracker forensic IoT forensic IoT forensic framework

来源：评论

学校读者我要写书评

暂无评论

Efficiency beyond Limits: LSTM and k-NN Synergy for Forecasting Energy Needs in Data Centers

Efficiency beyond Limits: LSTM and k-NN Synergy for Forecast...

引用

2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Mogal, Amit K. Sonaje, Vaibhav P. Kumar, C.H.N. Santhosh Devi, V. Chithra Gottumukkala, Prasanthi Satheesh Kumar, D. Sandip University School of Computer Science and Engineering Department of Computer Science & Application Maharashtra Nashik India Sandip University School of Computer Science and Engineering Department of Computer Science & Applications Maharashtra Nashik India Anurag Engineering College Department of CSE Telangana Kodada India PSNA College of Engineering and Technology Tamilnadu Dindigul India Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Hyderabad India Hindusthan College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350361537

The objective of this study is to examine the effectiveness of a hybrid methodology that combines Long Short-Term Memory (LSTM) and k-Nearest Neighbors (k-NN) models in the context of energy prediction within data centres. The objective of this study is to improve the accuracy of predictions and the efficiency of operations by utilising the combined capabilities of these two machine learning approaches. The proposed LSTM-kNN model has superior performance compared to individual LSTM and k-NN models, as evidenced by a thorough assessment on sample data. It achieves a Mean Absolute Error (MAE) of 9.45, Root Mean Square Error (RMSE) of 12.97, and a coefficient of determination (R2) value of 0.89. The importance of model synergy in enhancing the accuracy of energy forecasts is shown by comparative analysis. The results of this study provide a significant contribution to the advancement of energy management methods in data centres. These findings provide vital insights for stakeholders, enabling them to optimise resource allocation, capacity planning, and sustainability initiatives. Proactive energy optimisation measures and cost-effective operational decisions are examples of practical applications. Potential avenues for future study encompass the exploration of supplementary machine learning techniques and the examination of the influence exerted by external factors on patterns of energy usage. In summary, this work highlights the significance of incorporating machine learning methods to tackle intricate issues in energy prediction and control in data centre settings. © 2024 IEEE.

关键词： k-nearest neighbors

来源：评论

学校读者我要写书评

暂无评论

DCBD: An Efficient Design of Dynamic Compromise Black Hole Attack Detection (DCBD) in WSN

DCBD: An Efficient Design of Dynamic Compromise Black Hole A...

引用

2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Shanmugapriya, S. Shanmugapriya, N. Dr. Sns Rajalakshmi College of Arts & Science Department of Computer Science Coimbatore India Dr. Sns Rajalakshmi College of Arts & Science Department of Computer Applications Coimbatore India

ISBN: (纸本)9798350300826

In numerous fields, including environment monitoring, combat surveillance, and other uses, wireless sensor networks are widely employed. The data packets must be forwarded to the base station in each of these applications by a collaboration of the numerous sensor nodes. But it uses up the several capacities of a sensor node, including the battery, the storage, and the computational power. Using a technique called clustering, which groups sensors with similar characteristics under the control of a chosen sensor known as a Cluster Head (CH), wireless sensor networks (WSNs) can communicate more effectively. The Dynamic Compromise Black Hole Attack Detection (DCBD) algorithm is a novel method that this paper introduces to improve the throughput and network longevity of WSNs. The sensor network is segmented into a fair clustering in DCBD. Additionally, the CH selection for each location is determined by the sensor's energy, the distance to neighbors, and the distance to the base station. The performance of DCBD and other techniques have been compared using simulation studies, and the findings show that DCBD meets the design goals in terms of throughput, latency, and packet delivery ratio. © 2023 IEEE.

关键词： Sensor nodes

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：