Lung nodule growth measurement and prediction using Multi scale - 3 D- UNet segmentation and shape variance analysis

Sathyamoorthy; Ravikumar

doi:https://doi.org/10.54216/FPA.160104

International Journal of Neutrosophic Science | 2024

Modeling of Improved Sine Trigonometric Single Valued Neutrosophic Information based Air Pollution Prediction Approach

Industrialization and urbanization air is getting polluted due to human activities. CO, NO, C6H6, etc., are the major air pollutants. The focus of air pollutants in ambient air is controlled by the climatological parameters including wind direction, atmospheric speed of wind, temperature, and humidity. Air pollution prediction is a critical sector where machine learning (ML) technique plays a major role. Its main purpose is to tackle and understand the damaging effects of air pollutants on the environment and human health. By using a range of ML techniques such as neural networks, regression, and decision trees, we could analyze historical data on air quality alongside geographical and meteorological factors. This allows us to design model that could detect patterns and predict pollution levels. By taking proactive measures such as providing timely alerts to the public, adjusting controls on emissions, and, implementing strategies to reduce pollution, we can work towards creating healthier and cleaner environments. Embracing the potential of artificial intelligence (AI) in air pollution prediction empowers us to protect the well-being of our communities and make informed decisions. Therefore, this study develops an Improved Sine Trigonometric Single Valued Neutrosophic Information based Air Pollution Prediction (ISTSVNI-APP) approach. The major objective of the ISTSVNI-APP technique is to exploit AI concepts with neutrosophic sets (NS) models for the forecasting of air pollution. To do so, the ISTSVNI-APP technique makes use of min-max normalization as the initial preprocessing step. For predicting air pollution, the ISTSVNI-APP technique uses STSVNI approach. To improve the performance of the ISTSVNI-APP technique, modified crow search algorithm (MCSA) is used for the parameter tuning of the STSVNI system. The performance evaluation of the ISTSVNI-APP method is verified utilizing benchmark dataset. The experimental outcomes stated that the ISTSVNI-APP technique gains better performance in predicting air pollution

groups

Afrah Al-Bossly mail -

Shoraim M. H. A. mail -

Amal O. A. Al magdashi mail -

Badr Eldeen A. A. Abouzeed mail

link https://doi.org/10.54216/IJNS.240208

Volume & Issue

Vol. Volume 24 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2024

Leveraging Neutrosophic TOPSIS with Artificial Intelligence-Driven Tropical Cyclone Intensity Estimation for Weather Prediction

Tropical cyclones (TCs) are powerful, low-pressure weather systems attributed to heavy rainfall and strong winds, and have often resulted in extensive damage to coastal regions. TC intensity prediction, an essential aspect of meteorological forecasting, includes evaluating the strength of the storm to facilitate disaster preparedness and alleviate possible risks. Classical approaches for the prediction of TC intensity rely on different oceanic and atmospheric parameters, but the incorporation of artificial intelligence (AI) approaches, especially those leveraging image data, provides positive breakthroughs in efficiency and accuracy. By harnessing AI techniques like deep learning architectures and convolutional neural networks (CNNs), meteorologists could analyze radar data, satellite imagery, and other visual inputs to distinguish complicated patterns indicative of intensity changes and TC development. This combination of weather science and AI-driven image analysis enables more timely and precise predictions and improves our understanding of TC dynamics, eventually fortifying protection against the impacts of formidable storms. This article introduces Neutrosophic TOPSIS with Artificial Intelligence Driven Tropical Cyclone Intensity Estimation (NTOPSIS-TCIE) technique for Weather Prediction. The presented NTOPSIS-TCIE technique determines the intensities of the TC which in turn helps to forecast weather. In the NTOPSIS-TCIE technique, median filtering (MF) approach is used to remove the noise in the images. In addition, the features are extracted using deep convolutional neural network (CNN) model. To enhance the performance of the CNN model, Harris Hawks Optimization (HHO) algorithm is applied. Finally, the NTOPSIS model is employed for the prediction of TC intensities. The performance of the NTOPSIS-TCIE technique can be studied using TC image dataset and the results signify its promising results over other models

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Fuad S. Al-Duais mail -

Shoraim M. H. A. mail -

Amal O. A. Al magdashi mail -

Badr Eldeen A. A. Abouzeed mail

link https://doi.org/10.54216/IJNS.240209

Volume & Issue

Vol. Volume 24 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

Development of Digital Twin Technology in Hydraulics Based on Simulating and Enhancing System Performance

DT digital twin technology has become an essential tool in hydraulic systems. It not only offers a virtual representation of the actual plant, but also real-time monitoring and optimization of that same machinery. Digital Twin (DT) technology has become a cornerstone in the optimization of industrial processes, particularly in the domain of hydraulic systems. For example, this research aims to use digital twin technology to detect and fix leaks in hydraulic systems. By integrating advanced simulation algorithms for accurate leak detection and performance enhancement, this study presents a comprehensive framework. Combining techniques developed from both data-driven and state-of-the-art optimization methods our approach looks to change how leaks are detected in hydraulics. Our test introduces a comprehensive framework that not only accurately identifies leaks but also employs advanced simulation algorithms for subsequent performance enhancement. By bringing together data-driven insights and cutting-edge optimization methods, our work at the frontier of revolutionizing leak detection in hydraulic systems.

