Intelligent Multi-Level Feature Fusion Using Remote Sensing and CNN Image Classification Algorithm

Mustafa; Talib; M. A.; Ali; Thamer A.

doi:https://doi.org/10.54216/JISIoT.090103

Prospects for Applied Mathematics and Data Analysis | 2023

Combining regular solutions of the Schaefer -Ignaczak thermodynamical behaviors relating to the first plane state of elastic strain of the micropolar body subjected to temperature field

The importance of results of this paper consist in supplying new analytical method for solving the Ignaczak tensorial equations, governing the thermodynamical plane state of small elastic strains of the homogeneous, isotropic, micropolar elastic solid of 5 material constants of Eringen-Nowacki type, which shortly called  2D (E-N:5) (Iron plates, copper plates, aluminum plates, .. etc.).  The paper covers the mathematical model of the first plane state of small elastic strains of micropolar homogeneous and isotropic solid, of five material constants, subjected to temperature field, mathematically proposed by Eringen and Nowacki, and shortly called 2D (E-N:5).  In paper, for the 2D (E-N:5) considerable body, we generalize the Schaefer vector method to: I) The Traditional Description of the 2D (E-N:5) considerable body, II) The Ignaczak Description of the 2D (E-N:5) considerable body. Subsequently, these results have important applications in material resistance, plate theory, industry ….etc.    

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Mountajab Al-Hasan mail

link https://doi.org/10.54216/PAMDA.020103

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

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International Journal of Advances in Applied Computational Intelligence | 2022

Deep Learning Model for Early Weed Detection in Agriculture Application

One of the current issues in agriculture is the lack of mechanized weed management, which is why weed detection technologies are so crucial. Detecting weeds is useful because it may lead to the elimination of pesticide usage, which in turn improves the surroundings, human health, and the sustainability of agriculture. As novel algorithms are developed and computer capacity increases, deep learning-based approaches are gradually replacing classic machine learning methods for real-time weed detection. Mixed machine learning designs, which combine the best features of existing approaches, are becoming more popular. So, the goal of this study, present the CNN model for early weed detection. The CNN model is applied to the weed dataset. The CNN model achieved 96% accuracy.

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Abdullah Ali Salamai mail -

Nouran Ajabnoor mail -

Ali Mohammad Khawaji mail

link https://doi.org/10.54216/IJAACI.020103

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

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International Journal of Advances in Applied Computational Intelligence | 2022

Applying Transformer Networks for Accurate Fake News Classification

In the era of information overload and the widespread dissemination of news through various online platforms, the identification and mitigation of fake news have become imperative. This paper presents a comprehensive investigation into the application of Transformer Networks for accurate fake news classification. Transformers, known for their ability to model long-range dependencies and capture contextual information effectively, have demonstrated outstanding performance in natural language processing tasks. Leveraging this strength, we propose a simple but effective approach that employs Transformer-based architectures to discern fake news from genuine information with high precision. In our approach, we explore various techniques, such as attention mechanisms, positional encoding, and self-attention layers, to capture important contextual relationships and optimize the classification process.  Through extensive experimentation, we demonstrate the effectiveness of our approach in accurately identifying and classifying fake news articles. Our proposed model achieves state-of-the-art performance on a public benchmark dataset, surpassing existing approaches.

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Waleed Abd Elkhalik mail

link https://doi.org/10.54216/IJAACI.020104

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

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International Journal of Advances in Applied Computational Intelligence | 2023

Computational Intelligence Approach for Biometric Gait Identification

Gait recognition has gained significant attention in recent years due to its potential applications in various fields, including surveillance, security, and healthcare. Biometric gait identification, which involves recognizing individuals based on their walking patterns, is a challenging task due to the inherent variations in gait caused by factors such as clothing, footwear, and walking speed.  In this paper, we propose a computational intelligence approach for biometric gait identification. Specifically, we integrate an intelligent convolutional model to identify human gaits based on the inertial sensory data captured from the body movement during the human walk. Extensive experiments on two datasets demonstrated that the efficiency of the proposed approach outperforms the existing methods. Our approach has the potential to be used in real-world applications such as surveillance systems and healthcare monitoring, where accurate and efficient identification of individuals based on their gait is crucial.

