Innovative Fungal Disease Diagnosis System Using Convolutional Neural燦etwork

Alyas, Tahir; Alissa, Khalid; Mohammad, Abdul Salam; Asif, Shazia; Faiz, Tauqeer; Ahmed, Gulzar

doi:10.32604/cmc.2022.031376

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…Authors in [14] with lower fake-fine instances. They also determined that both malware and benign application networks show contained mixing and observe a power-law degree distribution.…”

Section: Related Workmentioning

confidence: 98%

Malware Attacks Detection in Network Security using Deep Learning Approaches

Humaira Naeem,

Asma Batool

2023

IJECI

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This abstract provides an overview of the study on the use of deep learning approaches, specifically Recurrent Neural Networks (RNNs) and Long Short-Term Memory Networks (LSTMs), for detecting malware attacks in network security. The increasing sophistication of malware attacks has made it challenging for traditional signature-based approaches to detect them effectively. Deep learning algorithms offer the potential to address these challenges, as they can automatically learn complex representations of the data and adapt to new and evolving threats. The study focused on the collection and analysis of a large and diverse dataset of both benign and malicious software samples, which were used to train and validate the deep-learning models. The results of the study showed that the RNN and LSTM algorithms outperformed traditional signature-based approaches in terms of accuracy and efficiency in detecting malware attacks. Additionally, developing more efficient and scalable training methods for deep learning algorithms is an important area for future research. Overall, the future of malware detection using deep learning is promising, and continued research in this field holds great potential for improving the security of our digital systems.

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“…Authors in [14] with lower fake-fine instances. They also determined that both malware and benign application networks show contained mixing and observe a power-law degree distribution.…”

Section: Related Workmentioning

confidence: 98%

Malware Attacks Detection in Network Security using Deep Learning Approaches

Humaira Naeem,

Asma Batool

2023

IJECI

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“…This helps to improve the accuracy and reliability of the data and prepare it for analysis.Privacy: The cloud server implements privacy measures to protect the confidentiality and security of the data collected from wearable devices and other sources. This includes encryption, access control, and data anonymization [14].…”

Section: Fig 1: Weak Deduction Based Multi Component Control Mechanismmentioning

confidence: 99%

Integration of Cloud Computing and Wearable Technology for Enhanced Interactivity

Asma Batool

2024

IJECI

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The emergence of wearable computing has revolutionized the way we interact with technology, blurring the lines between the physical and digital worlds. In this research, we explore the dynamic interaction between cloud technology and wearable computing, a synergy that is reshaping the landscape of personal technology and data management. The study delves into how cloud comput-ing provides a powerful platform for wearable devices, enabling enhanced data processing capabili-ties, storage, and ubiquitous access to information. We investigate the various applications of this interaction, ranging from health monitoring to augmented reality, emphasizing the transformative impact on everyday life and various industries. The research also addresses the challenges inherent in this integration, such as data security, privacy concerns, and the need for robust, low-latency communication networks. Through a comprehensive analysis of current trends and future prospects, this study highlights the potential of cloud-assisted wearable technology in creating more personal-ized, efficient, and interconnected experiences. The findings suggest that the convergence of cloud technology and wearable computing not only offers significant benefits in terms of functionality and user experience but also poses critical considerations for data governance and ethical implications in an increasingly connected world.

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