检索结果-内蒙古大学图书馆

International Conference on Smart Technologies for Sustainable Development 2021, ICSTSD 2021

作者： Majumder, Annwesha Banerjee Gupta, Somsubhra Singh, Dharmpal Dept. of Information Technology JIS College of Engineering Nadia West Bengal Kalyani India Amity University WestBengal Kolkata India Dept. of Computer Science and Engineering JIS College of Engineering Nadia West Bengal Kalyani India

Machine learning contributes into gamut of domains starting from industry automation to healthcare services. It is a field of Artificial Intelligence using which machine can make decision without human intervention. There are many predominant machine learning algorithms which have proven their excellence in the field of regression and classification problem. Machine learning now a day is used in large scale in field of disease prediction. The acceptability of a machine learning based model depends on dataset used for training the model. Analysis of dataset is very important to identify the importance of individual attributes contribute to make decision. In this paper a cardiovascular disease dataset collected from UCI has been analyzed in detail to identify the distribution and impact of them in decision making. © Published under licence by IOP Publishing Ltd.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

A Heart Disease Prediction Model using Merged XGBoost-SVM Classifier and Particle Swarm Optimization

A Heart Disease Prediction Model using Merged XGBoost-SVM Cl...

引用

Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Annwesha Banerjee Majumder Somsubhra Gupta Sourav Majumder Dharmpal Singh Dept. of Information Technology JIS College of Engineering Kalyani India Dept. of Comouter Science Swami Vivekananda University Kolkata India Capgemini Kolkata India Dept. of Computer Science and Engineering JIS College of Engineering Kalyani India

This study aims to develop an advanced heart disease prediction model using machine learning techniques to address the global health challenge of early identification and accurate diagnosis, especially considering the projected deficits in healthcare by 2030. The proposed model integrates two powerful algorithms, XGBoost and Support Vector Machine Classifier, leveraging their complementary strengths to achieve effective classification. To enhance the model's performance, feature selection has been conducted using the LASSO method, which identifies the most relevant features, while outlier removal based on the Z score ensures data quality. StandardScaler has been utilized to standardize the data, mitigating any biases caused by differing scales. Furthermore, Particle Swarm Optimization has been employed to optimize the model's performance by fine-tuning its parameters. The reported accuracy of 91.8% underscores the model's potential to accurately predict heart disease, offering valuable support in prevention efforts. These findings highlight the significance of technological advancements in healthcare, particularly in addressing the growing demand for accurate and timely diagnoses.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhancing Routing Efficiency and Performance in Mobile Ad-Hoc Networks Using Deep Learning Techniques

Enhancing Routing Efficiency and Performance in Mobile Ad-Ho...

引用

Sustainable technology and engineering (i-COSTE), International Conference on

作者： Depavath Harinath Archana Patil G R Ramadevi Madhu Bandi M.V. Ramana Murthy K Sukesh Reddy Dept of Computer Science Ramnath Guljarilal Kedia College of Commerce Hyderabad Telangana India Dept. of Computer Science & Engineering Rishi MS Institute of Engineering and Technology for women Hyderabad Telangana India Dept of Computer Science. TSWRDC(W) Nagar-Telangana India Rancho Cucamonga CA USA Computer Science and Mathematics Osmania university Rancho Cuca Monga California USA Garland Texas USA

ISBN: (数字)9798331517335

ISBN: (纸本)9798331517342

MANET stands for Mobile Ad-hoc Network also called wireless Ad-hoc Network or Ad-hoc Wireless Network. It is a decentralized wireless network consisting of mobile devices (nodes) that communicate with each other without relying on a fixed infrastructure. MANET forms a highly dynamic autonomous topology with the presence of one or multiple different transceivers between modes. MANETs consist of a peerto-peer, self-configuring and self-healing modes. Mobile Ad-hoc Network has wide range of applications such as military and defense operations, healthcare, sensor networks, wireless sensor networks, Internet of Things (IoT) etc. In order to enhance the routing efficiency and performance in Mobile Ad-Hoc Networks (MANETs) this paper proposing different deep learning techniques.

