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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AI-Powered Early Detection and Prevention System for Student Dropout Risk

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

Опубликована: Янв. 3, 2025

The increasing rate of student dropouts is a significant challenge in education systems worldwide, affecting both academic progress and institutional sustainability. This research presents an AI-driven predictive model aimed at early detection prevention dropouts. Leveraging advanced machine learning algorithms, including ensemble deep techniques, the analyzes variety data such as performance, attendance, behavioral patterns, socio-economic factors, psychological well-being. By identifying warning signs potential dropouts, provides actionable insights for educators administrators to intervene promptly. Additionally, system integrates personalized recommendations targeted support, ensuring students receive necessary resources improve their engagement performance. approach not only helps reducing dropout rates but also contributes fostering more supportive environment. Experimental results demonstrate effectiveness model, achieving high accuracy prediction offering promising implications its adoption educational institutions

Язык: Английский

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A Systematic Comparative Study on the use of Machine Learning Techniques to Predict Lung Cancer and its Metastasis to the Liver: LCLM-Predictor Model

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

Опубликована: Янв. 11, 2025

Lung cancer is one of the major causes deaths with thousands affected patients who have developed liver metastasis, complicating treatment and further prognosis. Early predictions lung metastasis may greatly improve patient outcomes since clinical interventions will be instituted in time. This paper compares performance different machine learning models including Decision Tree Classifiers, Logistic Regression, Naïve Bayes, K-Nearest Neighbors, Support Vector Machines Gaussian Mixture Models toward best set techniques for prediction. The applied dataset includes various features, such as respiratory symptoms biochemical markers, development stronger predictive performance. were cross-validated using testing validation aimed at generalizing whole model reliability generating both train test data. results generated are gauged metrics accuracy, precision, recall, F1-score, area under ROC curve. Results obtained revealed that KNN also showed accuracy strong classification performance, especially early-stage metastasis. present study a comparison models, which hence denotes potential these decision-making suggests application to diagnostic tools early detection cancer. provides very useful guide applicable use oncology helps pave way future research would focused on optimization integration into healthcare systems produce better management survival rates.

Язык: Английский

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Social and Cognitive Predictors of Collaborative Learning in Music Ensembles

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

Опубликована: Янв. 13, 2025

There have been many attempts to find ways make music education more relevant and useful for pupils. Learning theories, performance-based learning, contract-learning, discovery-learning, cooperative daily clocking, stage practice, music-focused required elective courses are all part of the implementation these methods. Since high vocational students tend lower GPAs, it is imperative that they discover strategies enhance their academic performance. Reform, rather than relying on theoretical frameworks, should be grounded practical, innovative human actions. Both instructors pupils possess capacity comprehend what learnt, according humanistic perspective. This paper provides evidence collaborative learning beneficial first-year practice in a popular program at Chinese institution. The work small, diverse groups. Data was collected analyzed from over course one year with grades 4-6.. Collaboration powerful tool has applications, including but not limited degree programs, which implemented this using machine techniques. It zeroed down seven important characteristics, had obvious applications educational process. Another online could use method predict students' performance, real-time tracking progress risk dropping out, after adjusted capture features corresponding different contexts. also applied other management platforms.

Язык: Английский

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Rainfall Forecasting in India Using Combined Machine Learning Approach and Soft Computing Techniques : A HYBRID MODEL

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

Опубликована: Янв. 9, 2025

Accurate rainfall prediction in India is crucial for agriculture, water management, and disaster preparedness, particularly due to the reliance on southwest monsoon. This paper examines historical trends from 1901 2022, highlighting significant anomalies changes identified through Pettitt test. The effectiveness of advanced machine learning techniques explored Artificial Neural Network-Multilayer Perceptron (ANN-MLP) enhancing forecasting accuracy compared with statistical methods. By integrating important climate variables—temperature, humidity, wind speed, precipitation into ANN-MLP model, its ability capture complex nonlinear relationships demonstrated. Additionally, analysis employs geo-statistical techniques, specifically Kriging, visualize spatial-temporal variability across different regions India. findings emphasize potential modern computational methods overcome traditional challenges, ultimately improving decision-making agricultural planning resource management face variability.