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Hadeer Mahmoud mail -

Ahmed Abdelhafeez mail

link https://doi.org/10.54216/IJAACI.020105

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

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International Journal of Advances in Applied Computational Intelligence | 2022

Deep Learning Defenders: Harnessing Convolutional Networks for Malware Detection

Malware attacks continue to pose a significant threat to computer systems and networks worldwide. Traditional signature-based malware detection methods have proven to be insufficient in detecting the increasing number of sophisticated malware variants. This has led to the exploration of new approaches, including machine learning-based techniques. In this paper, we propose a novel approach to malware detection using residually connect convolutional networks. We demonstrate the effectiveness of our approach by training CNN on a large dataset of malware samples and benign files and evaluating its performance on a separate test set. Extensive experiments on a public dataset of malware images demonstrated that our model could achieve high accuracy in detecting both known and unknown malware samples. The findings suggest that our residual convolution has great potential for improving malware detection and enhancing the security of computer systems and networks.

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Ahmed Abdelmonem mail -

Shimaa S. Mohamed mail

link https://doi.org/10.54216/IJAACI.010203

Volume & Issue

Vol. Volume 1 / Iss. Issue 2

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International Journal of Advances in Applied Computational Intelligence | 2022

Cardiovascular Diseases Forecasting using Machine Learning Models

Providing medical treatment is a vital part of human existence. Diseases of the heart and blood arteries are often referred to as cardiovascular disease. Predicting cardiovascular illness early on allowed doctors to make adjustments for individuals at high risk, lowering their mortality rate. Machine learning techniques are necessary for making appropriate judgments in the forecasting of cardiac problems because of the vast amounts of medical data available in the healthcare business. Mixed machine-learning approaches are the subject of recent research on unifying these methods. The study proposed machine learning models to predict the heart disease. In order to determine whether or not a person has heart disease, this project presents a model for forecasting. To achieve this, we compare the accuracy of using rules to that of using the Support Vector Machine (SVM), Random forest (RF), and Decision Tree (DT) separately on the dataset.

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Heba R. Abdelhady mail -

Mahmoud M. Ismail mail

link https://doi.org/10.54216/IJAACI.010204

Volume & Issue

Vol. Volume 1 / Iss. Issue 2

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International Journal of Advances in Applied Computational Intelligence | 2022

Unveiling the Power of Convolutional Networks: Applied Computational Intelligence for Arrhythmia Detection from ECG Signals

Arrhythmias are a significant cause of morbidity and mortality worldwide, necessitating accurate and timely detection for effective clinical intervention. Electrocardiogram (ECG) signals serve as invaluable sources of information for diagnosing arrhythmias, but their analysis is complex and demanding. Recent advancements in computational intelligence, particularly Convolutional Networks (CNNs), have demonstrated remarkable capabilities in various signal-processing tasks. In this paper, we unveil the power of CNNs by applying computational intelligence techniques to detect arrhythmias from ECG signals. The proposed methodology involves preprocessing the ECG signals to enhance their quality and remove noise interference. Subsequently, CNN architectures are developed and trained using a large dataset of annotated ECG recordings. The network's structure is optimized to effectively capture the discriminative features present in the ECG signals that characterize diverse types of arrhythmias. Through an extensive evaluation process, the performance of the CNN models is assessed using confusion matrices. Experimental results demonstrate the effectiveness of the applied computational intelligence approach in arrhythmia detection. The CNN model achieves outstanding performance, exhibiting robustness against noise and variations in ECG recording conditions, highlighting its potential for real-world applications.

