检索结果-内蒙古大学图书馆

1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Bai, K. Jhansi Lakshmi Akhil, Karnam Sumanth, V. Uday Kiran, V. Satwik, P. VNR Vignana Jyothi Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350356816

This Cognitive intelligence is the ability to understand, process, acquire information, and apply knowledge in a way that optimizes the possible steps to achieve the desired goal. Cognitive intelligence plays an important role in students' lives as it helps them understand and grasp new skills, maintain healthy relationships with people, and work positively in their lives. The levels of intelligence vary from person to person and depend on a variety of elements, including experiences, opinions, environment, and education. A lack of cognitive intelligence can lead to problems for students professionally, personally, and mentally. Cognitive abilities such as problem-solving, hypothesis skills, numerical skills, emotional sense, memory retention, and responses to problems significantly impact behavior and cognitive skills. The proposed work aims to test the cognitive abilities of undergraduate students by conducting surveys. The proposed work identifies various models, theories and techniques of cognitive science and builds a database based on students' survey responses which are based on testing various cognitive abilities. A recommendation mechanism is developed to enhance students' cognitive abilities in real-time scenarios. A knowledge-based recommendation system is provided to recommend solutions based on cognitive science theories to help students identify weak areas and improve their cognitive skills. © 2024 IEEE.

关键词： Knowledge based systems

来源：评论

学校读者我要写书评

暂无评论

Built-Up and Land Use Land Cover Mapping for Sustainable Planning in Himalayan Region by Using Machine Learning and Sentinel-2 Satellite Imagery 1

Built-Up and Land Use Land Cover Mapping for Sustainable Pla...

引用

1st International Conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Singh, Suraj Saini, Rashmi Govind Ballabh Pant Institute of Engineering and Technology Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350364590

The Himalaya region, known for its ecological sensitivity, also faces recurring natural hazards such as earthquakes, landslides, sinking, glacier bursts, and flash floods, which necessitate the implementation of effective planning and management strategies to ensure sustainable development. This study focuses on Joshimath, a subdivision of Chamoli district in Uttarakhand, India, situated in the Greater Himalaya region, covering an area of 4563.52 km2, Our objective is to map built-up areas using Machine learning techniques and Sentinel-2 satellite multispectral imagery to support sustainable planning initiatives. Two machine learning models, Random Forest (RF) and Support Vector Machine (SVM), were employed in this study. RF achieved an overall accuracy of 92.62% with a kappa value of 0.911, while SVM achieved an accuracy of 90.557 with a kappa value of 0.886. Precision and recall metrics for the built-up class were calculated, with RF achieving 94% precision and 83.22% recall, and SVM achieving 93.48% precision and 82.24% recall. Additionally, the total area classified as built-up by RF was 236.96 km2, whereas SVM classified 198.67 km2. The results demonstrate the effectiveness of machine learning techniques, particularly RF and SVM, in accurately mapping built-up areas in the Himalaya region. This information is crucial for sustainable planning and management practices, enabling stakeholders to make informed decisions regarding land use and development while preserving the ecological integrity of the region. © 2024 IEEE.

关键词： Mapping

来源：评论

学校读者我要写书评

暂无评论

Evolutionary Algorithms and Fuzzy Systems for Enabling Smart Technologies across Industries 1

Evolutionary Algorithms and Fuzzy Systems for Enabling Smart...

引用

1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Refonaa, J. Poornima, D. Shabu, S.L. Jany Kanipriya, M. Surya, V. Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India Srmist Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

