Orthogonal Semiderivations on Semiprime Γ-Semirings

Abdulrahman Hameed Majeed; Sundus Taha Kathem

doi:https://doi.org/10.54216/PMTCS.040103

International Journal of Advances in Applied Computational Intelligence | 2024

Enhancing Healthcare Data Classification: Leveraging Machine Learning on ChatGPT-Generated Datasets

With the large-scale language methods namely ChatGPT, there is a chance to explore the use of machine learning (ML) methods on ChatGPT-generated data for classifying healthcare data.  Healthcare data classification gains more significance in extracting and organizing useful insights from the huge volume of medical data available. The ChatGPT-generated data has realistic and different healthcare-based text datasets that can be applied to training classification methods. ML approaches include supervised learning methods as support vector machines (SVMs), and random forests (RF), which can be implemented for classifying the healthcare data. The methods were trained on the ChatGPT-generated data that can be carefully validated and labelled with suitable classes related to the healthcare field. With this motivation, this article presents an automated healthcare data classification-using barnacles mating optimizer with a pyramid neural network (AHDC-BMOPNN) technique. The presented AHDC-BMOPNN technique examines the healthcare data effectually using an ML model with a feature selection process. Primarily, the AHDC-BMOPNN technique exploits min-max data normalization for scaling the input dataset. In addition, the butterfly optimization algorithm-based feature selection (BOA-FS) method is deployed for the selection of optimum feature subset. In this work, the PNN algorithm was utilized for the classification of medical data. Ultimately, the BMO-based hyperparameter tuning process takes place to boost the overall classifier results of the PNN technique. The empirical findings of the AHDC-BMOPNN approach was validated on ChatGPT generated dataset. The simulation values highlight that the AHDC-BMOPNN method and the diverse healthcare text data generated by ChatGPT enhance the ability to extract valuable insights and organize medical information effectively.

groups

Basheer Abd Al Rida Sadiq mail -

Murhaf Obaidi mail

link https://doi.org/10.54216/IJAACI.050203

Volume & Issue

Vol. Volume 5 / Iss. Issue 2

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2024

Quantum Sparrow Swarm Optimization with Deep Learning Enabled Deception Detection on Facial Micro Expressions

Deception detection means finding whether an individual is lying or being deceptive depending on cognitive cues, and various behavioural, or physiological. It is a significant domain of research with applications in social psychology, law enforcement, and security. Deception detection relevant to microexpressions includes examining these subtle facial cues for determining whether an individual is being deceptive or lying. Microexpressions can deliver significant cues to detect deception. Deep learning (DL) and Machine learning (ML) models were utilized for finding micro-expressions and are trained for differentiating deceptive statements from genuine ones. Still, it necessitates a diverse and large dataset of video recordings in addition to careful tuning and pre-processing of the DL approach. So, this article presents an Automated Deception Detection on Facial Microexpressions using Improved Sparrow Swarm Optimization with Deep Learning (ADDFM-ISSODL) method. The proposed ADDFM-ISSODL algorithm examines facial micro-expressions effectively for detection of deceptive behaviour. To complete this, developed ADDFM-ISSODL model uses a Gaussian filtering (GF) approach for pre-processing. Besides, ADDFM-ISSODL technique employs MobileNetv3 model for feature extraction and the hyper parameter tuning procedure performed using ISSO algorithm. The ISSO approach was designed by the integration of the standard SSO approach with the quantum evolutionary algorithm (QEA). For deception detection, a probabilistic neural network (PNN) classifier was employed. At last, grasshopper optimization algorithm (GOA) was implemented for parameter tuning of PNN method. The performance validation of ADDFM-ISSODL system tested utilizing facial expression dataset. The simulation outcome stated the greater results of ADDFM-ISSODL algorithm over other methodologies.

groups

Khadija Ben Othman mail

link https://doi.org/10.54216/IJAACI.050204

Volume & Issue

Vol. Volume 5 / Iss. Issue 2

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2024

Gorilla Troops Optimizer with Deep Learning-based Multi-Criteria Decision Making for Traffic Analysis in V2X Networks

