NDNN based U-Net: An Innovative 3D Brain Tumor Segmentation Method

Trivedi, Sandeep; Patel, Nikhil; Faruqui, Nuruzzaman

doi:10.1109/uemcon54665.2022.9965705

Cited by 10 publications

(7 citation statements)

References 35 publications

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“…However, the literature review demonstrates that the machine learning model cannot perform well if the datasets are rich enough [36] . That is why data processing is essential in any machine learning-based approach [37] . There are two types of data in the proposed experiment.…”

Section: Proposed Methodologymentioning

confidence: 99%

Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis

Faruqui,

Yousuf,

Kateb

et al. 2023

Heliyon

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Section: Proposed Methodologymentioning

confidence: 99%

Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis

Faruqui,

Yousuf,

Kateb

et al. 2023

Heliyon

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“…Missing values in the dataset hamper the ML training process [25]. It has been observed that the original Myers-Briggs Type Indicator (MBTI) dataset has multiple missing values.…”

Section: ) Handling Missing Valuesmentioning

confidence: 99%

PersoNet: A Novel Framework for Personality Classification-Based Apt Customer Service Agent Selection

Sandra,

Prabowo,

Gaol

et al. 2024

IEEE Access

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Personality classification has garnered significant interest in psychology, computational social science, and Machine Learning (ML) due to its wide-ranging applications. This paper presents PersoNet, an innovative framework developed to identify personality types using the Myers-Briggs Type Indicator (MBTI), aimed at enhancing customer service experiences by matching customers with suitable support agents. PersoNet employs a Bidirectional Long Short-Term Memory (BiLSTM) neural network architecture and has achieved an impressive classification accuracy of over 93.98%. Our extensive experiments with the MBTI dataset reveal that the BiLSTM architecture effectively captures both temporal dependencies and semantic subtleties in textual data, contributing to this high level of accuracy. Consequently, PersoNet can accurately select customer service agents who match customer personalities, achieving a Customer Satisfaction Rate (CSR) of over 97.82%-a notable improvement of 20.25% in CSR based on our experimental data. These results establish PersoNet as a cutting-edge tool in personality classification, surpassing existing methods in both accuracy and computational efficiency and markedly enhancing customer service quality.

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“…The state-of-the-art machine learning evaluation metrics have been used in this paper to evaluate the performance of the proposed Deep-IDS [70]. These evaluation metrics are calculated using True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN) values from the confusion matrix illustrated in figure 7 [71]. The accuracy, precision, recall (sensitivity), and F1 Score are defined by equations 30, 31, 32, and 33, respectively [72].…”

Section: A Evaluation Metricsmentioning

confidence: 99%

Deep-IDS: A Real-Time Intrusion Detector for IoT Nodes Using Deep Learning

Racherla,

Sripathi,

Faruqui

et al. 2024

IEEE Access

Self Cite

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The Internet of Things (IoT) represents a swiftly expanding sector that is pivotal in driving the innovation of today's smart services. However, the inherent resource-constrained nature of IoT nodes poses significant challenges in embedding advanced algorithms for cybersecurity, leading to an escalation in cyberattacks against these nodes. Contemporary research in Intrusion Detection Systems (IDS) predominantly focuses on enhancing IDS performance through sophisticated algorithms, often overlooking their practical applicability. This paper introduces Deep-IDS, an innovative and practically deployable Deep Learning (DL)-based IDS. It employs a Long-Short-Term-Memory (LSTM) network comprising 64 LSTM units and is trained on the CIC-IDS2017 dataset. Its streamlined architecture renders Deep-IDS an ideal candidate for edge-server deployment, acting as a guardian between IoT nodes and the Internet against Denial of Service (DoS), Distributed Denial of Service (DDoS), Brute Force (BRF), Man-in-the-Middle (MITM), and Replay (RP) Attacks. A distinctive aspect of this research is the trade-off analysis between the intrusion detection rate and the false alarm rate, facilitating the real-time performance of the Deep-IDS. The system demonstrates an exemplary detection rate of 96.8% and an overall classification accuracy of 97.67%. Furthermore, Deep-IDS achieves precision, recall, and F1-scores of 97.67%, 98.17%, and 97.91%, respectively. On average, Deep-IDS requires 1.49 seconds to identify and mitigate intrusion attempts, effectively blocking malicious traffic sources. The remarkable efficacy, swift response time, innovative design, and novel defense strategy of Deep-IDS not only secure IoT nodes but also their interconnected subnetworks, thereby positioning Deep-IDS as a leading intrusion detection system for IoT-enhanced computer networks.

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NDNN based U-Net: An Innovative 3D Brain Tumor Segmentation Method

Cited by 10 publications

References 35 publications

Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis

Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis

PersoNet: A Novel Framework for Personality Classification-Based Apt Customer Service Agent Selection

Deep-IDS: A Real-Time Intrusion Detector for IoT Nodes Using Deep Learning

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