AI-Driven Real-Time Feedback System for Enhanced Student Support: Leveraging Sentiment Analysis and Machine Learning Algorithms

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2024, Номер 10(4)

Опубликована: Дек. 21, 2024

The rapid evolution of educational technologies has led to a shift toward personalized and adaptive learning experiences. A critical component such systems is the ability provide timely relevant feedback students. This paper presents an AI-driven real-time system designed enhance student support through integration sentiment analysis machine algorithms. leverages gauge emotional tone interactions, as forum posts, assignment submissions, feedback. Machine algorithms, including decision trees, vector machines (SVM), deep models, are used analyze predict engagement, performance, states. By combining both cognitive insights, delivers personalized, context-sensitive that helps students overcome challenges improve academic outcomes. effectiveness evaluated using multiple datasets, showing significant improvements in satisfaction, performance.

Язык: Английский

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A Smart Irrigation System Using the IoT and Advanced Machine Learning Model

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2024, Номер 10(4)

Опубликована: Ноя. 26, 2024

The rapid advancement of IoT (Internet Things) technologies and sophisticated machine learning models is driving innovation in irrigation systems, laying the foundation for more effective eco-friendly smart agricultural procedures. This systematic literature review strives to uncover advancements challenges implementation IoT-based systems integrated with advanced techniques. By analyzing 43 relevant studies published between 2017 2024, research focuses on ability these have evolved meet modern agriculture system. Predictive analytics, anomaly detection, adaptive control—that enhance precision decision-making processes. Employing PRISMA methodology, this uncovers strengths limitations current highlighting significant achievements real-time data utilization system responsiveness. However, it also brings attention unresolved issues, including complexities integration, network reliability, scalability frameworks. Additionally, study identifies crucial gaps standardization need flexible solutions that can adapt diverse environmental conditions. offering a comprehensive analysis, provides key insights advancing technologies, emphasizing importance continued overcoming existing barriers wider adoption effectiveness various settings.

Язык: Английский

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Secured Fog-Body-Torrent : A Hybrid Symmetric Cryptography with Multi-layer Feed Forward Networks Tuned Chaotic Maps for Physiological Data Transmission in Fog-BAN Environment

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2024, Номер 10(4)

Опубликована: Окт. 11, 2024

Recently, the Wireless Body Area Networks (WBAN) have become a promising and practical option in tele-care medicine information system that aids for better clinical monitoring diagnosis. The trend of using Internet Things (IoT) has propelled WBAN technology to new dimension terms its network characteristics efficient data transmission. However, these networks demand strong authentication protocol enhance confidentiality, integrity, recoverability dependability against emerging cyber-physical attacks owing exposure IoT ecosystem confidentiality biometric data. Hence this study proposes Fog based infrastructure which incorporates hybrid symmetric cryptography schemes with chaotic maps feed forward achieve physiological info security without consuming power hungry devices. In proposed model, scroll are iterated produce high dynamic keys streams real time applications feed-forward layers leveraged align complex input-output associations cipher subsequent mathematical tasks. constructed relies on principle Adaptive Extreme Learning Machines (AELM) thereby increasing randomness defensive nature different ensuring secured encrypted-decrypted communication between users fog nodes. analysis is conducted during live scenarios. BAN-IoT test beds interfaced heterogeneous healthcare sensors various metrics analysed compared residing cryptographic algorithms. Results demonstrates recommended methodology exhibited low computational overhead other traditional BAN oriented

Язык: Английский

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Blockchain-Enhanced Multi-Factor Authentication for Securing IoT Children's Toys

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2024, Номер 10(4)

Опубликована: Ноя. 19, 2024

The rapid expansion of Internet Things (IoT) devices underscores the critical importance robust security protocols, particularly in realm children's toys. This study introduces an innovative multi-factor authentication strategy integrating Quick Response (QR) codes with Blockchain technology to fortify IoT toys designed for children. primary objective is safeguard young users against potential threats stemming from unauthorized access, thereby ensuring a secure interaction IoT-enabled By amalgamating factors, including QR codes, proposed approach establishes multilayered framework. Leveraging inherent immutability and transparency Blockchain, system verifies authenticity by scanning unique code, thus mitigating risks associated malwares access. decentralization ensures no single point failure, enhancing resilience cyber threats. Extensive usability studies underscore efficacy practicality advanced solution, poised elevate safety standards digital age. not only bolsters but also fosters trust among users, enabling seamless worry-free children worldwide.

Язык: Английский

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CoralMatrix: A Scalable and Robust Secure Framework for Enhancing IoT Cybersecurity

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Янв. 7, 2025

In the current age of digital transformation, Internet Things (IoT) has revolutionized everyday objects, and IoT gateways play a critical role in managing data flow within these networks. However, dynamic extensive nature networks presents significant cybersecurity challenges that necessitate development adaptive security systems to protect against evolving threats. This paper proposes CoralMatrix Security framework, novel approach employs advanced machine learning algorithms. framework incorporates AdaptiNet Intelligence Model, which integrates deep reinforcement for effective real-time threat detection response. To comprehensively evaluate performance this study utilized N-BaIoT dataset, facilitating quantitative analysis provided valuable insights into model's capabilities. The results demonstrate robustness across various dimensions cybersecurity. Notably, achieved high accuracy rate approximately 83.33%, highlighting its effectiveness identifying responding threats real-time. Additionally, research examined framework's scalability, adaptability, resource efficiency, diverse cyber-attack types, all were quantitatively assessed provide comprehensive understanding suggests future work optimize larger adapt continuously emerging threats, aiming expand application scenarios. With proposed algorithms, emerged as promising, efficient, effective, scalable solution Cyber Security.

