Analysis of EEG signals with the use of wavelet transform for accurate classification of Alzheimer Disease, Frontotemporal Dementia and healthy subjects using Machine Learning Models

Akanksha; Preety D

doi:https://doi.org/10.54216/FPA.140203

Neutrosophic and Information Fusion | 2023

Efficient Data Fusion Framework for Real-Time Monitoring of Air Pressure Systems in Scania Trucks

Monitoring air pressure systems in heavy-duty vehicles such as Scania trucks is a key driver for operational safety and efficiency in the automotive industry. However, the complex interaction of sensors and data sources makes it difficult to quickly detect potential system failures. This problem is solved in our paper where we present a special-purpose data fusion framework for real-time monitoring of Scania trucks’ air pressure systems. To achieve this, PCA is used to reduce the size of the dataset followed by a voting classifier which combines diverse models such as Decision Trees, Random Forests, Naive Bayes, and Linear Regression using ensemble learning. In particular, our comparative analysis shows that the Voting Classifier outperforms other ML methods in terms of prediction accuracy. These findings suggest that our fusion framework can be utilized for the early detection of air pressure anomalies in heavy-duty vehicles enhancing their safety record.

groups

Durdona Bakhodirova mail

link https://doi.org/10.54216/NIF.020203

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new

Neutrosophic and Information Fusion | 2023

A Comprehensive Approach to Asset Degradation Modeling via Sensory Data Fusion for Remaining Useful Life Prediction

For effective management of assets, accurate forecasting for system failures is necessary. Sensory data fusion is a viable option to predict Remaining Useful Life (RUL) in assets by combining multiple data sources for improved prediction capabilities. This research paper aims at predicting RUL integrating various sensory data streams. Using Artificial Neural Networks (ANN), this research aims at synthesizing, learning from, and fusing information emanating from different sensors leading to accurate RUL estimations required for proactive maintenance strategies. The methodology in this study involves the use of ANN architectures for processing multivariate time-series data collected from sensors. By iterative training, the ANN captures complex relationships within the data allowing the integration of different information sources thus aiding in RUL predictions. Through the synthesis of sensory data by the ANN model, promising results have been achieved in predicting RUL. The model effectively learns from multiple sources demonstrating enhanced accuracy in estimating remaining operational cycles before asset failure.

groups

Durdona Davletova mail

link https://doi.org/10.54216/NIF.020204

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new

Neutrosophic and Information Fusion | 2023

Inertial Information Fusion for Improved Vehicular Perception Systems

Advancing the capabilities of vehicle perception systems, through the fusion of sensor data is a pursuit in the field of vehicles and intelligent transportation systems. This study explores the complexities involved in enhancing vehicle perception with the goal of tackling the challenges associated with interpreting various sensor inputs to gain an understanding of the environment. By utilizing techniques that fuse information and clustering methodologies this research aims to identify driving scenarios based on patterns observed in sensor data allowing for a nuanced analysis of environmental variations. Additionally a classification framework using Convolutional Neural Networks (CNNs) is employed to accurately classify types of road surfaces demonstrating how deep learning models can effectively utilize sensor representations for environmental characterization. The methods employed encompass clustered data fusion, where K means clustering is utilized to segment sensor data into scenarios and CNN classification, for accurate identification of road surface types. The study achieved impressive findings using these methodologies, exhibiting unique clusters typical of various driving circumstances based on sensor aggregation and demonstrating the CNN's capacity for accurate road surface classification.

groups

Durdona Uktamova mail

link https://doi.org/10.54216/NIF.020205

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2024

Neutrosophic Enhancement of YOLO-MD Algorithm for Automated Metal Surface Micro Defect Detection

To achieve automation of defect detection, the metal surface micro defect detection algorithm YOLO-MD is proposed. From the perspective of object detection, YOLOv5s is selected as the backbone algorithm and the SPD-Conv module is added to reduce feature loss caused by ordinary convolutional downsampling, improve the adaptability of low-resolution images, and improve the accuracy of small object detection. Using the MPDIoU loss function to accelerate model convergence and improve detection accuracy. Considering the small size of the dataset, data augmentation methods were adopted. After model training, mAP50-95 improved by 0.02 compared to YOLOv5, which has high real-time and robustness and can more effectively detect metal surface micro defects.

groups

Li Jiao mail -

Muhammad Irsyad Abdullah mail

link https://doi.org/10.54216/IJNS.230225

Volume & Issue

Vol. Volume 23 / Iss. Issue 2

Details open_in_new

Journal of Sustainable Development and Green Technology | 2024

Leveraging Business Intelligence for Enhanced Green Financial Practices in Advanced Corporations