Multi-criteria decision-making (MCDM) is employed for analyzing traffic in a Vehicle-to-Everything (V2X) network. V2X suggests communication among vehicles and other entities, containing pedestrians, infrastructure, and other vehicles. Traffic analysis and management in V2X networks need effectual decision-making approaches, which assume several conditions. MCDM contains estimating and choosing alternatives depending on several conditions or objectives. In the context of traffic analysis in V2X networks, MCDM algorithms are employed for decision-making concerning traffic flow optimizer, resource allocation, route planning, and congestion management. Deep learning (DL) approaches are trained to analyze massive counts of data gathered from several sources from the V2X network. These sources contain traffic sensors, GPS data, vehicle-to-infrastructure (V2I) communication, and historical traffic designs. By processing this data, DL approaches extract useful insights and create informed decisions depending on various conditions. Therefore, this article proposes a gorilla troops optimizer with deep learning-based MCDM for traffic analysis (GTODL-MCDMTA) technique in the V2X network. The purpose of the GTODL-MCDMTA algorithm is to identify the traffic flow prediction for improving route planning and resource allocation with the consideration of various factors into account. In the presented GTODL-MCDMTA technique, the input data is pre-processed to remove noise and normalize it for analysis. Next, the GTO algorithm is used for the feature selection process. Besides, the deep extreme learning machine (DELM) model is used for the forecast of traffic movement. Finally, the seeker optimization algorithm (SOA) has been utilized for the parameter tuning of the DELM technique. A brief set of simulation outcomes can be applied to emphasize the promising outcomes of the GTODL-MCDMTA technique. The experimental outcome demonstrates the efficiency and efficiency of the GTODL-MCDMTA approach in handling the complexity and dynamic nature of V2X network traffic analysis.

groups

Djamal Lhiani mail -

Othman Al-basheer mail

link https://doi.org/10.54216/IJAACI.050205

Volume & Issue

Vol. Volume 5 / Iss. Issue 2

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2024

Optimal Deep Learning-Based Image Classification for IoT-Enabled UAVs in Remote Sensing Applications

Unmanned Aerial Vehicles (UAVs), together with Internet of Things (IoT) technology, have emerged as robust tools for remote sensing (RS) and data collection in different sectors, including environmental monitoring, agriculture, and disaster management. The incorporation of data from UAVs with IoT sensors on the ground can provide a holistic view of the environment, improving the quality of input for image classification. Deep learning (DL) models-based image classification is a key component of IoT-assisted UAVs, transforming them from data collection tools into intelligent decision-making platforms. Especially, Convolutional Neural Networks (CNNs) can automatically recognize objects, patterns, and anomalies in images captured by UAVs. Therefore, the study presents an automated image classification with the Tyrannosaurus optimization algorithm using deep learning (AIR-TROADL) method on the IoT-aided UAV network. The AIR-TROADL technique aims to examine the UAV images for the identification and classification of images into distinct categories. In the projected AIR-TROADL method, an enhanced ShuffleNet model is exploited for feature extraction. Besides, the hyperparameter tuning of enhanced ShuffleNet model can be performed by using TROA, which in turn boosts the classification performance. Finally, the classification of images takes place using the attention-based gated recurrent unit (AGRU) model. A series of simulations have been conducted to exhibit the promising outcome of the AIR-TROADL technique. The comparative outcomes highlighted that the AIR-TROADL method reaches high efficiency over its recent approaches in terms of distinct measures.

groups

Sanjar Mirzaliev mail -

Samandarboy Sulaym mail

link https://doi.org/10.54216/IJAACI.060101

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2024

Enhanced Wetland Classification using Deep Learning based Fusion Approach on Multi-Source Remote Sensing Images

Accurate remote sensing (RS) monitoring of wetland ground objects is an enormous importance for ecological preservation. Wetland classification on multi-source remote sensing images (MS-RSI) includes leveraging data from different sensors for accurately describing and categorizing wetland regions. This method normally incorporates data from infrared, radar, and optical sensors to take a wide-ranging view of wetland features. Advanced image processing methodologies, comprising machine learning (ML) approaches are often implemented for analyzing these multi-source images as well as recognizing spectral and spatial patterns indicative of wetland characteristics. The interaction of various RS data increases the accuracy and robustness of wetland classification models, allowing a more complex analysis of wetland ecosystems and aiding environmental observation, conservation, and control measures. To accomplish effective training for wetland mapping through the RS, it is essential for a significant training data that comprises a numerous array of class variants. In this article, we propose an Enhanced Wetland Classification using a Deep Learning based Fusion Approach (EWC-DLFA) on MS-RSI. The proposed EWC-DLFA technique examines the MS-RSI for wetland classification using the DL model which can be used for other land cover classification types. To accomplish this, the EWC-DLFA technique utilizes the data from multiple sources such as Sentinel-1 (SAR), Landsat-8, Sentinel2 (multi-spectral), and digital elevation model (DEM). In the presented EWC-DLFA technique, a deep convolutional neural network-based EfficientNetB-5 model can be applied for the extraction of features from the multi-source images. For increasing the performance of the EfficientNet-B5 model, the marine predators algorithm (MPA) based hyper parameter tuning process can be applied. Finally, an ensemble of three ML classifiers such as extreme learning machine (ELM), multilayer perceptron (MLP), and gradient boosting machine (GBM) are used to classify the wetland into different types such as fen, bog, marsh, swamps, and upland. The performance of the EWC-DLFA technique can be validated using a large set of simulations. The resultant values pointed out that the EWC-DLFA technique reaches better performance over other models on wetland classification.