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Alber S. Aziz mail -

Hoda K. Mohamed mail -

Ahmed Abdelhafeez mail

link https://doi.org/10.54216/IJAACI.010205

Volume & Issue

Vol. Volume 1 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2023

Intelligent Load Identification of Household-Smart Meters Using Multilevel Decision Tree and Data Fusion Techniques

The DTA-LI system's fusion data method is crucial in the monitoring of appliance loads for the purposes of improving energy efficiency and management. Common home electrical devices are identified and classified from smart meter data through the analysis of voltage and current variations, allowing for the measurement of energy usage in residential buildings. A load identification system based on a decision tree algorithm may infer information about the residents of a building based on their energy usage habits. Better power savings rates, load shedding management, and overall electrical system performance are the results of the clusters' ability to capture families' purchasing patterns and geo-Demographic segmentation. The DTA-LI system's fusion data method presents a promising avenue for improving residential buildings' energy performance and lowering their carbon footprint, especially in light of the widespread use of smart meters in recent years.

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Mohammed Hasan Aldulaimi mail -

Ibrahim Najem mail -

Tabarak Ali Abdulhussein mail -

M. H. Ali mail -

Asaad Shakir Hameed mail -

M. Altaee mail -

Hatira Günerhan mail

link https://doi.org/10.54216/JISIoT.090102

Volume & Issue

Vol. Volume 9 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2023

Granulation-Based Data Fusion Approach for a Critical Thinking Worldview Information Processing

Natural computation, motivated by the organic game arrangement, is a knowledge base field that formalizes the measurements seen in living organic entities to plan machine techniques to tackle complicated issues or to plan artificial structures with additional traditional behaviors. Seeable corporations disconnected from natural wonders, reminiscent of mind demonstration, self-association, self-redundancy, Darwinian resistance, self-evaluation, discernment, and granulation, nature is crammed as a supply of motivation to advance competition. Computational devices or frameworks accustomed solve complex problems. The ideal, nature-motivated primary computation models used for such sweetening incorporate artificial neural organizations, spongy reasoning, arduous set, biological process calculations, shape mathematics, DNA registration, artificial life, And granular or insight-based processing. The granulation of information within the granular register is an innate attribute of human thought and therefore the life of thought acted call at regular daily existence. This paper illustrates the importance of normal recording in terms of granulation-based data preparation models, for example, neural organizations, soft and ugly sets, and their hybridization. we have a tendency to emphasize the bio-sensitive inspiration, designing standards, application zones, open scan problems, and testing issues for these models.

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A. Madhuri mail -

Veerapaneni Esther Jyothi mail -

S. Phani Praveen mail -

Mustafa Altaee mail -

Ibrahim N. Abdullah mail

link https://doi.org/10.54216/JISIoT.090104

Volume & Issue

Vol. Volume 9 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2023

Intelligent Multi-Level Feature Fusion Using Remote Sensing and CNN Image Classification Algorithm

The collection of fetures in both multispectral and hyperspectral domains is possible with Hyperspectral Image (HSI). It comprises a vast array of multispectral bands with functional relationships. However, they become more complex when dealing with small samples. To tackle this issue, researchers employed a deep learning convolutionary neural network system (DL-CNN) and implemented a temporal abstraction strategy to grade HSI. This approach is an intelligent multi-level feature fusion that combines the temporal abstraction strategy and DL-CNN for HSI grading. Researchers have introduced the impact of seven separate classifiers in implementing the Location estimation on our broad CNN framework, which plays the shallow CNN model's main training phase. PSO, Adagrad, Plans to implement, Alexnet, Adam, Environmental benefits, and Nadam are the seven distinct remained significantly included in this analysis. A detailed study of the four multispectral remote sensing techniques sets showed the deep CNN system's supremacy followed with the HSI identification AlexNet Optimizer.

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Mustafa Altaee mail -

Talib A. mail -

M. A. Jalil mail -

Ali J. mail -

Thamer A. Alalwani mail

link https://doi.org/10.54216/JISIoT.090103

Volume & Issue

Vol. Volume 9 / Iss. Issue 1

Details open_in_new