检索结果-内蒙古大学图书馆

2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Gurbade, Viraj Vijay Verma, Prateek Gundewar, Swapnil Bramhe, Vijendra Singh Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Vit Bhopal University School of Computing Science and Engineering Kothrikalan Madhya Pradesh Sehore466114 India

ISBN: (纸本)9798331528713

A data lake is a centralized repository where you may store both structured and unstructured data at any scale. Unlike typical data warehouses, which need data structure for storage, data lakes allow you to store raw data in its natural state. This adaptability makes data lakes suitable for storing large volumes of various data kinds, including photographs, videos, log files, sensor data, and others. When integrating a data lake with Power Business intelligence (BI), organizations can harness the power of both technologies to gain valuable insights from their data. In the realm of data-driven decision-making, organizations face the challenge of managing and extracting insights from vast and diverse datasets, a task conventional on-premises storage solutions often struggle with due to the exponential growth of data. Thus, the adoption of more scalable and flexible alternatives becomes imperative. Cloud-based data lakes emerge as transformative solutions, offering unparalleled scalability, accessibility, cost-effectiveness, and advanced security protocols. This paper discuss the concept of constructing a cloud-based data lake optimized for Power BI, leveraging leading cloud storage services like Azure Data Lake Storage (ADLS) and Amazon S3. By seamlessly integrating Power BI with the cloud data lake, organizations can overcome traditional storage limitations and reap numerous benefits. The practical applications in advanced analytics, customer insights, IoT data management, and data discovery underscore the real-world utility of cloud data lakes. The integration of Power BI with a cloud data lake augments efficient data analysis and self-service analytics potential, offering organizations a comprehensive solution for deriving actionable insights from their data assets. © 2024 IEEE.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Simulation and Efficiency Comparison of MOS Solar Cells Using Ag/TiO2 and Au/TiO2 as Back Reflector/Protective Sandwich Layers 9

Simulation and Efficiency Comparison of MOS Solar Cells Usin...

引用

9th International Conference on Applying New Technology in Green Buildings, ATiGB 2024

作者： Maram, Manodhar Perugu, Sisindri Cyril Robinson Azariah, J. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Artificial Intelligence and Machine Learning Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331505042

Current research aims to compare the performance of Ag/TiO2 and Au/TiO2 as back and front reflector layers in novel photovoltaic (PV) cells using simulations. The study investigated a range of panel thicknesses, from 80 nm to 920 nm. Both back and front reflector layers in the novel metal-oxide-semiconductor (MOS) solar cell (MOSSC) were fabricated with either Ag/TiO2 or Au/TiO2, respectively. A total of 42 samples were simulated across two groups using the Nanohub simulation tool. The performance of both novel solar cell devices was evaluated using the Nanohub online simulation tool. Simulated results indicated a statistically significant difference (p2-based MOSSC exhibited a higher efficiency of 19.75% compared to the 13.30% efficiency of the Au/TiO2-based MOSSC. In conclusion, the study suggests that Ag/TiO2-based MOSSCs achieve significantly better performance than Au/TiO2-based counterparts, with an efficiency of 19.75%. © 2024 IEEE.

关键词： Layered semiconductors

来源：评论

学校读者我要写书评

暂无评论

Sign Language Detection in Real-Time Applications

Sign Language Detection in Real-Time Applications

引用

2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Milinda, Renata Rachael Sara Ramsiej, Barrister Yasmin, Reshma Ambeth Kumar, V.D. Sekar, K. Anna University India Dept. of Artificial Intelligence and Machine Learning Anna University India Dept. of Computer Engineering Mizoram University Aizawl India Department of AIML Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9798331510299

