Recently, Internet of Things (IoT) based smart systems have led to more latency-sensitive and bandwidth hungry IoT applications. Fog computing as an extension of Cloud computing fulfills those requirements more effect...
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The paper presents investigations concerning the decision rule filtering process controlled by the estimated relevance of available attributes. In the conducted study, two search directions were used, sequential forwa...
The paper presents investigations concerning the decision rule filtering process controlled by the estimated relevance of available attributes. In the conducted study, two search directions were used, sequential forward selection and sequential backward elimination. The steps of sequential search were governed by three rankings obtained for variables, all related to characteristics of data and rules that can be induced, as follows, (i) a ranking based on the weighting factor referring to the occurrence of attributes in generated decision reducts, (ii) the OneR ranking exploiting short rule properties, and (iii) the proposed ranking defined through the operation of greedy algorithm for rule induction. The three rankings were confronted and compared from the perspective of their usefulness for the selection of rules performed in the two directions and with two strategies for rule selection. The resulting sets of rules were analysed with respect to the properties of the constituent decision rules and from the point of performance for all constructed rule-based classifiers. Substantial experiments were carried out in the stylometric domain, treating the task of authorship attribution as classification. The results obtained indicate that for all three rankings and search paths it was possible to obtain a noticeable reduction of attributes while at least maintaining the power of inducers, at the same time improving characteristics of rule sets.
In the contemporary digital era, data has evolved into the cornerstone of myriad sectors, encompassing academia, commerce, and medical research. Yet, the acquisition of insights from this expansive reservoir of inform...
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ISBN:
(数字)9798350351378
ISBN:
(纸本)9798350351385
In the contemporary digital era, data has evolved into the cornerstone of myriad sectors, encompassing academia, commerce, and medical research. Yet, the acquisition of insights from this expansive reservoir of information has historically demanded a level of proficiency in formal languages such as SQL, thereby imposing a substantial barrier for individuals lacking technical expertise. In response to this pervasive challenge, the advent of Natural Language Interface to Databases (NLIDB) has emerged as a promising and transformative solution. NLIDB offers users the capability to engage with databases through the utilization of their natural language, whether expressed in textual or verbal form. This paradigm shift empowers both seasoned experts and neophytes to effortlessly extract valuable information from databases without the prerequisite of specialized technical skills. In this research paper, we propose an innovative system that capitalizes on an intermediary SQL generation process, facilitating the seamless bridge between natural language input and subsequent database retrieval. This novel approach eliminates the need for users to possess awareness of the intricate underlying complexities of the database management process. By simplifying the interaction between users and databases, our system endeavours to democratize data access and promote the universal utilization of this invaluable resource across diverse domains.
Agriculture faces many challenges of precision farming, such as the need for sustainable practices, improving yields, ensuring high yields. In resolution to these challenges, the present research provides an AI-based ...
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Agriculture faces many challenges of precision farming, such as the need for sustainable practices, improving yields, ensuring high yields. In resolution to these challenges, the present research provides an AI-based system that enables the use of deep learning, Global Positioning System (GPS), and Geographic Information System (GIS) technologies to create a highly intelligent smart agricultural precision farming system. Its goal is to monitoring crop health and reduce disease risk, which will lead to improved resource utilization and environmentally sustainability techniques. The proposed framework addresses the urgent need for consistency in agricultural practices, especially as global agriculture deals with pressures from climate change, resource shortages, and increasing demand for food. Traditional agricultural methods for predicting and optimizing crop yields due to increasing factors affecting crop performance Not enough generative AI, especially the use of deep learning models, supports agricultural research in many cases, allowing patterns to be identified and future results to be predicted accurately. The integration of GPS and GIS allows for more accurate mapping, real-time analysis, and effective decision-making. Weather forecasting variability, resource constraints, and demand for more food are isolated from environmental influences using deep learning models, especially Artificial Neural Networks (ANN). By using large data sets, including historical crop yield performance, soil properties, and weather conditions, the system provides highly accurate crop forecasts. Generative Adversarial Networks (GANs) and You Only Look Once (YOLO) hybrid model is playing a key role in generating crop yield and growth potential under different conditions, adjusting model accuracy over time, and this combination of ANN, GANs and YOLO optimization algorithms ensures that the system continuously enhances its predictive accuracy and overall effectiveness. The proposed gene
Medical professionals use Anomaly Detection(AD) to identify patients with potential health problems in the heart rate data (HRD), an essential metric related to cardiovascular health. In this research, the most effect...