Smart technology have end up an increasing number of vital in today's rapidly evolving generation panorama. Automation, records-pushed decision-making, and streamlined operations are all being revolutionized by means of the usage of clever era. However, there are some of boundaries that save you their effective deployment, which include as converting conditions, complicated device interactions, and the requirement for instantaneous flexibility. Fuzzy Logic Control for Smart technology Optimization (FLC-STO) is a way that makes use of the complementary nature of Evolutionary Algorithms and Fuzzy Systems (EA-FS) to achieve optimization in smart era. FLC-STO is a dynamic and adaptive technique for optimizing smart generation for maximum performance over a great form of situations. FLC-STO has proven to be powerful via simulation evaluation in optimizing smart generation in practical settings. It demonstrates how the proposed method works in workout, demonstrating how common performance, performance, and flexibility are all improved in the face of dynamic surroundings and complex interactions. The FLC-STO method gives a effective framework for permitting and optimizing clever generation throughout industries due to its fusion of Evolutionary Algorithms and Fuzzy Systems. The studies presented here allows to enhance clever technology and encourage their fashionable reputation with the aid of addressing the difficulties confronted with the aid of converting contexts, complex gadget interconnections, and the need for actual-time variation. © 2023 IEEE.

关键词： Evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

Optimized Convolutional Neural Network-Based Bengali Speech Emotion Classifier 21

Optimized Convolutional Neural Network-Based Bengali Speech ...

引用

21st IEEE India Council International Conference, INDICON 2024

作者： Pal, Shubhodip De, Debjit Dhar, Sohini Samanta, Samit Mukherjee, Chiradeep Sikdar, Sudipta Institute of Engineering and Management University of Engineering and Management Department of Computer Science and Technology Kolkata India

ISBN: (纸本)9798350391282

Speech emotion recognition (SER) is a subdomain of artificial intelligence that assists computers in comprehending user preferences and intentions. Nevertheless, the mission of SER in low-resource South-Asian languages, such as Bengali, continues to be difficult due to its content varieties, dialectic changes, acoustic variability, and speakers' ages. This work proposes a convolutional neural network-based model for SER in the Bengali language using the SUBESCO dataset. The model uses the various temporal and spectral features to recognize and understand emotions conveyed through speech. The proposed work is the first attempt to introduce augmentation to the SER task. We augment the speech signals with uniform random noise, apply time stretching and shifting, and then incorporate pitch. We compile the proposed model using Adam, Nadam, RMSProp, and SGD optimizers and tune it with hyperparameters, resulting in significantly higher accuracies compared to traditional machine-learning-based predictive models. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

VORONOI-KHHS approach coupled with EESC protocol for energy-efficient deployment and secure communication in WSNS

引用

International Journal of Information technology (Singapore) 2025年 1-12页

作者： Gayathri, R. Shreenath, K.N. Department of Computer Science and Engineering Siddaganga Institute of Technology Tumkur Visvesvaraya Technological University Belagavi 590018 India

This research introduces a novel protocol to address the critical challenges of coverage, connectivity, and security in large-scale Wireless Sensor Networks (WSNs). The proposed protocol optimizes energy management, ensuring robust coverage, seamless connectivity, and secure data transmission. Initially, a distributed approach combining the Voronoi Diagram with Krill Herd and Harmony Search (KHHS) maximizes the coverage of a region of interest. The Voronoi diagram divides the region into cells, generating initial solutions for the positions of deployed IoT objects. Harmony Search is then utilized to mutate among krill during the update process, enhancing solution quality. The KHHS leverages previous information efficiently to produce superior solutions, allowing for decentralized decision-making suitable for autonomous node operations in WSNs. The protocol further employs an Energy Efficient Signcrypted Clustering (EESC) method to prolong network lifetime by managing energy consumption effectively. This involves organizing sensor nodes into clusters with cluster head selected based on an Improved Grey Wolf Optimization (IGWO) algorithm, Cluster heads strike a compromise between energy efficiency and connectivity by rotating CHs and collecting data from member nodes, reducing node depletion and ensuring multihop connection. Modified Tug of War Optimization balances the load for efficient data transmission, while lightweight encryption provides secure data exchange. The proposed model's performance is evaluated against existing techniques, demonstrating superior results in PDR (97%), end-to-end delay (75 ms), energy consumption (0.7 kJ), residual energy (30%), and minimal energy efficiency (90% with 0.1 kJ/ bits). These results highlight the protocol's effectiveness in maintaining high throughput, adapting to dynamic network changes, and ensuring secure and efficient network operation. © Bharati Vidyapeeth's institute of computer Applications and Management 2025.