Individuals with hearing and speech impairments often rely on gesture language for communication, but a lack of widespread understanding of this language among others creates significant barriers. A machine learning model that recognizes hand signals and converts them into English can help bridge this gap, facilitating communication between hearing and deaf individuals. Existing non-verbal communication identification systems, while leveraging computational learning and AI models for both single- and double-handed gestures, generally lack real-time capabilities. This research proposes a live symbolic language identification platform that uses a webcam to build a regional gesture communication dataset and TensorFlow for cross-domain learning. Despite a smaller dataset, the system targets high accuracy in recognition. Advanced technologies, including computer vision and deep learning, are applied to improve communication for deaf individuals by developing accessible technical applications and platforms. The proposed model, utilizing TensorFlow and OpenCV, aims to identify commonly used American Sign Language gestures in real time with accuracy and efficiency, including signs such as "hello, " "thanks, " "bye, " "yes, " "no, " "dad, " and "mom". © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Optimizing Travel Itineraries: A Multi-Objective machine learning Model Approach 3

Optimizing Travel Itineraries: A Multi-Objective Machine Lea...

引用

3rd International Conference on Applied artificial intelligence and Computing, ICAAIC 2024

作者： Roy, X Samuel Issac, Titus Terrance, J Sebastian Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Tamil Nadu Coimbatore India

ISBN: (纸本)9798350375190

A travel itinerary is a complex problem that involves multiple objectives and constraints, such as cost, time, transportation modes, and comfort levels. This research study focuses on creating a real-time eco-friendly itinerary generator by integrating machine learning techniques. This research work explores two machine-learning algorithms: artificial Neural Networks (ANNs) and Multi-Layer Perceptron (MLP). These algorithms are evaluated for their suitability in generating user-friendly and environment-friendly itineraries. Through experimentation, the accuracy and precision of the proposed models are assessed, leading to the identification of an optimal travel planner model. The ultimate goal of this model is to provide a detailed itinerary that considers prominent modes of transportation while addressing the pressing environmental crisis. By prioritizing eco-friendly options, this approach sets itself apart from conventional travel planning tools. © 2024 IEEE.

关键词： Multilayer neural networks

来源：评论

学校读者我要写书评

暂无评论

An Optimal Scheduling of Energy Storage Units in Renewable Energy Systems Using Strength-Pareto Evolutionary Algorithm

An Optimal Scheduling of Energy Storage Units in Renewable E...

引用

2024 International Conference on Intelligent Algorithms for Computational intelligence Systems, IACIS 2024

作者： Kumari, Nalli Vinaya Kumar, Komuravelly Sudheer Alsalami, Zaid Harshitha, P. Ramadan, Ghazi Mohamad Jntuh Malla Reddy Engineering College for Women Department of Computer Science and Engineering Hyderabad India Sr University School of Computer Science & Artificial Intelligence Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Ahl Al Bayt University College of Mlt Karbala Iraq

ISBN: (纸本)9798350360660

Dual-stage optimization scheduling model by hybrid energy storage for grid-connected renewable energy systems, is proposed in this paper which focuses on both intra-day and day-ahead phases. In day-ahead phase, model improves economic efficiency by considering of price values at its peak value difference. In intra-day phase, its motive is to decrease renewable energy usage and it's related with the day-ahead schedules using accurate, short-term predictions. Strength-pareto evolutionary algorithm 2 (SPEA2) algorithm for optimization is for multiple objective optimizations, achieving optimal and balanced solutions across multiple objectives. This SPEA2 optimization algorithm efficient handles and it enables system to cost-effectively and firmly operate and optimizing scheduling of grid-connected systems by addressing both the aspects of economic and operational. Overall, the model improves economic efficiency, reduces 6.3% the usage of renewable energy, and improves the sustainability power grid through effective integration and utilization of renewable energy sources. © 2024 IEEE.

关键词： Clean energy

来源：评论

学校读者我要写书评

暂无评论

Scheduling the Tasks in Cloud Computing using Adaptive Adjustment in Whale Optimization Algorithm 2

Scheduling the Tasks in Cloud Computing using Adaptive Adjus...

