检索结果-内蒙古大学图书馆

Real-Time Plant Species Recognition Using Non-averaged DenseNet-169 Deep Learning Paradigm 1

7th International Conference on computer Vision and Image Processing, CVIP 2022

作者： Kumar, V. Sathiesh Pearline, S. Anubha Department of Electronics Engineering MIT Campus Anna Universi ty Chennai India Department of Computer Science Engineering SRM Institute of Science and Technology Chennai India

ISBN: (数字)9783031314179

ISBN: (纸本)9783031314162

Real-time plant species recognition under unconstrained environment (viewpoint variation, changing background, scale variation, illumination changes etc.) is a challenging and time-consuming process. In this paper, a non-averaged DenseNet-169 (NADenseNet-169) CNN architecture is proposed and demonstrated to perform real-time plant species recognition. The architecture is evaluated on two datasets namely, Flavia (Standard) and Leaf-12 (custom created). The hyperparameters (optimizers, learning rate) are optimized to achieve higher performance metrics with lower computation time. From the experimental investigation, it is observed that Adam optimizer with a learning rate of 0.0001 (Batch size of 32) resulted in obtaining higher performance metrics. In case of Flavia dataset, an accuracy of 98.58% is obtained with a computational time of 3.53 s. For Leaf-12 dataset, an accuracy of 99% is obtained with a computational time of 4.45 s. The model trained on Leaf-12 dataset performed better in identifying the plant species under unconstrained environment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Plant diseases

来源：评论

学校读者我要写书评

暂无评论

DCTCS stack classifier-an integrated framework leveraging discrete cosine transformation, cuckoo search algorithm and stacked machine learning models for EEG-based eye state classification

引用

International Journal of Information Technology (Singapore) 2025年第4期17卷 2015-2033页

作者： Chandralekha, M. Jayadurga, N. Priyadharshini Chen, Thomas M. Sathiyanarayanan, mithileysh Department of Computer Science and Engineering Amrita School of Computing AmritaVishwaVidyapeetham Chennai India School of Science & Technology University of London London United Kingdom Research & Innovation MIT Square London United Kingdom

Accurate classification of eye state using Electroencephalogram (EEG) data reflects the high dimensionality and intricacy associated with the nature of the EEG signals. This state-of-the-art technique applies Discrete Cosine Transformation (DCT) for feature extraction, Cuckoo Search Algorithm (CSA) for optimal feature selection followed by a robust stacking algorithm integrating Adaptive Gradient Boosting Classifier (A-GBC) and k-Nearest Neighbors (k-NN) with Logistic Regression as meta-learner to improve the predictive accuracy and model performance. This model would strengthen the accuracy, reliability, and efficiency of eye state classification. DCT is used to compress the time series data into frequency domain for feature extraction, resulting in concentrating a vast amount of superficial signal energy. The next stage uses the Cuckoo Search Algorithm (CSA) in the selection of the optimal features. It thus optimizes the feature sub- set of the input in-order to get an optimal learning rate. The combination of these processes with a stacked ensemble using Adaptive Gradient Boosting Classifier (A-GBC) & k-Nearest Neighbors (k-NN) basal learners results in an immediate classification following the path of a Logistic Regression meta learner that achieves a remarkable classification accuracy of 96.96%. This strongly emphasizes the exceptional efficiency of the proposed framework when placed in context with existing approaches. It implies that the research not only reinforces the existing state of EEG eye state categorization but also provides for the optimization of its utility in other types of biomedical signal processing, thereby making it an example of a versatile medical tool. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Brain-computer Interface (BCI) Cuckoo Search Algorithm (CSA) DCTCS Stack Classifier Discrete Cosine Transformation (DCT) Electroencephalogram (EEG) Eye state Classification Stacked Machine Learning Model

来源：评论

学校读者我要写书评

暂无评论

Predicting Financial Success: Regression Modeling of Sustainable Business Practices in Start-up Ecosystem 18

Predicting Financial Success: Regression Modeling of Sustain...

