Data openness for efficient e-governance in the age of big data

Alaoui, S.; Farhaoui, Yousef; Aksasse, Brahim

doi:10.1504/ijcc.2021.120391

Cited by 30 publications

(21 citation statements)

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“…By using these, the performance of the proposed intrusion detection is evaluated and compared based on the parameters of detection rate, accuracy, precision, recall, efficiency, and F1-score [44] . During assessment, some of the recent literature models are compared with the proposed approach for determining its efficacy over other approaches [45][46][47] . Moreover, various evaluation parameters used to assess the results of the proposed AMOA-DSLC are as calculated by using the following equations:…”

Section: Resultsmentioning

confidence: 99%

Adaptive Marine Predator Optimization Algorithm (AOMA)-Deep Supervised Learning Classification (DSLC) Based IDS Framework for MANET Security

Sheela,

Soundari,

Mudigonda

et al. 2024

Intell. and Converged Netw.

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Due to the dynamic nature and node mobility, assuring the security of Mobile Ad-hoc Networks (MANET) is one of the difficult and challenging tasks today. In MANET, the Intrusion Detection System (IDS) is crucial because it aids in the identification and detection of malicious attacks that impair the network's regular operation. Different machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of MANET. However, it still has significant flaws, including increased algorithmic complexity, lower system performance, and a higher rate of misclassification. Therefore, the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning models. Here, the minmax normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields, which increases the overall intrusion detection performance of classifier. Then, a novel Adaptive Marine Predator Optimization Algorithm (AOMA) is implemented to choose the optimal features for improving the speed and intrusion detection performance of classifier. Moreover, the Deep Supervise Learning Classification (DSLC) mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training operations. During evaluation, the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.

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Section: Resultsmentioning

confidence: 99%

Adaptive Marine Predator Optimization Algorithm (AOMA)-Deep Supervised Learning Classification (DSLC) Based IDS Framework for MANET Security

Sheela,

Soundari,

Mudigonda

et al. 2024

Intell. and Converged Netw.

Self Cite

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“…Consequently, organizations will be able to generate innovative public offerings, ensure effective decision-making for the public good, and increase citizens' satisfaction and experience. (23,24,34,35,36) In summary, acknowledging this diversity of perspectives provides an in-depth understanding of the dynamics between collective intelligence and open innovation.…”

Section: Discussionmentioning

confidence: 99%

Fostering innovation through collective intelligence: a literature review

Ibrahimi,

Merioumi,

Benchekroun

2023

Data and Metadata

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In the twenty-first century, Collective intelligence (CI) arose as a social phenomenon to assist organizations in managing future uncertainty. It pushes a broad diverse group to come up with new solutions that outperform those uncovered within the organization itself. Accordingly, CI has been widely acknowledged as a means to foster innovation, and develop, and sustain an organization's creative potential. This paper aims to conduct a literature review to examine the existing body of literature regarding the ways collective intelligence improves innovation. The findings emphasized the importance of collective intelligence in fueling a firm’s knowledge and innovation in all of its forms to overcome public and private organizational challenges. Furthermore, our review underlined the mediating role of information technology in taking full advantage of collective intelligence via digital platforms. In addition, our analysis pointed out the multifaceted traits of collective intelligence as reflected in the literature under several terms, including crowdsourcing. Our research revealed several gaps in the current literature, including insufficient analysis and modeling of the relationship between the two concepts. Finally, we concluded our paper by identifying the limits of our research and suggesting avenues for future studies on collective intelligence and innovation.

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“…(29,30,31,32,33,34) These metrics provide benefits over traditional ones like MAE or RMSE by offering better interpretability, being more suited to certain types of data (like those with non-linear relationships or uneven variance) and offering robustness against specific types of errors. (35,36,37,38,39,40,41)…”

Section: Rmslementioning

confidence: 99%

Utilizing Machine Learning and Deep Learning for Predicting Crypto-currency Trends

El Youssefi,

Hessane,

Zeroual

et al. 2024

Salud, Ciencia y Tecnología - Serie de Conferencias

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In the dynamic and often volatile world of the cryptocurrency market, accurately predicting future market movements is crucial for making informed trading decisions. While manual trading involves traders making subjective judgments based on market observations, the development of algorithmic trading systems, incorporating Machine Learning and Deep Learning, has introduced a more systematic approach to trading. These systems often employ technical analysis and machine learning techniques to analyze historical price data and generate trading signals. This study delves into a comparative analysis of two charting techniques, Heikin-Ashi and alternate candlestick patterns, in the context of forecasting single-step future price movements of cryptocurrency pairs. Utilizing a range of time windows (1 day, 12 hours, 8 hours, ..., 5 minutes) and various regression algorithms (Huber regressor, k-nearest neighbors regressor, Light Gradient Boosting Machine, linear regression, and random forest regressor), the study evaluates the effectiveness of each technique in forecasting future price movements. The primary outcomes of the research indicate that the application of ensemble learning methods to the alternate candlestick patterns consistently surpasses the performance of Heikin-Ashi candlesticks across all examined time windows. This suggests that alternate candlestick patterns provide more reliable information for predicting short-term price movements. Additionally, the study highlights the varying behavior of Heikin-Ashi candlesticks over different time windows

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Data openness for efficient e-governance in the age of big data

Cited by 30 publications

References 0 publications

Adaptive Marine Predator Optimization Algorithm (AOMA)-Deep Supervised Learning Classification (DSLC) Based IDS Framework for MANET Security

Adaptive Marine Predator Optimization Algorithm (AOMA)-Deep Supervised Learning Classification (DSLC) Based IDS Framework for MANET Security

Fostering innovation through collective intelligence: a literature review

Utilizing Machine Learning and Deep Learning for Predicting Crypto-currency Trends

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