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检索条件"机构=School of Computer Science and Engineering and Information Systems"

共 83748 条记录，以下是4901-4910 订阅

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An Enhanced Energy Efficient Clustering for Biomedical Wireless Sensor Networks

An Enhanced Energy Efficient Clustering for Biomedical Wirel...

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2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Vennira Selvi, G. Hl, Chandrakala Sheeja Kumari, V. Ponmaniraj, S. Anbazhagan, K. Nanthini, S. Presidency University Rajanukunte Yelahanka School of Information Science Bengaluru India Presidency University Rajanukunte Yelahanka School of Computer Science and Engineering Bengaluru India Saveetha School of Engineering Simats Department of Computational Intelligence Chennai India Saveetha School of Engineering Simats Department of Data Science Chennai India

ISBN: (纸本)9798350317060

Biomedical Wireless Sensor Networks (BWSN) is major technique for Health Care applications for providing quality of life with minimum cost. To enforce the quality of medical care provided to the citizen, such networks should be integrated with existing network infrastructures. The requirement of quality of service should be concentrated on development stages and are monitored for the duration of network operation. The life time of the network is most relevant feature for ensuring the quality of medical care requirements. In order to increases the network lifetime, maintaining the remaining energy of each sensor node with reliable communication link throughout the network. An energy aware clustering for biomedical wireless sensor network (EACBWSN) is proposed for increasing the network lifetime by minimizing the energy consumption among sensor nodes and provides a reliable communication link between sensor nodes. The proposed algorithm efficiently prolong the lifetime of the network when compared with existing algorithm. © 2023 IEEE.

关键词： Sensor nodes

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Map Building of Indoor Environment with Sensors using Neural Network

Map Building of Indoor Environment with Sensors using Neural...

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2023 Winter Summit on Smart Computing and Networks, WiSSCoN 2023

作者： Latha Mary, S Angel Ulagapriya, K. Poonguzhali, A. Menaha, R. David, Beaulah Priyadharshini, T.R. Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Sri Sairam College of Engineering Department of Electronics and Communication Engineering Bangalore India Sri Eshwar College of Engineering Department of Information Technology Coimbatore India Hindusthan College of Engineering and Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798350334050

The necessity of a blueprint of a building structure is a mandatory requirement for any reconnaissance or rescue operations. In our project, we build a modular system combining sensors related to sonar, laser, micro-wave to read sensory values and generate a 2D path of any building. The data is fetched and stored to feed to anOptimal Neural Network (ONN)-based computing system to create a 2D route with minimal discrepancies of error. Here the NN architecture is fine-tuned using Modified Dolphin Partner Optimization (MDPO) Exploration of unknown environments and space using autonomous vehicles has recently gained good attention in the field of Robotic Mapping. The recent advancements in the Internet of Things have enabled us to establish an ideal frame of reference for sonar and lidar-based systems. New effects are displayed by the sensors according to the physical characteristics of a room. The range data from sensors in various surroundings are interpreted by NNs. The distorted errors due to the material medium, particles, and moving objects present in the environment pose a threat to building a high-quality path map. The sensor fusion technique is applied to the rotatable modular array sensor to minimalize discrepancies caused by cloth materials during sonar readings, particle noise in an environment for Lidar reading, and moving human bodies present in the environment for path building and obstacle detection. © 2023 IEEE.

关键词： Optical radar

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Certifiably Robust Out-Of-Distribution Detection Based on Prediction Uncertainy Weighting

Certifiably Robust Out-Of-Distribution Detection Based on Pr...

引用

2023 International Annual Conference on Complex systems and Intelligent science, CSIS-IAC 2023

作者： Liu, Yanchao Dou, Quansheng Tang, Huanling Zhou, Liyuan Li, Xiujuan College of Information and Electronic Engineering Shandong Technology and Business University Yantai China School of Computer Science and Technology Shandong Technology and Business University Yantai China School of Computer Science and Technology Kashi University Xinjiang China

ISBN: (纸本)9798350309003

The application of machine learning in safety-critical systems requires a reliable assessment of uncertainy. However, deep neural networks are known to produce highly overconfident predictions on out-of-distribution (OOD) data. Even if trained to be non-confident on OOD data one can still adversarially manipulate OOD data so that the classifier again assigns high confidence to the manipulated data. In this paper, we improve the existing guaranteed OOD detection GOOD, and propose Certifiably Robust Out-of-distribution Detection based on Prediction Uncertainy Weighting (PUWOOD). Due to the loose boundary of GOOD, it is obviously not robust enough to deal with adversarial OOD close to the boundary of the decision. Therefore, the evaluation and weighting based on prediction uncertainty enable the model to achieve importance ranking in the face of different samples, and make it focus on key samples. Through experiments, we achieve the detection on adversarial OOD data while maintaining good performance on the original classification task and detecting clean data, which proves that our method has better performance in both robustness and accuracy. © 2023 IEEE.

