Analysis of hyperspectral images (HSIs) can be highly accurate, however conventional band selection techniques are frequently computationally costly anddo not thoroughly examine the spectral-spatial relationship requ...
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ISBN:
(数字)9798331502768
ISBN:
(纸本)9798331502775
Analysis of hyperspectral images (HSIs) can be highly accurate, however conventional band selection techniques are frequently computationally costly anddo not thoroughly examine the spectral-spatial relationship required for accurate interpretation. Existing hand-crafted approaches, such as filter-based, wrapper-based, and hybrid approaches, have drawbacks such as an excessive computing load and an inability to account for spatial correlations. These issues are addressed by the proposeddeep Learning-Based Adaptive Band Selection (dLABS), which uses Convolutional Neural Networks (CNNs) to automate band selection. In addition to providing observable improvements in classification accuracy and savings in processing time and memory usage, the dL-ABS system learns the intricate spectralspatial dependency relationship. The proposed method achieves 92.5%, 94.3%, and 95.1% accuracy fordatasets such as Indian Pines, Pavia University, and Salinas, respectively, with reduced memory and computation time. In order to illustrate the advantages of the proposed approach over traditional HSI classification techniques, experimental data are presented. The proposed system's efficiency and scalability forreal-time hyperspectral imaging use.
This work provides a novel and sustainable advertisement display system that uses trained models, such as CNN and SSd, to adapt to the age and gender preferences of bystanders. By integrating a webcam with the adverti...
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ISBN:
(数字)9798350375442
ISBN:
(纸本)9798350375459
This work provides a novel and sustainable advertisement display system that uses trained models, such as CNN and SSd, to adapt to the age and gender preferences of bystanders. By integrating a webcam with the advertising screen, the system is able to identify the age and gender of passersby based on the faces that are detected and processed by trained models. Customers can personalize adverts to certain groups by accessing the classification results via a website, which is accessed and stored in real-time on a database server. The system has a motion sensor for energy economy andruns continuously, updating every two seconds. With 91.7% and 95.5% accuracy rates for age and gender classification, respectively, the technology guarantees accurate content delivery. It also has the capacity to show news and guidance in crowded places, thereby fostering societal awareness and knowledge. The work well analyzes the genderdetection using conventional methods andreveled it significance.
Implicit regularization is an important way to interpret neural networks. recent theory starts to explain implicit regularization with the model of deep matrix factorization (dMF) and analyze the trajectory of discret...
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The study suggests a hybrid machine learning (ML) and Internet of Things (IoT) methodology for effective bio floc monitoring in sustainable aquaculture. Aquaculture's sustainability is hampered by issues with conv...
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ISBN:
(数字)9798331502768
ISBN:
(纸本)9798331502775
The study suggests a hybrid machine learning (ML) and Internet of Things (IoT) methodology for effective bio floc monitoring in sustainable aquaculture. Aquaculture's sustainability is hampered by issues with conventional methods, such as manual water quality monitoring, regular sensor calibration, and a slow reaction to anomalies in water quality. By integrating IoT sensors forreal-time data collection with ML algorithms for prediction, the proposed approach overcomes these limitations and becomes more automated, precise, andresource-efficient. It shows risk assessment, resource optimization, andreal-time monitoring of water quality metrics. The results demonstrate notable gains over traditional systems, with accuracy, precision, andrecall levels of 92%, 91%, and 94%, respectively. Additionally, the proposed methodreduces the frequency of interventions andresource consumption while increasing forecast accuracy to 90%. It provides an improved, more sustainable, and scalable aquaculture technology that enhances water quality and boosts production.
Traffic incident detection and classification are essential aspects of enhancing road safety and efficiency. A new approach to enhancing real-time traffic incident detection and image classification with the help of C...
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ISBN:
(数字)9798331543891
ISBN:
(纸本)9798331543907
Traffic incident detection and classification are essential aspects of enhancing road safety and efficiency. A new approach to enhancing real-time traffic incident detection and image classification with the help of Convolutional Neural Networks (CNNs), viz., Alex Net, is presented in this paper. Alex Net, a deep CNN architecture that is widely used for image recognition, is employed to classify traffic images and videos for incident identification. The system makes use of the ability of the model to learn robust features to identify images as different events, such as accidents, roadblocks, and vehicle breakdowns. Low latency and high accuracy incident identification is done by the system utilizing real-time traffic camera data and Alex Net. differentiation between different types of events. This is for the sake of enhancing reaction time, enabling effective traffic management, anddifferentiating between events. AlexNet enables 92% accuracy in real-time traffic detection and image classification, enhancing the efficiency of intelligent transportation systems. The results demonstrate how deep learning (dL) methods can revolutionise traffic monitoring systems and improve safety protocols fordrivers.
Energy production from solar photovoltaic (PV) plants is unpredictable, mainly due to the stochastic formation and movement of clouds or aerosol - dust particles which scatter ordisperse solarradiation. Accurate for...