引用

18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Sharma, Riya Christa, Sharon Mani, Ambica Prakash Sarishma Graphic Era Deemed to Be University Department of Commerce Dehradun India Mit Art Design and Technology University Department of Computer Science and Engineering Pune India

ISBN: (纸本)9789380544519

This research investigates the intricate relationship between sustainability adoption and the financial performance of startups in the Indian business landscape. Utilizing data from a survey conducted among 152 startups, the study employs robust statistical analyses, including regression to discern the impact of sustainable practices on financial outcomes. Positioned at the nexus of global imperatives for sustainable development and the pragmatic challenges faced by startups, the research addresses the unique context of India's entrepreneurial ecosystem. By combining empirical insights with statistical rigor, the study illuminates the mechanisms by which sustainability affects financial health. This study provides actionable information for startup decision-makers, informs policy discussions, and broadens scholarly discourse on the symbiotic relationship between sustainability and financial performance. Its findings are relevant to a wide range of stakeholders, providing a nuanced knowledge of how entrepreneurs might navigate the convergence of sustainability and economic resilience within the unique contours of the entrepreneurial scene. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： circular economy startup sustainable business model sustainable business practices triple bottom line

来源：评论

学校读者我要写书评

暂无评论

Efficient resource allocation scheme for on-the-fly computing based mobile grids

引用

International Journal of Information Technology (Singapore) 2022年第2期14卷 943-954页

作者： Savyanavar, Amit Sadanand Ghorpade, Vijay Ram Department of Computer Science and Engineering Shivaji University Kolhapur India Department of Computer Engineering MIT College of Engineering Pune India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Kolhapur India

Mobile grid (MG) is emergi ng as a new computing paradigm due to the ubiquitous availability of mobile devices. With the advancement in the capability of these devices, computationally intensive tasks can be executed using a peer-to-peer grid of such devices. MG can provide an edifice to execute parallel computationally intensive tasks. Key challenges that crop up while computing on a MG are resource constrained environment, inefficient resource allocation, high failure probability, etc. As a result, selection of appropriate nodes for task execution becomes critical for successful execution of the application. In this paper, we propose an efficient resource allocation model (ERAM) which provides resource allocation with failure handling. We created a MG comprising of Wi-Fi Direct connected Android smartphones. Different scenarios are considered for the purpose of experimentation. Our approach performs well with respect to application completion time, % battery consumption and recovery time from failure in comparison with existing techniques. © 2018, Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Checkpointing Mobile grid computing Peer-to-peer computing Resource allocation Rough set theory

来源：评论

学校读者我要写书评

暂无评论

Flexible Context-Driven Sensory Processing in Dynamical Vision Models 38

Flexible Context-Driven Sensory Processing in Dynamical Visi...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Govindarajan, Lakshmi Narasimhan Iyer, Abhiram Kothare, Valmiki Fiete, Ila Department of Brain and Cognitive Sciences MIT CambridgeMA United States Department of Electrical Engineering and Computer Science MIT CambridgeMA United States McGovern Institute for Brain Research MIT CambridgeMA United States MIT CambridgeMA United States

Visual representations become progressively more abstract along the cortical hierarchy. These abstract representations define notions like objects and shapes, but at the cost of spatial specificity. By contrast, low-level regions represent spatially local but simple input features. How do spatially non-specific representations of abstract concepts in high-level areas flexibly modulate the low-level sensory representations in appropriate ways to guide context-driven and goal-directed behaviors across a range of tasks? We build a biologically motivated and trainable neural network model of dynamics in the visual pathway, incorporating local, lateral, and feedforward synaptic connections, excitatory and inhibitory neurons, and long-range top-down inputs conceptualized as low-rank modulations of the input-driven sensory responses by high-level areas. We study this Dynamical Cortical network (DCnet) in a visual cue-delay-search task and show that the model uses its own cue representations to adaptively modulate its perceptual responses to solve the task, outperforming state-of-the-art DNN vision and LLM models. The model's population states over time shed light on the nature of contextual modulatory dynamics, generating predictions for experiments. We fine-tune the same model on classic psychophysics attention tasks, and find that the model closely replicates known reaction time results. This work represents a promising new foundation for understanding and making predictions about perturbations to visual processing in the brain. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Flexible mapping of abstract domains by grid cells via self-supervised extraction and projection of generalized velocity signals 38