The research aims to fill a gap in the current sustainability strategies by investigating how business intelligence can be integrated into advanced companies to improve green financial practices. We will apply our proposed framework to Mutual Funds and Exchange-Traded Funds (ETFs) as we recognize the need for environmentally responsible financial decisions. Our study uses statistical analysis and predictive modelling with Random Forest and Ordinary Least Squares based on a comprehensive dataset obtained from Yahoo Finance. The results, presented through sector distributions, risk ratings, and distribution by category, provide detailed insights into the multifaceted impacts of business intelligence. Our findings indicate that the suggested framework optimises financial decisions and emphasizes the importance of customized approaches across different financial instruments. This study provides a valuable roadmap for practitioners, policymakers, and researchers navigating the changing landscape of environmentally responsible financial strategies in an era where advanced corporations grapple with the complexities of sustainable finance.

groups

Mohamed eassa mail -

Nariman A. Khaliel mail

link https://doi.org/10.54216/JSDGT.040102

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

Journal of Sustainable Development and Green Technology | 2024

A Framework for Sustainable and Green Finance Through Effective Trading Portfolio Management

This research paper responds to the growing global demand for environmentally and socially responsible financial practices by outlining a strong framework for incorporating sustainable and green finance into effective trading portfolio management. The study acknowledges the current difficulties of reconciling financial goals with sustainability criteria and uses a methodological approach that includes risk-sensitive asset allocation, mean-variance optimization, and strategic maximization of the Sharpe ratio. By carefully examining and analyzing this research, it explores the complex dynamics of sustainable finance, thus providing a holistic understanding of how financial success is related to environmental and social responsibility. The findings of this study provide important insights into ongoing discussions on responsible investment strategies, thereby giving investors and policymakers a guide on how to align their financial objectives with sustainable development imperatives.

groups

Mohamed Elkholy mail -

Ahmed A. El-Douh mail

link https://doi.org/10.54216/JSDGT.040103

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2024

Neutrosophic Fuzzy Logic based SVM approach for Enhanced Skin Cancer Prediction

In this study, a thorough methodology is used to present a unique way for improving skin cancer prediction accuracy. The research uses sophisticated preprocessing methods, such as the Frost filter for noise reduction and histogram equalization for contrast enhancement, to boost contrast on dermoscopic pictures from various sources, using an ISIC 2020 dataset. These actions greatly raise the dermoscopic pictures’ overall quality and usefulness for diagnosis. Utilizing labeled data for training, we offer a Fuzzy-based C-means clustering technique based on Neutrosophic Logic during the segmentation phase. In order to overcome ambiguities in skin lesion segmentation, the neutrosophic set—a groundbreaking idea in philosophy—is used. The suggested model enhances the accuracy of segmentation by modifying the neutrosophic set functions. For precise prediction, the approach combines Support Vector Machine (SVM) classification with Histogram of Oriented Gradient (HOG) feature extraction. While SVM, a supervised learning algorithm, diagnoses skin lesions based on the collected features, HOG features capture gradient information. To improve object recognition and classification, the HOG-SVM architecture is made to methodically collect and quantify essential information using dermoscopic pictures. The use of Neutrosophic Fuzzy Logic, which combines the benefits of fuzzy clustering with neutrosophic sets to produce more precise and nuanced predictions, sets the suggested method apart. The integration of different approaches into a holistic solution for skin cancer prediction is what makes the proposed study innovative. Findings and performance analysis show of the HOG-SVM method exhibits an outstanding accuracy of 98.69%, outperforming LR, KNN, and GNB methods. Python software is used to accomplish the suggested approach. This discovery opens up a possible path for better skin cancer diagnosis and advances the rapidly developing fields of dermatology and medical image processing.