关键词： Deep learning Correlation coefficient Wireless sensor networks Accuracy Wireless networks Predictive models Routing Routing protocols Peer-to-peer computing Topology

来源：评论

学校读者我要写书评

暂无评论

Reinforcement Learning for Language Grounding: Mapping Words to Actions in Human-Robot Interaction 4

Reinforcement Learning for Language Grounding: Mapping Words...

引用

4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Adapa, Srinivasa Rao Gupta, Himanshu Mukherjee, Susmoy Mallupalli, Praneeth Reddy Avhad, Priyanka Upreti, Kamal OpenLogix LLC Software Engineer MI United States Software Engineer Meta NJ United States VIT Institute Electrical and Electronics Engg. Tamil Nadu India Arcesium Hyderabad India Veermata Jijabai Technological Inst. Computer Engineering Mumbai India Christ University Dept. of Computer Science Delhi NCR Campus India

ISBN: (纸本)9798350353266

Within the domain of human-robot communication, effective communication is paramount for seamless and smooth collaboration between humans and robots. A promising method for improving language grounding is reinforcement learning (RL), which enables robots to translate spoken commands into suitable behaviors. This paper presents a comprehensive review of recent advancements in RL techniques applied to the task of language grounding in human-robot interaction, focusing specifically on instruction following. Key challenges in this domain include the ambiguity of natural language, the complexity of action spaces, and the need for robust and interpretable models. Various RL algorithms and architectures tailored for language grounding tasks are discussed, highlighting their strengths and limitations. Furthermore, real-world applications and experimental results are examined, showcasing the effectiveness of RL-based approaches in enabling robots to understand and execute instructions from human users. Finally, promising directions for future research are identified, emphasizing the importance of addressing scalability, generalization, and adaptability in RL-based language grounding systems for human-robot interaction. © 2024 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing Public Transport: RFID-Enabled Occupancy Monitoring and Frequency Analysis System

Revolutionizing Public Transport: RFID-Enabled Occupancy Mon...

引用

International Conference on Communication, Computing and Internet of Things (IC3IoT)

作者： J. Uma Mageswaran G. Indumathi A.V. Kalpana G. Elangovan S.D. Lalitha S. Nalini Mayank Kumar Sohanda Dept. of Computer Science Engineering Amrita School of Computing Vengal Campus Tiruvallur Dist Tamil Nadu SRM Institute of Science and Technology Ramapuram Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Department of Data Science and Business Systems SRM Institute of Science and Technology Department of Computer Science and Engineering R.M.K Engineering College Department of Computing Technologies SRM Institute of Science and Technolog Kattankulathur

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

Public bus stops in India are becoming more crowded due to the country's fast population expansion. People wait a long time for buses to come, then suddenly congregate around them when they do, packing the buses with people and pushing them up into the footboards, which causes accidents. An additional concern of overcrowding is theft. All of this is the result of numerous bus stops not having adequate information on when busses would arrive. The proposed work paves a very important role for the passengers. The passengers will be provided with the information of the arrival of next bus, seat occupancy and the total passengers inside the bus. The information about the seat occupancy, in-passengers and out-passengers is done with the help of Radio Frequency Identification (RFID) tags. The arrival of the next bus is determined by calculating the duration of the buses that are near using RFID readers placed in different bus stations. The count and the frequency will be interfaced in the web application. The passengers can check the application to know the occupancy and today's society where people can catch the right bus at the station and at the right time. This will solve the problems like waiting in bus stops and wasting time and going in a crowded bus.

关键词： Sociology Telecommunication computing Internet of Things Statistics RFID tags Monitoring Accidents

来源：评论

学校读者我要写书评

暂无评论

Streamlining Task Planning Systems for Improved Enactment in Contemporary Computing Surroundings

引用

SN computer science 2024年第8期5卷 993页

作者： Menon, Sindhu Addula, Santosh Reddy Parkavi, A. Subbalakshmi, Ch. Dhandayuthapani, V. Bala Pokkuluri, Kiran Sree Soni, Anita School of Computing and Information Technology REVA University Karnataka Bangalore India Department of Information Technology University of the Cumberlands Williamsburg KY United States Dept of CSE M S Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering Guru Nanak Institution Technical Campus (Autonomous) Ibrahimpatnam Ranga Reddy District Telagana State Hyderabad 501506 India Department of IT College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Campus Oman Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Bhimavaram India CSE Department IES University Bhopal Madhya Pradesh Bhopal India