关键词： And modified tug of war optimization Energy efficient Signcrypted clustering Improved grey wolf optimization Krill Herd and harmony search Voronoi diagram Wireless sensor networks

来源：评论

学校读者我要写书评

暂无评论

Electric Power Scam Prediction Using Machine Learning Techniques 8th

Electric Power Scam Prediction Using Machine Learning Techni...

引用

8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Suresh, A. Vishnoi, Mahendra Mishra, Aditya Kumar Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur TamilNadu Chengalpattu603203 India

ISBN: (纸本)9789819765805

Electricity fraud is causing huge losses in both the public and private sectors. The proposition of this paper uses a temporarily controlled learning program to include potential theft data from non-specific data on the existence of multiple sample theft. There are two basic applications, one anticipating the use of power and the other to create a broken application and analysis space. Both are rushing for power protection in creating configuration, redesign, and functionality. Because of the significant costs of energy acquisition, as a restricted measure of energy assets, the efficient and effective use of energy assets is an important part of social and financial development in any country. This work introduces two accounting frameworks for the detection of theft of high- and low-power consumers who rely on machine learning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

An Optimized Deep Learning Approach To Identfiy the Alzheimer's Stages Identification Based on Biomarkers Extraction

An Optimized Deep Learning Approach To Identfiy the Alzheime...

引用

2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Sampath, R. Baskar, M. School of Computing Srm Institute of Science and Technology Department of Computer Science and Engineering Chengalpattu Tamilnadu Kattankulathur603203 India Sri Sai Ram Institute of Technology Department of Information Technology Tamilnadu Chennai600044 India

ISBN: (纸本)9798350394580

Alzheimer's is a progressive brain disorder. It affects human brain cell connections;as a result, it creates memory losses and other significant mental functional problems. The diagnosis of early stages of Alzheimer's and proper medication helps control the disease's progression since Alzheimer's is not a curable disease. The precise prediction of AD stages is an open challenge for researchers. Therefore, Researchers are developing lots of Alzheimer's disease (AD) detection techniques to predict the stages accurately. Most AD detection approaches contain several issues and can't reach the maximum accuracy rate. Therefore, this research introduces an efficient AD stages detection approach to solve the accuracy issues. This research customizes two aspects of the present systems to achieve the research objective. Initially, the appropriate adaptation of the AD stage's MRI brain image processing methodologies is enhancing the biomarker regions detection accuracy, and then the AD stage detection accuracy is improved by an optimized deep learning model. In this, the characteristics of the cuckoo search optimizer are incorporated in the Deep Belief Network (DBN) to optimize the AD stages detection functionalities of the AD detection algorithm, which helps to reduce the detection error. The AD detection approach's performance is evaluated using evaluation metrics such as accuracy, correlation coefficient, precision, Mean absolute error (MAE), Root Mean Square Error (RMSE), and Signal to Noise ratio SNR. The evaluation results prove that the accuracy of the AD stages detection approach is increased up to 0.66%, and the detection error is reduced up to 0.0345 % than the comparison approaches. © 2023 IEEE.

关键词： Alzheimer detection Alzheimer's disease deep learning MRI images optimizer

来源：评论

学校读者我要写书评

暂无评论

Analysis of Micro Bacteria Organism Classification by Using Convolution Neural Network with Improved Accuracy 1st

Analysis of Micro Bacteria Organism Classification by Using ...

引用

1st International Research Conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Kumar, Neelam Sanjeev Hussain, H. A. Mohamed Kumar, P. Harish Abinash, A. Department of Computer Science and Engineering SRM Institute of Science and Technology Vadapalani Chennai India