groups

Bahromjon Urmanov mail -

Maha Ibrahim mail

link https://doi.org/10.54216/IJAACI.060102

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2024

A Review on the Neutrosophic Number Theory Based Cryptography and Neutrosophic Public Key Crypto-Systems

The main objective of this chapter is to introduce the concept of neutrosophic number theory, and to demonstrate its potential applications in modern cybernetic systems. The chapter will also explore the possibility of utilizing neutrosophic theory to enhance existing security algorithms, including a detailed explanation of the neutrosophic version of the RSA algorithm. Furthermore, the chapter will present a novel neutrosophic version of the Diffie-Hellman key exchange algorithm.

groups

Ali Allouf mail

link https://doi.org/10.54216/IJAACI.060103

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2024

Decision-Making Model for Robot Selection Application using Neutrosophic Sets

Robot selection is a crucial process that involves choosing the most suitable robot for a specific task or application. This work provides an overview of the critical criteria for selecting a robot. It emphasizes the importance of evaluating task requirements, payload capacity, workspace and reach, precision and accuracy, speed and cycle time, safety features, programming and control interface, maintenance and reliability, cost and return on investment, integration, and compatibility, and future scalability and flexibility. By carefully considering these criteria, stakeholders can make informed decisions and select a robot that meets their needs, optimizing productivity, efficiency, and safety in various industrial and commercial settings. We used the concept of multi-criteria decision-making to deal with multiple criteria in the robot section. We used the Weighted Euclidean distance-based Approach (WEDBA) to analyze the robot selection criteria and rank the alternatives. The WEDBA method integrated with the neutrosophic set environment. The neutrosophic set used for dealing with uncertainty information.  We used the 11 criteria and 15 options in this study. The main results show the load capacity has the highest weight.

groups

Rozina Ali mail -

Ammar Rawashdeh mail

link https://doi.org/10.54216/IJAACI.060104

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2024

Multi-Criteria Decision-Making Methodology for Sustainable Crop Selection

Choosing the best biomass crop option for producing biofuel requires a decision-making model because of the many factors involved, the subjective nature of human judgement, and the inherent unpredictability. The neutrosophic type 2 is a valuable tool for handling the ambiguous, inconsistent, and uncertain data often appearing in real-world decision-making situations. Therefore, this study aims to provide a new framework for weighted aggregated sum product assessment (WASPAS) that can be used to solve multi-criteria decision-making (MCDM) issues using neutrosophic type 2 data. The criteria weights are computed. The results show the economic factor has the highest importance in all requirements. This study used nine criteria and twenty alternatives. The WASPAS method was used to rank the other options. The sensitivity analysis is performed under different cases to show the stability of the results. The results show the rank is stable under different cases in this study.

groups

Ahmad Khaldi mail -

Murat Ozcek mail

link https://doi.org/10.54216/IJAACI.060105

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2024

On Two New Algorithms for Solving Mixed Integer Linear Programming Problems and Their Applications

  The objective of this paper is to introduce two new algorithms for dealing with mixed integer linear programming problems, where the first method will be applied to get the efficient cut in the standard cutting plane procedure to obtain the same optimal solution. The second method will be applied with many special conditions to get the global solution instead of the local solution by using cutting-plane and other famous algorithms. On the other hand, we compare our results to the other obtained results by applying other algorithms.  

groups

Taher Ahmed Jubbori mail

link https://doi.org/10.54216/IJAACI.060106

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

Pure Mathematics for Theoretical Computer Science | 2024