关键词： Forecasting

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Ranking Cellular Internet Card User VIP Values Based on Graph Clustering with Usage Data

Ranking Cellular Internet Card User VIP Values Based on Grap...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Gao, Shijie Wu, Fan Qian, Kai Chen, Shixu Ren, Jinbao Lyu, Feng Central South University School of Computer Science and Engineering Changsha China Aerospace Information Innovation Research Institute Su Zhou China

ISBN: (纸本)9798350345384

Internet card (IC) is a new business model in telecom operators, which has attracted numerous users. However, the high user churn rate and the lack of IC user VIP value classification methods make the development of IC business encounter a bottleneck. In this paper, we conduct an in-depth study of IC user VIP value characterization and ranking by utilizing a large-scale dataset collected from a provincial cellular operator of China. We combine the knowledge in the field of traditional business user value management and new feature mining, and devise a data-driven system named REAL, i.e., Ranking cellular InternEt cArd user VIP vaLues, which consists of a dataset collection component, a feature extraction component, a graph clustering model (GCW) and a result output component. In REAL, consumption features, dependency features, and penalty features are extracted, and GCW model is devised to learn the common relationship among different IC users. Finally, a weight-based approach is designed to rank each user and output their VIP grades according to a threshold. Extensive experiments corroborate the efficacy of REAL in ranking IC users' VIP values. © 2023 IEEE.

关键词： Large dataset

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Adversarial Transfer Learning for Automated Segmentation of Brain Tumors

Adversarial Transfer Learning for Automated Segmentation of ...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Acharjya, Kalyan Saxena, Vineet Kamalraj, R. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

Adverse switch mastering for automated Segmentation of brain Tumors is a novel method of deep studying-based scientific photo segmentation that employs a generative opposed network to transfer learned expertise from a version trained on a goal dataset to any other version trained on a one-of-a-kind source dataset. The method allows the segmentation of mind tumors in a substantially quicker, more accurate, and more extraordinary steady style than conventional guide methods. It does this by leveraging the obtained expertise approximately the goal dataset from a trained model and reprojecting it onto any other educated community, thus ensuring that the version can analyze extra quickly and more appropriately from the source dataset. This technique may permit rapid and accurate segmentation of mind tumors, allowing for more dependable treatment plans and advanced affected person care... © 2024 IEEE.

关键词： Adversarial improved method Transfer treatment

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Optimized Task Scheduling in Diverse Distributed systems with a Hybrid Genetic Algorithm Approach 3rd

Optimized Task Scheduling in Diverse Distributed Systems wit...

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3rd International Conference on Intelligent systems and Sustainable Computing, ICISSC 2023

作者： Muthusamy, Ramesh Kumar Kadu, Swati Ayyasamy, Karthikeyan Pichuka Veera Venkata Satya, Srinivas Arumugam, Ramachandran Ammasai, Viswanathan Sengan, Sudhakar Department of Information Technology V.S.B. College of Engineering Technical Campus Tamil Nadu Coimbatore642109 India Department of AI&DS AISSMS IOIT Pune411001 India Department of Computer Science and Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu Chennai602105 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627012 India

ISBN: (纸本)9789819783540

In Heterogeneous Distributed systems (HDS), efficient task scheduling (TS) across diverse resources remains a pressing challenge. This paper introduces a Hybrid Genetic Algorithm (HGA) as a solution, blending heuristic techniques with the evolutionary strengths of Genetic Algorithms (GAs) to optimize Task Scheduling (TS). Traditional algorithms often fall short in balancing computational speed with solution quality, especially in large-scale environments. This proposed HGA addresses this by leveraging domain-specific heuristics to provide a promising starting point for the GA's optimization process. The TS dilemma has been successfully defined by an all-encompassing logical system, which ensures the accuracy of the design of algorithms. In comparison tests, the HGA frequently comes out on top, demonstrating less makespan and a more excellent distribution of tasks among the resources available. Also, resources with more efficient processing capabilities will be provided priority in this recommended heuristic method, which focuses on tasks with higher computational demands. The findings of the present investigation demonstrate that the HGA may transform TS approaches by providing high-quality solutions while increasing performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Scheduling algorithms

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Analyzing the Impact of Deep Learning Approaches on Real-Time Data Analysis in Machine Learning

Analyzing the Impact of Deep Learning Approaches on Real-Tim...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Deepti Nalluri, Mounika Mupparaju, Chinna Babu Rongali, Aruna Sri Dhar, Sohong Ajitha, P. Sloan School of Management Mit United States Computer Information Systems Murray State University United States University of Central Missouri Computer Science warrensburg United States University of the Cumberlands Graduate Doctoral Information Technology Information Technology Emphasis/Information Technology United States Azure Microsoft United States Library & Information Science Jadavpur University India School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798350357769