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R. Uma Maheshwari mail -

D. Jayasutha mail -

Indu Nair V. mail -

R. Senthilraja mail -

Subash Thanappan mail -

Ramya S. mail

link https://doi.org/10.54216/JCIM.130204

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2024

Rare and Dense Sets in Fuzzy Neutrosophic Topological Spaces

The purpose of the current paper is study some new concept of sets and called fuzzy neutrosophic rar and fuzzy neutrosophic dense sets in fuzzy neutrosophic opology and investigate some properties. In fact, the subject of fuzzy neutrosophic sets is already conducted by F. M. Mohammed et.al. [1-9]. However, the current study illustrates number of notable examples to shed the light on some novel attributes of recently established terms, as well as showing related interactions among these researches.

groups

Sara Q. khamis mail -

Fatimah M. Mohammed mail

link https://doi.org/10.54216/IJNS.240210

Volume & Issue

Vol. Volume 24 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2024

A New Neutrosophic Extended Rayliegh Distribution for Enhanced Productivity and Efficiency Across Industrial Sectors: A case study of Al-Kharj region

This paper introduces a new statistical distribution called the Neutrosophic Extended Rayleigh Distribution (NERD), which is specifically developed to handle uncertainty commonly found in industrial applications. We conduct a comprehensive examination of the statistical characteristics of NERD, including important measures such as the quantile function, moments, moment generating function, mean deviation, skewness, kurtosis, reliability measures, uncertainty measures, distributions of order statistics, and L-moments. Parameter estimation is conducted by maximum-likelihood estimation within a neutrosophic framework, guaranteeing resilient inference in practical situations. Through the application of NERD to actual industrial datasets, we evaluate its adaptability and efficiency in simulating industrial processes. A real case study of Al-Kharj region demonstrates the higher performance of NERD. This research highlights the capacity of NERD to greatly improve productivity and efficiency in several industrial sectors.

groups

Fuad S. Al-Duais mail -

Walid Aydi mail

link https://doi.org/10.54216/IJNS.240211

Volume & Issue

Vol. Volume 24 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

Filtering Big Data with Optimized Hybrid Algorithm for IoT-Based Data Selection

Data management across servers has grown problematic because of technological advancements in data processing and storage capacities. Data that is neither organized nor labelled adds an additional layer of difficulty to the storing and retrieving processes. This data, which is not tagged, requires analytic techniques that are more powerful and time efficient. Clustering has long been regarded as one of the most effective methods for managing large amounts of data; nonetheless, larger volumes can lead to unexpectedly poor accuracy when using conventional clustering methodologies. In this study, we suggest the use of a novel framework for the clustering of large amounts of data. The preprocessing stage is one of the most important parts in the data cleansing process; hence, a global stop-word list is used to filter the contents of the files before sending them on to the cluster distribution stage. A meta-heuristic focused Genetic Algorithm (GA) is utilized to eradicate the redundant information present in the datasets. In addition to the generalized attributable fitness function, an attribute-based innovative fitness function (f) is being developed. To determine how well proposed method performs, it is compared to a variety of alternative clustering approaches. When comparing the distributions of clusters for the purpose of evaluation, the Standard Error (SE), root mean squared error (RMSE), and corrected R squared error are all computed.

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Sarvesh Kumar mail -

Satyajee Srivastava mail -

Surendra Kumar mail -

Arun Kumar Saini mail -

Neeraj Verma mail -

Dhiraj Kapila mail

link https://doi.org/10.54216/JISIoT.120211

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2024

Improving Arabic Spam classification in social media using hyperparameters tuning and Particle Swarm Optimization

Online social networks continue to evolve, serving a variety of purposes, such as sharing educational content, chatting, making friends and followers, sharing news, and playing online games. However, the widespread flow of unwanted messages poses significant problems, including reducing online user interaction time, extremist views, reducing the quality of information, especially in the educational field. The use of coordinated automated accounts or robots on social networking sites is a common tactic for spreading unwanted messages, rumors, fake news, and false testimonies for mass communication or targeted users. Since users (especially in the educational field) receive many messages through social media, they often fail to recognize the content of unwanted messages, which may contain harmful links, malicious programs, fake accounts, false reports, and misleading opinions. Therefore, it is vital to regulate and classify disturbing texts to enhance the security of social media. This study focuses on building an Arabic disturbing message dataset extracted from Twitter, which consists of 14,250 tweets. Our proposed methodology includes applying new tag identification technology to collected tweets. Then, we use prevailing machine learning algorithms to build a model for classifying disturbing messages in Arabic, using effective parameter tuning methods to obtain the most suitable parameters for each algorithm. In addition, we use particle swarm optimization to identify the most relevant features to improve the classification performance. The results indicate a clear improvement in the classification performance from 0.9822 to 0.98875, with a 50% reduction in the feature set. Our study focuses on Arabic spam messages, classifying spam messages, tuning effective parameters, and selecting features as key areas of investigation.