groups

Khaled Bedair mail -

Ahmed H. Samak mail -

Kottakkaran Sooppy Nisar mail -

Ali Elrashidi mail -

Amina Toumi mail -

Shawkat Alkhazaleh mail -

Afrah S. Albalawi mail -

Rasha M. Abd El-Aziz mail

link https://doi.org/10.54216/IJNS.230226

Volume & Issue

Vol. Volume 23 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2024

A Novel Approach for Minimizing Response Time in IoT using Adaptive Algorithm

This research offers four work and computer tool setups. The dynamic Resource Allocation Algorithm is crucial to the system.  This lets you manage changing supply. Once the PWMA knows how much work is coming up, it may divide resources and plan. The Load Balancing Algorithm (LBA) distributes work evenly to avoid over- or under-utilization and it also provides access content faster via the Adaptive Caching Algorithm (ACA). The proposed system surpasses the top alternative in several domains, such as data transmission, reaction time, energy conservation, load distribution effectiveness, and recovery time from failures. This is because the suggested solution incorporates many disparate approaches. Graphs and charts are visual representations that effectively illustrate the similarities and differences between the two methodologies. The hybrid technique is especially beneficial when the workload is unpredictable and prone to fluctuations. To do this, it instructs you on the fundamentals of efficient and adaptable computer resource management.

groups

Hitesh Kumar Sharma mail -

Samta Jain Goyal mail -

Sumit Kumar mail -

Abhishek Kumar mail

link https://doi.org/10.54216/FPA.140201

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2024

Enhanced Recognition of Handwritten Marathi Compound Characters using CNN-SVM Hybrid Approach

This study presents a hybrid recognition system for multi-class compound Marathi characters, which addresses the problem of handwritten Marathi character recognition. The methodology efficiently bridges the gap between feature extraction and classification by integrating a Convolutional Neural Network (CNN) and Support Vector Machine (SVM). The first step is gathering and preprocessing a wide range of handwritten Marathi compound characters that are written in different styles. Using conventional supervised learning methods, the CNN is trained on this dataset, paying special attention to data augmentation and validation in order to reduce overfitting. High-level features taken from the final fully connected layer of the CNN are fed into an SVM classifier in the next step. By using these features in its training, the SVM improves prediction accuracy. For multi-class classification, the one-vs-all method is used. The hybrid CNN-SVM algorithm demonstrates its effectiveness in the crucial phases of feature extraction and classification by identifying handwritten compound Marathi characters with remarkable accuracy. Evaluation metrics, such as accuracy, precision, recall, F1-score, and confusion matrix analysis, are employed in the process of evaluating the effectiveness of the model. This assessment is carried out on a different testing dataset, offering a thorough examination of the model's functionality. The proposed algorithm demonstrates its superior performance and potential for improved character recognition by achieving training accuracy of 98.60% and validation accuracy of 97.69%. The development of handwriting recognition systems has benefited greatly from this research, especially when it comes to intricate scripts like Marathi. The suggested hybrid algorithm shows encouraging outcomes and has a great deal of potential for use in document processing, natural language comprehension, and character recognition in languages that use the Marathi script. Subsequent efforts will centre on refining the model and investigating ensemble methods to increase the robustness and accuracy of recognition.

groups

Ashwini Patil mail -

Puneet Dwivedi mail

link https://doi.org/10.54216/FPA.140202

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2024

Analysis of EEG signals with the use of wavelet transform for accurate classification of Alzheimer Disease, Frontotemporal Dementia and healthy subjects using Machine Learning Models

Dementia is a brain disorder, if not prevented; takes the form of various types of diseases that have no cure yet. Accurate classification of multiple types of dementia diseases is required to provide proper medication to the patient so that growth of that disease can be delayed. This study analyzes EEG signal for the classification of multiple dementia diseases such as Alzheimer’s disease (AD), Fronto-temporal dementia (FTD) and control normal (CN) subjects using machine learning (ML) algorithms. Each of the 19 channels of EEG dataset is analyzed separately in this work to perform the classification. Combination of parameters like Hjorth Activity, Mobility and Complexity along with kurtosis value of the data has been extracted in time-frequency domain for each EEG frequency band (Delta, Theta, Alpha, Beta and Gamma) is applied to the machine learning algorithms. This research is focused on classification of multiple dementia classes (ADvsFTD) as well as three-way (ADvsFTDvsCN) classification. This research is validated using public EEG dataset with 23 participants of each category. Best classification result is achieved using random forest classifier and leave-one-subject-out (LOSO) cross validation method. The three-way classification i.e., ADvsCNvsFTD achieved best accuracy of 75.29%, whereas binary classifications i.e. ADvsCN, ADvsFTD and CNvsFTD achieved best accuracy of 88.90%, 88.44% and 84.10% respectively. The proposed framework shows better results than existing work on dementia classification using machine learning. The results obtained from proposed framework showed that combination of EEG frequency band features can be utilized for the classification of multiple dementia diseases with greater accuracy.

groups

Akanksha Parihar mail -

Preety D Swami mail

link https://doi.org/10.54216/FPA.140203

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new