Deep learning is a powerful subset of synthetic intelligence, and it has the potential to significantly enhance the skills of machine getting to know. it's miles primarily based on the idea of artificial neural networks, which might be laptop fashions that are capable of gaining knowledge of from records and making decisions, just like the manner a human mind works. Deep mastering isn't the same as traditional system mastering algorithms in that it can technique complex inputs with multiple layers of abstraction, main to greater accurate evaluation. Deep studying algorithms are capable of unexpectedly examine raw facts and generate styles, making them perfect for actual-time statistics analysis. Deep studying is capable of as it should be predicting behaviors and trends, which may be used to optimize procedures and enhance operational efficiencies. Deep studying models can also be used for automated fault detection and predictive renovation, permitting businesses to count on and prevent capacity troubles. Additionally, deep mastering models frequently offer quicker consequences than other device studying or conventional techniques, as well as permitting an extra diploma of control over the resolution of the data being analyzed. This has widespread implications for real-time programs, such as medical diagnostics, independent automobiles, and manufacturing monitoring. © 2023 IEEE.

关键词： Diagnosis

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Using Knowledge Graph Embeddings in Embedding Based Recommender systems 20

Using Knowledge Graph Embeddings in Embedding Based Recommen...

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20th International Conference on Language engineering, ESOLEC 2022

作者： Ragab, Ahmed Hussein El-Kafrawy, Passant School of Information Technology and Computer Science Computer Science Department Nile University Cairo Egypt School of Information Technology and Computer Science CIS Research Center Nile University Computer Science Department Egypt Effat Collage of Engineering Jedah Saudi Arabia

ISBN: (纸本)9781665453226

This paper proposes using entity2rec [1] which utilizes knowledge graph-based embeddings (node2vec) instead of traditional embedding layers in embedding based recommender systems. This opens the door to increasing the accuracy of some of the most implemented recommender systems running in production in many companies by just replacing the traditional embedding layer with node2vec graph embedding without the risk of completely migrating to newer SOTA systems and risking unexpected performance issues. Also, Graph embeddings will be able to incorporate user and item features which can help in solving the well-known Cold start problem in recommender systems. Both embedding methods are compared on the movie-Lens 100-K dataset in an item-item collaborative filtering recommender and we show that the suggested replacement improves the representation learning of the embedding layer by adding a semantic layer that can increase the overall performance of the normal embedding based recommenders. First, normal Recommender systems are introduced, and a brief explanation of both traditional and graph-based embeddings is presented. Then, the proposed approach is presented along with related work. Finally, results are presented along with future work. © 2022 IEEE.

关键词： Recommender systems

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Applications of Machine Learning in Medicine: Current Trends and Prospects 6

Applications of Machine Learning in Medicine: Current Trends...

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6th Global Conference on Wireless and Optical Technologies, GCWOT 2023

作者： Aamir, Muhammad Bazai, Sibghatullah Bhatti, Uzair Aslam Dayo, Zaheer Ahmed Liu, Jing Zhang, Kun Huanggang Normal Univeristy College of Computer Science Huanggang China Balochistan University of Information Technology Engineering and Management Sciences Department of Computer Engineering Quetta Pakistan Hainan University School of Information and Communication Engineering Haikou China Zhejiang Lab Research Center for Healthcare Data Science Hangzhou China

ISBN: (纸本)9798350333718

With the continuous development of information technology and medical data information, more and more clinicians recognize artificial intelligence or will completely change medical practice by using advanced machine learning methods. The potential of using machine learning and predictive analysis to customize specific treatments for individuals is currently under research. Machine learning can learn a large number of medical data and explore the dependencies in data concentration, forming a corresponding medical model that can quickly and accurately predict new data, which is conducive to the early diagnosis of diseases and assisted clinical decisions. Clinical medicine faces the status quo of the relative shortage of medical resources and the identification and rapid diagnosis and treatment of critically ill emergency patients. In the era of big data, clinical demand is driven by clinical needs, and intelligent medical care provided by machines is the key to tackling the aforementioned issues. © 2023 IEEE.

关键词： Patient treatment

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Deep Learning Using Convolutional Neural Nets for Image Classification

Deep Learning Using Convolutional Neural Nets for Image Clas...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Gupta, Rupal Jebaraj, N.R. Solomon Kumar, Ritesh Teerthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

Deep mastering the usage of convolutional neural Nets (CNNs) has been a popular technique for image categories. A CNN is designed to inversely clear up the mission today's representing input data in a significant way to classification tasks. It is composed of a cutting-edge sequence brand brand-new convolutions and pooling operations that reduce the enter to characteristic maps that constitute the shape of state-of-the-art enter. Given enough educational information, the convolution layers can learn how to extract meaningful functions from the input picture that allow the network to classify it. A common approach is to excellent track a pre-skilled CNN, including VGGnet or ResNet, that has already been skilled to understand objects in snapshots. CNNs can achieve accuracy for various picture-type responsibilities. Moreover, they are robust to quite a number of real-international situations, which include unique degrees of modern-day illumination, occlusions, and distortions within the photo. © 2024 IEEE.

关键词： Approach Characteristic Convolutional Distortions Illumination Significant

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