ISBN: (纸本)9783031823886

Microorganisms, comprising bacteria, viruses, fungi, and protozoa, are pivotal across various domains including human health, agriculture, biotechnology, and environmental science. Understanding these microorganisms is essential for advancements in these fields. This abstract highlight the significance of studying microorganisms and highlights key research areas and microscopy techniques utilized in their analysis. This paper presents a deep learning approach for microbacteria image classification using Convolutional Neural Network (CNN) architectures. The study focuses on accurately distinguishing between different microbacteria species, including amoeba, euglena, hydra, paramecium, and yeast. Various CNN architectures such as LeNet and VGG are explored, and the dataset comprises carefully annotated images representing different microbacteria species for training and testing the model. Through experimentation, the proposed model achieves high accuracy, reaching 94% in classifying microbacteria species. The performance of different CNN architectures is evaluated, providing insights into their strengths and weaknesses. Additionally, challenges encountered during model development are discussed, along with proposed solutions for improving accuracy and efficiency. Overall, this research contributes to the field of microorganism classification by demonstrating the effectiveness of deep learning approaches, particularly CNN architectures, in accurately identifying and classifying microbacteria from images. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A Machine Learning Approach for the Identification and Classification of Cardiovascular Diseases Using ECG Signals and Sensor data

A Machine Learning Approach for the Identification and Class...

引用

2024 International Conference on Smart Devices, ICSD 2024

作者： Shaik, Nazeer Dharani, M. Kumar, Bura Vijay Gupta, Vikas Singh, Jagendra Pal, Yogesh SRIT-Autonomous Department of Computer Science Engineering Anantapur India K S R Institute for Engineering and Technology School of Electronics and Communication Engineering Tiruchengode India SR University School of Computer Science and Artificial Intelligence Warangal India Raj Kumar Goel Institute of Technology Department of Computer Science Engineering Ghaziabad India Bennett University School of Computer Science Engineering & Technology Greater Noida India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350394412

Cardiovascular diseases (CVDs) continue to be a major concern in the medical field to date. Among the many diagnostic tools, electrocardiogram (ECG) remains one of the main ways with which to detect cardiac abnormalities. Although classic ECG interpretation is powerful, it is a laborious and time-demanding task and tends to be onerous for physicians. This study suggests a means of effective machine learning to increase the accuracy and speed of diagnosing CVDs based on ECG signals and other sensor data. On the other hand, we are concerned with the application of Frequency Domain Measures in deep-learning methodologies of image classifiers. Other models include the raw ECG sent to a 1-dimensional convolutional model together with the XGBoost model for the time-series feature-based prediction. Preliminary assessment reveals that Frequency Domain Measures provide good atrial fibrillation prediction but are quite limited for other types of arrhythmias. Computational results for the XGBoost model are, however, efficient for predictions on long data but proceed at the cost of extended inference times, considering the computational demand of the pre-processing stage. This paper opens up the scope of the potential for machine learning in revolutionizing ECG-based CVD detection, and we have to keep on the protocols of refinement and research toward this usage. © 2024 IEEE.

关键词： Frequency domain analysis

来源：评论

学校读者我要写书评

暂无评论

Adverse Impacts of 5G Wireless Networks: Analysis, Computation and Mitigation of RF-EMF Exposure on Human's Health 1

Adverse Impacts of 5G Wireless Networks: Analysis, Computati...

引用

1st International Conference on Computational science and technology, ICCST 2022

作者： Annalakshmi, N. Umarani, S. Srm Institute of Science and Technology Department of Computer Science Ramapuram Chennai India

ISBN: (纸本)9781665476553

Compared to the preceding generations networks to attain fast data transfer and high data rates, the 5G wireless communication has deployed in many countries. The access point's raising level and number of wireless devices will increase the level of EMF radiation and exposure. Several researchers have voiced their concerns about 5G that it raised on adverse health impacts by high expose of Electromagnetic Fields (EMF). This paper presents the human EMF exposure on human body and mitigation techniques which are used to reduce the risks and also it investigates the impacts of 5G downlinks. The adverse health impacts related to EMF is still on debate in science. Several health organizations already provided the safety guidelines about 5G and suggest minimum distance between User Equipment (UE) and the Base Station (BS) or transmitter. Nevertheless, it is still under in consideration that is not safe to human while we are using the high frequencies. i.e., above 6 GHz. In this article I analyze the alleged health effects, impact and challenges of 5G and also accurate estimation of the EMF distribution by using Specific Absorption Rate, Massive MIMO and Power Density techniques. © 2022 IEEE.

关键词： Electromagnetic fields

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：