Язык: Английский

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Blockchain-Enhanced Machine Learning for Robust Detection of APT Injection Attacks in the Cyber-Physical Systems

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2024, Номер 10(4)

Опубликована: Окт. 30, 2024

Cyber-Physical Systems (CPS) have become a research hotspot due to their vulnerability stealthy network attacks like ZDA and PDA, which can lead unsafe states system damage. Recent defense mechanisms for PDA often rely on model-based observation techniques prone false alarms. In this paper, we present an innovative approach securing CPS against Advanced Persistent Threat (APT) injection by integrating machine learning with blockchain technology. Our leverages robust ML model trained detect APT high accuracy, achieving detection rate of 99.89%. To address the limitations current enhance security integrity process, utilize technology store verify predictions made model. We implemented smart contract Ethereum using Solidity, logs input features corresponding predictions. This immutable ledger ensures traceability mitigating risks data tampering reducing alarms, thereby enhancing trust in system's outputs. The implementation includes user-friendly interface inputting features, backend processing prediction, interaction module integration Machine enhances both precision resilience while providing additional layer ensuring transparency immutability recorded data. dual represents substantial advancement protecting from sophisticated cyber threats.

Язык: Английский

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A novel optimized deep learning based intrusion detection framework for an IoT networks

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2024, Номер 10(4)

Опубликована: Ноя. 26, 2024

The burgeoning importance of Internet Things (IoT) and its diverse applications have sparked significant interest in study circles. inherent diversity within IoT networks renders them suitable for a myriad real-time applications, firmly embedding into the fabric daily life. While devices streamline various activities, their susceptibility to security threats is glaring concern. Current inadequacies measures render vulnerable, presenting an enticing target attackers. This suggests novel dealing address this challenge through execution Intrusion Detection Systems (IDS) leveraging superior deep learning models. Inspired by benefits Long Short Term Memory (LSTM), we introduce Genetic Bee LSTM(GBLSTM) development intelligent IDS capable detecting wide range cyber-attacks targeting area. methodology comprises four key execution: (i) collection unit profiling normal device behavior, (ii) Identification malicious during attack, (iii) Prediction attack types implemented network. Intensive experimentations suggested are conducted using validation methods prominent metrics across different threat scenarios. Moreover, comprehensive experiments evaluate models alongside existing results demonstrate that GBLSTM-models outperform other intellectual terms accuracy, precision, recall, underscoring efficacy securing networks.

Язык: Английский

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Secured Cyber-Internet Security in Intrusion Detection with Machine Learning Techniques

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2024, Номер 10(4)

Опубликована: Окт. 11, 2024

The rapid proliferation of Internet-connected devices has elevated the significance cybersecurity, making intrusion detection a critical aspect maintaining network integrity. Traditional security measures often fail to provide adequate protection against sophisticated attacks, necessitating advanced and robust solutions. This paper introduces comprehensive cyber-internet framework that leverages machine learning techniques for real-time prevention. proposed methodology employs hybrid approach, integrating supervised unsupervised models detect anomalies classify intrusions effectively. Specifically, combination Support Vector Machine (SVM), Decision Trees (DT), K-means clustering is used enhance accuracy reduce false-positive rates. experimental results demonstrate model achieved 97.8%, precision 96.5%, recall 95.2% on NSL-KDD dataset. implementation also reduced rate 1.2% computational overhead by 15% compared traditional systems. Additionally, system was tested traffic data, where it successfully identified mitigated various cyber threats, including Distributed Denial Service (DDoS) attacks infiltrations, with minimal latency high reliability. In conclusion, study presents an efficient secured significantly enhances capabilities using techniques. provides scalable adaptive solution securing infrastructure networks evolving ideal candidate deployment in real-world cybersecurity applications.

Язык: Английский

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Remote Monitoring and Early Detection of Labor Progress Using IoT-Enabled Smart Health Systems for Rural Healthcare Accessibility

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2024, Номер 10(4)

Опубликована: Ноя. 25, 2024

Delayed detection of labor pain in pregnant women, especially during their first delivery, often leads to delays reaching healthcare facilities, potentially resulting complications. This research proposes an innovative IoT-enabled system for remote monitoring progress and fetal health, designed specifically address the needs women areas within a 100 km radius facilities. The includes wearable device integrated with sensors detect onset continuously monitor heartbeat. Upon detecting pain, automatically sends alert medical team, allowing timely intervention. Experimental results demonstrate system's efficacy 99.2% accuracy 98.5% reliability heartbeat monitoring. latency transmission was measured at average 3.2 seconds, ensuring prompt notification providers. proposed solution enhances accessibility maternal care, reduces complications due delayed hospital admission, provides continuous monitoring, even resource-constrained environments. innovation bridges gap delivery underserved regions, offering practical, cost-effective, scalable solution. .

Язык: Английский

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An Interpretable PyCaret Approach for Alzheimer's Disease Prediction

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2024, Номер 10(4)

Опубликована: Ноя. 29, 2024

Alzheimer's Disease (AD) is a major global health concern. The research focuses on early and accurate diagnosis of AD for its effective treatment management. This study presents novel Machine Learning (ML) approach utilizing PyCaret SHAP interpretable prediction. employs span classification algorithms the identifies best model. value determines contribution individual features final prediction thereby enhancing model’s interpretability. feature selection using improves overall performance proposed XAI framework clinical decision making patient care by providing reliable transparent method detection.

Язык: Английский

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