Flexible mapping of abstract domains by grid cells via self-...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Iyer, Abhiram Chandra, Sarthak Sharma, Sugandha Fiete, Ila Department of Electrical Engineering and Computer Science MIT CambridgeMA United States Department of Brain and Cognitive Sciences MIT CambridgeMA United States McGovern Institute for Brain Research MIT CambridgeMA United States MIT CambridgeMA United States

Grid cells in the medial entorhinal cortex create remarkable periodic maps of explored space during navigation. Recent studies show that they form similar maps of abstract cognitive spaces. Examples of such abstract environments include auditory tone sequences in which the pitch is continuously varied or images in which abstract features are continuously deformed (e.g., a cartoon bird whose legs stretch and shrink). Here, we hypothesize that the brain generalizes how it maps spatial domains to mapping abstract spaces. To sidestep the computational cost of learning representations for each high-dimensional sensory input, the brain extracts self-consistent, low-dimensional descriptions of displacements across abstract spaces, leveraging the spatial velocity integration of grid cells to efficiently build maps of different domains. Our neural network model for abstract velocity extraction factorizes the content of these abstract domains from displacements within the domains to generate content-independent and self-consistent, low-dimensional velocity estimates. Crucially, it uses a self-supervised geometric consistency constraint that requires displacements along closed loop trajectories to sum to zero, an integration that is itself performed by the downstream grid cell circuit over learning. This process results in high fidelity estimates of velocities and allowed transitions in abstract domains, a crucial prerequisite for efficient map generation in these high-dimensional environments. We also show how our method outperforms traditional dimensionality reduction and deep-learning based motion extraction networks on the same set of tasks. This is the first neural network model to explain how grid cells can flexibly represent different abstract spaces and makes the novel prediction that they should do so while maintaining their population correlation and manifold structure across domains. Fundamentally, our model sheds light on the mechanistic origins of cognitive flexib

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Efficient IoT-Blockchain Sharding Technique using Frequently Transacting Sender and Receiver Information 9

An Efficient IoT-Blockchain Sharding Technique using Frequen...

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Pramod Jain, S.A. Yashaswini, H.M. Salvi, Sanket Dashputre, Nupur Tyagi, Saanya Panchal, Krishna Nitte Meenakshi Institute of Technology Department of Information Science And Engineering Karnataka Bangalore India Dr. Ambedkar Institute of Technology Department of Computer Science And Engineering Karnataka Bangalore India Mit World Peace University School of Computer Engineering Technology Maharashtra 411038 India

ISBN: (纸本)9798350394474

Distributed systems have become an integral part of many modern applications, processing large amounts of data and serving large numbers of users simultaneously. However, as these systems grow in size and complexity, it becomes increasingly difficult to ensure optimal performance and scalability. This scenario is more common for Internet of Things (IoT) applications. In this research, the problem of limited scalability of IoT frameworks is addressed by proposing a blockchain-integrated new sharding algorithm. The algorithm is based on analyzing transaction records of frequently traded sender-receiver pairs. Our goal is to improve the overall performance of the system and improve its ability to handle higher volumes of transactions. By analyzing a real dataset, we observed a significant reduction in overall execution time by about 10%, demonstrating the effectiveness of our proposed sharding algorithm. Additionally, we found that the algorithm continued to improve the transaction per second rate (TPS) as the number of tasks processed increased. These results highlight the potential of our proposed method for optimizing the efficiency and scalability of distributed systems. Further investigation and analysis across different datasets and system configurations are recommended to validate the generalizability and effectiveness of our approach. © 2024 IEEE.

关键词： Scalability

来源：评论

学校读者我要写书评

暂无评论

mit-WPU Insight Sync: A Data Empowerment Platform

MIT-WPU Insight Sync: A Data Empowerment Platform

引用

2024 mit Art, Design and Technology School of Computing International Conference, mitADTSoCiCon 2024

作者： Shah, Dipesh Singh, Amit Kumar Prasad, Ayushi Koppikar, Siya Dixit, Bharati. Pawar, Rajendra G. Dr. Vishwanath Karad MIT World Peace University School of CET Pune India MIT Art Design and Technology University Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350362879

With over 100,000 graduates worldwide and a strong 40-year history of promoting top-notch academic achievement, mit-WPU is one of the top universities in India, providing undergraduate, graduate, certificate, and doctorate courses. Our goal in this project is to develop a beautiful Power BI dashboard that will enable our faculty members to carry out their duties more effectively and scalable. In this case, this means keeping track of student accomplishments, journal articles, conference proceedings that the faculty has published, reports on patents, faculty R&D consulting, etc. The advanced Excel techniques used in this project, along with the creation of stunning Power BI Dashboards, Power BI Apps, Power BI Paginated Report generation, and dynamic data generation using data analysis, will aid the faculty in better comprehending and efficiently visualizing all of the sheets. © 2024 IEEE.