Task planning algorithms are essential for maximizing efficiency and enhancing performance in modern computing systems, given the escalating demand for computational resources. This paper delves into the effectiveness of various scheduling algorithms across different computing environments, characterized by their unique workload dynamics and resource limitations. This method integrates adaptive algorithms and machine learning to dynamically optimize task planning, enhancing performance and efficiency in cloud, IoT, and distributed computing environments. These algorithms are crucial for optimizing key factors such as task completion times, job prioritization, and resource allocation while also addressing quality of service (QoS), cost, reliability, and specific resource needs. One significant challenge is that many task scheduling approaches do not account for the potential failure of tasks or resources. While some strategies effectively reduce overall completion time (makespan), they can result in severe workload imbalances. To address these issues, our study proposes a new approach that harnesses the processing capabilities of grid systems, thus boosting application performance and throughput. Our algorithm particularly focuses on balancing workload and accelerating scheduling operations, even in the face of grid node failures, by incorporating QoS metrics. This allows for a more robust and adaptable scheduling solution. Comparative analysis with existing methods demonstrates that our algorithm not only improves resource utilization but also significantly diminishes flow time and makespan, confirming its efficacy. Through this research, we contribute to the evolving field of autonomous task scheduling, presenting a solution that responds dynamically to changing environmental conditions and workload demands. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： CPU Flow time Makespan MSHA QoS

来源：评论

学校读者我要写书评

暂无评论

Enhancing Security and Privacy of Healthcare Data Using Hybrid Cryptographic Scheme for Cloud-Based Environment 3

Enhancing Security and Privacy of Healthcare Data Using Hybr...

引用

3rd International Conference on Disruptive Technologies, ICDT 2025

作者： Kumar, Sanjay Kumar, Ajay Abhishek, Kumar Arya, Pradeep Kumar National Institute of Technology Department of Computer Science and Engineering Bihar Patna India Bennett University Dept of Computer Science Engineering and Technology UP Greater Noida India Bennett University Department of Scset UP Greater Noida India

ISBN: (纸本)9798331519582

Hospitals are progressively adopting cloud computing to store and manage patient data, enhancing accessibility and collaboration. The sensitive nature of health information necessitates robust security measures to avert unauthorized access and potential breaches. This study introduces a hybrid methodology that combines the Blowfish algorithm with Elliptic Curve Cryptography (ECC) to protect data during cloud transmission. ECC is used as an asymmetric encryption technique for key exchange, whereas Blowfish serves as a symmetric encryption strategy for data encryption. Combining asymmetric and symmetric cryptography makes sure that security is higher and key management is more efficient. We commend ECC for its exceptional performance and enhanced security. Blowfish is optimal for cloud data encryption because of its swift encryption and decryption rates. It employs various key lengths, rendering it resilient against brute force assaults and safeguarding the secrecy of stored data. Blowfish enables robust encryption mechanisms to protect data during cloud transport from unauthorized access. We assessed the efficacy of this hybrid encryption method by conducting various tests across numerous data clouds. The findings indicate that the suggested hybrid security scheme outperforms the current schemes regarding computing time. © 2025 IEEE.

关键词： Electrotherapeutics

来源：评论

学校读者我要写书评

暂无评论

Enhancing IoT Security Through AI-Based Anomaly Detection and Intrusion Prevention 6

Enhancing IoT Security Through AI-Based Anomaly Detection an...