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The availability of service through cloud computing upon the emergence of IoT and wireless networking has exposed the security of information to an even greater risk of abuse. These security threats are caused by cybe...
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Image fusion typically employs non-invertible neural networks to merge multiple source images into a single fused image. However, for clinical experts, solely relying on fused images may be insufficient for making dia...
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作者:
Sheenam MalhotraWilliamjeet SinghResearch Scholar
Department of Computer Science and Engineering Faculty of Engineering and Technology Punjabi University Patiala Punjab India Assistant Professor
Department of Computer Science and Engineering Faculty of Engineering and Technology Punjabi University Patiala Punjab India
In recent times, cloud computing is being utilized largely for storage and information sharing purposes in several established commercial segments, particularly those where online businesses are prevalent, such as Goo...
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In recent times, cloud computing is being utilized largely for storage and information sharing purposes in several established commercial segments, particularly those where online businesses are prevalent, such as Google, Amazon, etc. Cloud system presents several benefits to users in terms of easy operations, low implementation, and maintenance expenses. However, significant risks are encountered in the data security procedures of cloud systems. Although the area is frequently being analyzed and reformed, the concern of cloud data protection and user reliability remains under uncertainty due to growing cyber-attack schemes as well as cloud storage system errors. To deal with this risk and contribute to the endeavor of providing optimal data security solutions in cloud data storage and retrieval system, this paper proposes a Symmetric Searchable Encryption influenced Machine Learning based cloud data encryption and retrieval model. The proposed model enhances data security and employs an effective keyword ranking approach by using an Artificial Neural Network. The comparative assessment of the proposed model against multiclass SVM and Naïve Bayes has established the better operational potentiality of the model. The effectiveness of the proposed work is justified by the association between high TPR and low FPR. Further, a low CCR of 0.6973 adds up to the success of the proposed work.
This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algori...
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Accurately predicting survival time holds significant importance in various medical and scientific domains. This study proposes a novel survival prediction model for oral cancer using multi-omics data including clinic...
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Accurately predicting survival time holds significant importance in various medical and scientific domains. This study proposes a novel survival prediction model for oral cancer using multi-omics data including clinical, DNA methylation, copy number alteration, and mRNA data. The proposed model incorporates four feature selection methods, namely LASSO, Elastic Net, Random Forest Regressor, and Recursive Feature Elimination (RFE), to improve the accuracy. The findings reveal that RFE outperforms the other methods, achieving the highest C-Index of 0.944. This is a significant improvement over previous models, which have a c-index of 0.694 using only clinical data and 0.916 using multi-omics data. In terms of precision, the Random Forest Regressor is the most precise with an MSE of 18.37, followed by RFE of 21.61. Notably, both RFE and Random Forest Regressor outperformed LASSO and Elastic Net in predictive accuracy. Additionally, the results highlight the consistency of RFE, as indicated by its low Median Absolute Error (MedAE) of 2.86. Random Forest Regressor also performed well in this regard, with a MedAE of 4.05. In contrast, Elastic Net exhibited a higher MedAE of 4.55, while LASSO showed more variability, with a MedAE of 8.58. These findings offer valuable insights into survival analysis and feature selection, helping researchers and practitioners select appropriate approaches for survival time prediction, and improving accuracy and reliability in critical applications.
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