引用

2nd International Conference on Data Science and Information System, ICDSIS 2024

作者： Pareek, Piyush Kumar Nithyashree, G.D. Rajashekhar, S.A. Zanke, Pankaj Navyashree, K.S. Nitte Meenakshi Institute Of Technology Artificial Intelligence And Machine Learning Bengaluru India East West Institute Of Technology Department Of Computer Science And Engineering Bengaluru India Sapiens International Corporation United States

ISBN: (纸本)9798350381665

One of the most popular infrastructures for employing virtual machines, also known as processing units, is cloud computing. Task scheduling is fundamental problems of cloud computing. A number of methods to handle the nondeterministic polynomial-time (NP)-complete optimisation issue of choosing the best scheduling criteria in cloud computing have been proposed by academics in the past. An effective optimisation approach is suggested in this research, based on the whale optimisation algorithm (WOA), to address the common problems of solution algorithms, which include poor global optimisation ability, ease of sliding into local optimisation, and sluggish convergence speed. We provide an adaptive adjustment method that may dynamically alter search behaviour during the whale optimisation algorithm's iterative operation. The technique described to further update the position of search individuals also uses the evolutionary approach of differential variation. The algorithm's exploration and development capacities are then balanced, and a restart approach is included to keep the system from entering local optimisation. The approach simultaneously resolves the work planning issue in the situation of rain cloud processing. When compared to other algorithms, this technique uses more resources and converges more quickly. Additionally, the evaluation function seeks for the optimal answers by taking the makespan, resource cost, and load balancing level into account. © 2024 IEEE.

关键词： Cloud computing

来源：评论

学校读者我要写书评

暂无评论

Intrusion Detection in IoT Environment Using Stochastic Gradient Descent with Warm Restarts and Gated Recurrent Unit 2

Intrusion Detection in IoT Environment Using Stochastic Grad...

引用

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

作者： Krishnamurthy, Revatthy Alabdeli, Haider Rajani, N. Ramesh, Vankudoth Kumar, Bura Vijay Rajalakshmi Engineering College Department of Artificial Intelligence & Machine Learning Chennai India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Dept. of Ece Bengaluru India Cvr College of Engineering Department of CSE-DS Mangalpalle India School of Computer Science and Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350373110

The rapid growth of Internet of Things (IoT) has attracted the cybercriminals which resulted in increase of cyber-attacks on IoT devices. If the cyber-attacks are undetected for a longer time, it causes serious service interruptions, identity protection threats. To handle these issues, this research proposes a novel intrusion detection system by employing Stochastic Gradient Descent with warm Restarts and Gated Recurrent Unit (SGDR-GRU) method. The data is normalized to obtain common scaled data by using z-score normalization technique and the feature selection is performed by Stochastic Gradient Descent with warm Restarts (SGDR). The SGDR is an optimization technique that escapes local minima, reduces overfitting and improves the performance by adjusting learning rate. Finally, classification of normal and anomaly data is carried out by Gated Recurrent Unit (GRU) that deals with gradient descent problem and captures long-range dependencies. The performance of the proposed model is evaluated on NSL-KDD dataset and achieved high accuracy 99.12%, precision 99.45%, recall 99.23% in identifying intrusion over IoT devices. The proposed method surpassed existing intrusion detection models such as stacked-deep polynomial network (SDPN), Hybrid Chicken Swarm Genetic Algorithm (HCSGA). © 2024 IEEE.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

A recurrent neural network architecture for android mobile data analysis for detecting malware infected data

引用

Soft Computing 2024年第21期28卷 12917-12928页

作者： Murugan, Prabhu Manimaran, A. Sundar, Ramesh Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman Garg, Shruti Department of ECE Saveetha School of Engineering Simats Chennai602105 India Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Simats Chennai602105 India Department of Netwoking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Data Science and Business System School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Applied Machine Learning Saveetha School of Engineering Saveetha Institute of Medical and Technological Sciences Tamilnadu Chennai India Birla Institute of Technology Mesra Ranchi India