引用

6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Ramaiah, Narayana Swamy Andrews, S. Kevin Shenbagharaman, A. Gowtham, M.S. Bhaskar, Bandi Tiwari, Mohit Sankalchand Patel University SPCE Dept of Computer Engineering Gujarat Visnagar India Dr. Mgr Educational and Research Institute Department of Computer Applications Tamil Nadu Chennai India National Engineering College Department of CSE Tamilnadu Kovilpatti India Karpagam Institute of Technology ECE Department Tamilnadu Coimbatore India School of CS & AI SR University Telangana Warangal India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350304480

Enhancing the Internet of Things (IoT) security is among the most pressing concerns confronting the information technology sector. With significant numbers of loT systems being created and established, it is difficult for these systems to interact securely without affecting performance. The difficulties arise since the majority of loT systems are resource restricted and so possess restricted processing capability. In, this study, we intend to study the enhancement of IoT security of anomaly detection and intrusion prevention. To enhance anomaly detection and intrusion prevention performance, a binary categorization of typical and unusual IoT traffic is created. In this paper, we carefully evaluate the specificity and complication of IoT security protection, and then discover that Artificial Intelligence (AI) approaches like Machine Learning (ML) and ensemble classifiers may offer new strong abilities to satisfy IoT security demands. This enhancement can be associated with ensemble learning methods that make use of a variety of learning processes with variable capacities. We were capable of improving the predictability of our predictions while decreasing the likelihood of classification errors through the combination of these methods. The outcomes of the experiments suggest that the architecture could enhance the effectiveness of the anomaly detection and intrusion prevention System, with an accuracy level of 0.9863. © 2023 IEEE.

关键词： Anomaly detection Artificial intelligence Internet of things Intrusion prevention Machine learning Security

来源：评论

学校读者我要写书评

暂无评论

Enhancing Security and Privacy of Healthcare Data Using Hybrid Cryptographic Scheme for Cloud-Based Environment

Enhancing Security and Privacy of Healthcare Data Using Hybr...

引用

Disruptive Technologies (ICDT), International Conference on

作者： Sanjay Kumar Ajay Kumar Kumar Abhishek Pradeep Kumar Arya Department of Computer Science and Engineering National Institute of Technology Patna Bihar India Dept of Computer Science Engineering and Technology Bennett University Greater Noida UP India Department of SCSET Bennett University Greater Noida UP India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

关键词： Cloud computing Hospitals Force Collaboration Memory Organizations Elliptic curve cryptography Rendering (computer graphics) Encryption Eavesdropping

来源：评论

学校读者我要写书评

暂无评论

Automated Healthcare Medical Imaging through EOA Optimized Hyperparameter in CNN

Automated Healthcare Medical Imaging through EOA Optimized H...

引用

6th International Conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2024

作者： Agrawal, Umang Kumar Panda, Nibedan Das, Debashreet Dalai, Asish Kumar Ramana, B.V. Mishra, Ayush School of Computer Engineering KIIT Deemed to Be University Odisha Bhubaneswar751024 India Dept. of Computer Science and Engineering Centre for UG and PG Studies BPUT Odisha Rourkela India School of Computer Science and Engineering VIT-AP University AP Amaravati India Department of IT Aditya Institute of Technology and Management AP Tekkali532201 India

Medical imaging (MI) is the prefatory field of healthcare that plays a vital role in the patient's clinical and medical intervention. Recent developments in MI help healthcare professionals such as doctors and radiologists diagnose patients with improved quality of MI methods such as X-ray, CT and MRI scans. The upsurge of the Convolutional Neural Network (CNN) offers the latent for timely detection and classification of the disease with deeper insights. It adapts the spatial features and learns the input data hierarchically, which makes well-suited for this task. To improve the robustness of the CNN, the parameters are optimized through a nature-inspired dynamic mass balance-based metaheuristic approach named the Equilibrium Optimizer Algorithm (EOA). As a real-life implication, the suggested EOA-CNN approach has been verified by utilizing two benchmark datasets. The competence of EOA-CNN is assessed by present-day methods in terms of parameters namely accuracy, error rate, min, avg, std, accuracy, sensitivity and specificity. By streamlining the workflow, the said approach is a valuable asset conforming to the reliability to resolve healthcare imaging complications with advanced diagnostics support. This framework is being used to optimize the constraints meanwhile bridging the gaps in healthcare convenience. © 2025 The Author(s). Published by Elsevier B.V.

关键词： Functional neuroimaging

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：