关键词： Information analysis

来源：评论

学校读者我要写书评

暂无评论

Enhancing Cloud Computing Security through Deep Learning and Attention Mechanism Intrusion Detection Systems 4

Enhancing Cloud Computing Security through Deep Learning and...

引用

4th International Conference on Intelligent Technologies, CONIT 2024

作者： Shukla, Sunil Singh, Jagendra Ramya, T. Rahul, Satyakam Mallick, Arnab Kumar Pandey, Pawan Graphic Era Hill University School of Computing Dehradun India Bennett University School of Computer Science Engineering & Technology Greater Noida India Cvr College of Engineering Department of CSE-DS Hyderabad India Mit Adt University School of Computer Science Engineering Pune India Indian Institute of Technology Madras Department of Computer Science Engineering Chennai India Raj Kumar Goel Institute of Technology Department of Computer Science Ghaziabad India

ISBN: (纸本)9798350349900

This research investigates the enhancement of cloud computing security through adaptive neural network intrusion detection systems, employing Deep Belief Networks (DBN) and Attention Mechanism. The study aims to address the growing concerns surrounding security threats in cloud environments by leveraging advanced machine learning techniques. Experimental evaluations were conducted to assess the performance of the proposed approach, with specific emphasis on detection accuracy, false positive rate, and computational efficiency. Results indicate that the DBN-based intrusion detection system achieved an average detection accuracy of 94.4%, with a false positive rate of 2.2% and a computational efficiency of 1 5 8. 4 milliseconds. In comparison, the IDS utilizing Attention Mechanism demonstrated superior performance, with an average detection accuracy of 96.2%, a false positive rate of 1.1%, and a computational efficiency of 153.2 milliseconds. These findings underscore the effectiveness of DBN and Attention Mechanism in bolstering the security posture of cloud computing environments. By integrating adaptive neural network-based intrusion detection systems, organizations can enhance threat detection capabilities, mitigate risks, and safeguard sensitive data and critical assets in the cloud. © 2024 IEEE.

关键词： Cloud security

来源：评论

学校读者我要写书评

暂无评论

Sharding:A Scalability Solutions for Blockchain Networks 3

Sharding:A Scalability Solutions for Blockchain Networks

引用

3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Kale, Dattatray Raghunath Jadhav, Amolkumar N. Kruishna Prasad, K. Salunkhe, Somanath Janardan Hirve, Sumit Goswami, Chandrashekhar Mit Art Design and Technology University Mit School of Computing Department of Computer Science & Engg Pune India Annasaheb Dange College of Engineering and Technology Department of Computer Science & Engg Ashta India Shrinivas University Department of Computer Science & Engg Mangalore India D Y Patil Agriculture and Technical University Department of Computer Science & Engg Talsande India Sir Padampat Singhania University Faculty of Computing and Informatics Rajasthan Udaipur India

ISBN: (纸本)9798350354348

Blockchain technology's decentralized and immutable data storage has changed a number of sectors. But typical blockchain networks scalability issues prevent them from being widely used for large-scale applications. By dividing the blockchain network into smaller, more controllable sections known as shards, sharding has become a viable remedy for scalability issues. This paper offers a thorough introduction to sharding as a blockchain network scalability solution. We explore the basic ideas of sharding as well as its advantages, drawbacks, and several sharding strategies. We also analyze experimental results and real-world implementations to assess how well sharding contributes to increased blockchain scalability. Lastly, we talk about possible developments in sharding approaches and future research paths to further improve the scalability of blockchain networks. © 2024 IEEE.

关键词： Distributed ledger

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：