One of the latest modern communication devices is a mobile device seriously affected by multiple malware. Malware is a virus software installed automatically by hackers on various computing devices. Malware corrupts the system software, kills *.exe files, and tries to access user-sensitive data from the device. Around the world, 80% of people use smartphones, and 70% use Android phones. All Android phones are installed with anti-virus software but not used properly. Thus, it creates a pathway for malware to enter Android mobile devices and deploy vulnerabilities. Several earlier methods have focused on using the malware detection method by analyzing static data, which cannot identify the dynamic behavior of the malware. Thus, this paper implemented one of the deep learning algorithms, RNN, for identifying and detecting malware activities in Android mobile devices statically and dynamically. An IoT network architecture is proposed for experimenting with the RNN algorithms verifying the performance. The results of the RNN method show that it is better than other models in malware detection in mobile devices. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Android malware

来源：评论

学校读者我要写书评

暂无评论

Feedback Delay-Tolerant Proactive Caching Scheme Based on Federated learning at the Wireless Edge

IEEE Networking Letters

引用

IEEE Networking Letters 2023年第1期5卷 26-30页

作者： Lin, Na Wang, Yamei Zhang, Enchao Yu, Keping Zhao, Liang Guizani, Mohsen Shenyang Aerospace University School of Computer Science Shenyang110136 China Hosei University Graduate School of Science and Engineering Tokyo184-8584 Japan Riken Riken Center for Advanced Intelligence Project Tokyo103-0027 Japan Mohamed Bin Zayed University of Artificial Intelligence Machine Learning Department Abu Dhabi United Arab Emirates

Edge caching has emerged as a promising approach to meet explosive mobile data on 6G networks. One critical issue in edge caching is file popularity prediction. The federated learning (FL) based distributed algorithm is used to predict file popularity, which solves user privacy issues in centralized algorithms. However, due to the heterogeneity of user devices, the feedback time (i.e., the update and upload time of model parameters) is various for each user, which causes the feedback delay in the framework of FL-based edge caching. In this letter, we devise a feedback delay-tolerant proactive caching scheme (FLASH) based on FL in which each user utilizes hybrid filtering on stacked autoencoders to train the prediction model locally. Experimental results indicate that FLASH can effectively handle feedback delay and outperform counterpart algorithms in cache efficiency. © 2019 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

DBROA based Convolutional Auto Encoder for European Credit Card Fraud Detection 2

DBROA based Convolutional Auto Encoder for European Credit C...

引用

2nd International Conference on Data Science and Information System, ICDSIS 2024

作者： Pareek, Piyush Kumar Mohan, B.R. Vidya, E.V. Zanke, Pankaj Navyashree, K.S. Nitte Meenakshi Institute Of Technology Artificial Intelligence And Machine Learning Bengaluru India East West Institute Of Technology Department Of Computer Science And Engineering Bengaluru India Sapiens International Corporation United States

ISBN: (纸本)9798350381665

Numerous studies using bad neuronic nets (DNNs) for the objective of detecting recognition card fraud have concentrated on enhancing point prediction accuracy and limiting unwelcome biases by developing various network designs or learning models. It is crucial to reckon uncertainty together with point estimate of the training data has a significant impact on how well machine learning algorithms function, therefore data imbalance is not a minor problem. In general, the data only shows a tiny portion of false communications. The effectiveness of simple machine acquiring classifiers is significantly impacted by this. This research studies several data augmentation strategies to handle the unbalanced data problem for credit card fraud detection in order to deal with the shortage of false events. The balanced data is then sent to a Convolutional Auto Encoder (CAE) for classification, and a Developed Battle Royale optimisation algorithm (DBROA) is used to determine the model's weight in the best possible way. For the purpose of recognising credit card fraud involving European cardholders, each model includes stratified K-fold cross-validation and a real-world dataset. The results show that the suggested model is more accurate than current models. © 2024 IEEE.

关键词： Convolution

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：