International Journal of Advances in Applied Computational Intelligence

  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.  

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.

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.

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.

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.

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.