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Amr Mohamed El Koshiry mail -

Entesar H. Ibraheem Eliwa mail -

Ahmed Omar mail

link https://doi.org/10.54216/FPA.160101

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

Automated Gesture Recognition Using Zebra Optimization Algorithm with Deep Learning Model for Visually Challenged People

Gesture recognition for visually challenged people plays a vital role in improving their convenience and interaction with digital gadgets and environments. It includes improvement of systems that permit them to relate with digital devices by using hand actions or gestures. To improve user-friendliness, these systems select in-built and effortlessly learnable gestures, often integrating wearable devices prepared with sensors for precise detection. Incorporating auditory or haptic feedback devices offers real-time cues about achievement of familiar gestures. Machine learning (ML) and deep learning (DL) methods are useful tools for accurate gesture detection, with customization choices to accommodate individual preferences. In this view, this article concentrates on design and development of Automated Gesture Recognition using Zebra Optimization Algorithm with Deep Learning (AGR-ZOADL) model for Visually Challenged People. The AGR-ZOADL technique aims to recognize the gestures to aid visually challenged people. In the AGR-ZOADL technique, the primary level of data pre-processing is involved by median filtering (MF). Besides, the AGR-ZOADL technique applies NASNet model to learn complex features from the preprocessed data. To enhance performance of NASNet technique, ZOA based hyperparameter procedure performed. For gesture recognition process, stacked long short term memory (SLSTM) model is applied. The performance validation of AGR-ZOADL technique carried out using a benchmark dataset. The experimental values stated that AGR-ZOADL methodology extents significant performance over other present approaches

groups

Mohammed Basheri mail

link https://doi.org/10.54216/FPA.160102

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

EEG-Based Brain-Computer Interfaces Using Gazelle Optimization Algorithm with Deep Learning for Motor-Imagery Classification

Brain-computer interface (BCI) is a procedure of connecting the central nervous system to the device. In the past few years, BCI was conducted by Electroencephalography (EEG). By linking EEG with other neuro imaging technologies like functional Near Infrared Spectroscopy (fNIRS), promising outcomes were attained. An important stage of BCI is brain state identification from verified signal properties. Classifying EEG signals for motor imagery (MI) is a common use in the BCI system. Motor imagery includes imagining the movement of certain body parts without executing the physical movement. Deep Artificial Neural Network (DNN) obtained unprecedented complex classification outcomes. Such performances were obtained by an effective learning algorithm, improved computation power, restricted or back-fed neuron connection, and valuable activation function. Therefore, this study develops a Gazelle Optimization Algorithm with Deep Learning based Motor-Imagery Classification (GOADL-MIC) technique for EEG-Based BCI. The GOADL-MIC technique aims to exploit hyperparameter-tuned DL model for the recognition and identification of MI signals. To achieve this, the GOADL-MIC model initially undergoes the conversion of one dimensional-EEG signals into 2D time-frequency amplitude one. Besides, the EfficientNet-B3 system is applied for the effectual derivation of feature vector and its hyperparameters can be selected by using GOA. Finally, the classification of MIs takes place using bi-directional long short-term memory (Bi-LSTM). The experimentation result analysis of the GOADL-MIC method is verified utilizing the BCI dataset and the results demonstrate the promising results of the GOADL-MIC algorithm over its counter techniques

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P. Radhakrishnan mail -

Abullaıs Nehal Ahmed mail -

K. Kalaiarasi mail -

Koppisetti Giridhar mail -

S. Thenappan mail

link https://doi.org/10.54216/FPA.160103

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

Lung nodule growth measurement and prediction using Multi scale - 3 D- UNet segmentation and shape variance analysis

In this work, a statistical model is constructed to forecast the possibility of lung nodules that may grow in the future. This study segments all potential lung nodule candidates using the Multi-scale 3D UNet (M-3D-UNet) method. 34 patients' CT scan series yielded an average of approximately 600 nodule candidates larger than 3 mm, which were then segmented. After removing the arteries, non-nodules and 3D shape variation analysis, 34 actual nodules remained. On actual nodules, the nodule growth Rate (NGR) was calculated in terms of 3D-volume change. Three of the 34 actual nodules had RNG values greater than one, indicating that they were malignant. Compactness, Tissue deficit, Tissue excess, Isotropic Factor and Edge gradient were used to develop the nodule growth predictive measure.

groups

Sathyamoorthy k. mail -

Ravikumar S. mail

link https://doi.org/10.54216/FPA.160104

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new