Energy production from solar photovoltaic (PV) plants is unpredictable, mainly due to the stochastic formation and movement of clouds or aerosol - dust particles which scatter ordisperse solarradiation. Accurate forecasts of PV output are essential to distribution and Transportation System Operators as they assist efficient solar energy trading and management of electricity grids. This work evaluates an autoregressive, computationally-light KNN-regression scheme (TSFKNN) for hourly, day-ahead forecasts of solar irradiance and energy yield of various PV technologies. The model is being tested and validated using data measured in Thuwal, Saudi Arabia. The available measuredrecords span a 60-month period. The developed forecasting models are designed for online systems and provide increased levels of accuracy and low computational cost. Several parametric and nonparametric specifications, coupled with conventional versus outlier-robust estimation procedures are tested, in order to derive an optimal month-specific daily profile (MdP). Current results demonstrate that including intraday variability to the monthly-based irradiance models achieve improved predictive accuracy between 10% and 25% on average.
Getting robots to navigate to multiple objects autonomously is essential yet difficult in robot applications. One of the key challenges is how to explore environments efficiently with camera sensors only. Existing nav...
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With the continued expansion of India's market economy, competition among firms is heating up. To effectively navigate this severe market rivalry, corporate managers are increasingly prioritizing internal manageme...
With the continued expansion of India's market economy, competition among firms is heating up. To effectively navigate this severe market rivalry, corporate managers are increasingly prioritizing internal management. In comparison to traditional financial analysis approaches, data mining technology provides for a more in-depth comprehension of the underlying information inside large amounts of financial data. It assists financial analysts, investors, anddecision-makers in acquiring full insights into the company's financial status and making appropriate judgments on pertinent topics. Both financial and non-financial data produced by traditional accounting information systems frequently confront issues of excess data and insufficient information. When presented with an excess of structured or semi-structureddata, adding data mining technologies to the system allows for a detailed and effective projection of the enterprise's future development *** succeed in this competitive market setting and achieve sustainable development, businesses must realize the critical role of financial management, finance, anddata mining technologies. Fundraising management is an important component of financial management for the majority of businesses. On the one hand, finance management has a direct impact on the funding process for small and medium-sized businesses, ultimately defining the extent and pace of their financing initiatives. On the other hand, by prudently allocating and managing finances, businesses may assure the smooth functioning of diverse operations, providing a firm basis fordebt payback financing. This article will go into the research and study of financial management and financing for businesses, exploiting the possibilities of data mining technologies.
Classroom scheduling is vital but difficult for educational institutions, especially those with large student populations anddiverse needs. Traditional methods using outdated software or manual processes often cause ...
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ISBN:
(数字)9798331528140
ISBN:
(纸本)9798331528157
Classroom scheduling is vital but difficult for educational institutions, especially those with large student populations anddiverse needs. Traditional methods using outdated software or manual processes often cause inefficiencies, disagreements, and administrative workload. In low-resource regions without access to cutting-edge technology and infrastructure, these issues are severe. This paper introduces Android-based SmartClass Mobile to transform classroom scheduling. The program's SOA-based functionality offers flexibility, adaptability, and integration with institution-specific procedures. The Heuristic algorithm and Solution Space Navigation (SSN) tackle scheduling problems such as room availability, multimedia needs, and seating capacities. SSN dynamically resolves conflicts by exploring alternate configurations, while the Heuristic Algorithm assigns time slots and classrooms depending on availability and priority. SmartClass Mobile analysis indicates benefits. The solution reduces conflicts, increases user satisfaction, anddecreases scheduling time by 50% compared to manual methods with 85% test user approval. due to its mobile-first approach, the initiative is open to urban andrural institutions. dependence on internet access for some functions is a downside, especially in resource-constrained contexts. The findings suggest that SmartClass Mobile provides a scalable, effective, and simple classroom scheduling solution. Mobile technology, advanced algorithms, and SOA concepts make it valuable for school administrators. Additions like AI and machine learning will boost its potential.
The “Internet of things” is the developing technology to transfer the world into smart innovation. “IoT” security is the most problematic issue in recent applications due to its properties including ‘centralized ...
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ISBN:
(数字)9798350377972
ISBN:
(纸本)9798350377989
The “Internet of things” is the developing technology to transfer the world into smart innovation. “IoT” security is the most problematic issue in recent applications due to its properties including ‘centralized architecture’, ‘limited capacity devices’, and ‘ambiguity’. So, blockchain with “IoT” applications provides secure data communication compared to traditional “IoT” applications. Blockchain is a recent growing technology that has features including ‘decentralized structure’, ‘transparency’, ‘immutable records’, ‘increased capacity’, and ‘quick backtracks’. The ‘cryptography hash algorithm’ is very important to provide secure data transmission. It converted the different size input to fixed-size unalterable output to enhance the security. But still, it needs efficient algorithm to secure data transmission with safeguard of private key. The objective of this paper is to propose a secure hash cryptography algorithm with double encryption (SHCA-dE) to protect the private key message. Then, this proposed algorithm has been analyzed with Md5, SHA-256, and SHA3-512. From this analysis, the SHA3-512 performs well with proposed algorithm.
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