Язык: Английский

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Innovative Computational Intelligence Frameworks for Complex Problem Solving and Optimization

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

Опубликована: Янв. 9, 2025

The rapid advancement of computational intelligence (CI) techniques has enabled the development highly efficient frameworks for solving complex optimization problems across various domains, including engineering, healthcare, and industrial systems. This paper presents innovative that integrate advanced algorithms such as Quantum-Inspired Evolutionary Algorithms (QIEA), Hybrid Metaheuristics, Deep Learning-based models. These aim to address challenges by improving convergence rates, solution accuracy, efficiency. In context a framework was successfully used predict optimal treatment plans cancer patients, achieving 92% accuracy rate in classification tasks. proposed demonstrate potential addressing broad spectrum problems, from resource allocation smart grids dynamic scheduling manufacturing integration cutting-edge CI methods offers promising future optimizing performance real-world wide range industries.

Язык: Английский

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Metaheuristic-Driven Optimization for Efficient Resource Allocation in Cloud Environments

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

Опубликована: Янв. 7, 2025

Intrusion Detection Systems (IDS) play a pivotal role in safeguarding networks against evolving cyber threats. This research focuses on enhancing the performance of IDS using deep learning models, specifically XAI, LSTM, CNN, and GRU, evaluated NSL-KDD dataset. The dataset addresses limitations earlier benchmarks by eliminating redundancies balancing classes. A robust preprocessing pipeline, including normalization, one-hot encoding, feature selection, was employed to optimize model inputs. Performance metrics such as Precision, Recall, F1-Score, Accuracy were used evaluate models across five attack categories: DoS, Probe, R2L, U2R, Normal. Results indicate that XAI consistently outperformed other achieving highest accuracy (91.2%) Precision (91.5%) post-BAT optimization. Comparative analyses confusion matrices protocol distributions revealed dominance DoS attacks highlighted specific challenges with R2L U2R study demonstrates effectiveness optimized detecting complex attacks, paving way for adaptive solutions.

Язык: Английский

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GreenGuard CNN-Enhanced Paddy Leaf Detection for Crop Health Monitoring

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

Опубликована: Фев. 10, 2025

The GreenGuard: CNN-Enhanced Paddy Leaf Detection for Crop Health Monitoring initiative will create multiple future-oriented results. processing of agricultural imagery becomes revolutionized through the combination median filtering and Exponential Tsallis entropy Gaussian Mixture model (ExTS-GMM) advanced techniques initially. essential preprocessing operation delivers better quality data to Convolutional Neural Network (CNN) classifier which results in optimal performance outcomes. simple integration CNN classifiers launch an innovative age that more accurate efficient paddy leaf detection images. Deep learning features a enable it uncover complex structural details found both normal sick specimens. classifier's aptitude creates pathway execute precise assessment group into appropriate categories while extended database information rapidly. Effective implementation "GreenGuard" reshape conventional field crop health monitoring systems modern standards. Modern stakeholders can make choices about pest management along with disease control irrigation schedules because timely assessments from implemented system. new capabilities generated this empowerment system major yield growth enhance food safety protocols as well promote sustainable farming throughout farms globally.

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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Comparative Evaluation of EEG signals for Mild Cognitive Impairment using Scalograms and Spectrograms with Deep Learning Models

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

Опубликована: Окт. 31, 2024

Electroencephalography (EEG) is a valuable tool for studying brain function and identifying neurological disorders. This study aimed to analyze EEG data using various techniques feature extraction classification. The was preprocessed by applying filters dividing it into epochs. Feature techniques, including Fast Fourier Transform (FFT) in the frequency domain Continuous Wavelet (CWT) time-frequency domain, were applied convert signals scalograms spectrograms. primary objective classify individuals with Mild Cognitive Impairment (MCI) Healthy Controls (HC) spectrograms 2D Convolutional Neural Networks (CNN) Recurrent (CRNN). classification results obtained from epochs of different durations (5 seconds 2 seconds) compared. analysis revealed that CRNN model incorporating achieved highest accuracy 87.79% 5 sec 88.25% demonstrates effectiveness combination deep learning models accurately classifying MCI HC data.

Язык: Английский

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