Towards Efficient Hyperspectral Object Detection and Classification using Thermal Optimization Algorithm with Deep Learning

Noor Edin Rabeh

Object detection in remote sensing images (RSI) is a main procedure where the purpose is to automatically recognize and categorize certain objects or features from large-scale, remotely developed images like aerial imagery or satellite. This task role a vital play in extracting appreciated data from massive geographical regions, contributing to various applications under several domains namely environmental monitoring, urban planning, agriculture, and disaster management. Recent developments in deep learning (DL) technologies have significantly enhanced the accuracy and efficacy of object detection systems for RS, enabling more precise and automated analysis of various landscapes and facilitating informed decision-making. DL approaches namely convolutional neural networks (CNNs) are exposed to remarkable abilities in learning intricate patterns and features from difficult spatial data, resulting in enhanced accuracy and effectiveness. In this article, we present a Towards Efficient Hyperspectral Object Detection and Classification using Thermal Optimization Algorithm with Deep Learning (HODC-TOADL) system. The objective of HODC-TOADL algorithm is to identify and categorize distinct types of objects that exist in the RSI. In the HODC-TOADL method, an improved Dense Net model is applied to learn the distinct features of the input RSI. Besides, the TOA has been deployed to boost the hyper parameter choice of the Dense Net method. Furthermore, the classification of objects can be carried out by employing of adaptive neurofuzzy inference system (ANFIS). The experimental evaluation of the HODC-TOADL algorithm can be studied on benchmark databases. The experimental values stated that the HODC-TOADL algorithm reaches effective classification performance compared to recent DL models.

Doi: https://doi.org/10.54216/IJAACI.060201

Vol. 6 Issue. 2 PP. 1-15, (2024)

Deep Learning Driven Automated Red Palm Weevil Detection Using Sparrow Search Optimization

Narek Badjajian , Warshine Barry

In recent decades, Red Palm Weevils (RPW) have been demonstrated as a harmful pest of palm trees worldwide, predominantly in the Middle East. The RPW is produced massive damage to several palm varieties. Primary detection of the RPW is a complex problem to optimum date production while the recognition is avoided by palm trees as to be influenced by RPW. Several studies are driven to determine a precise approach for the detection, localization, and classification of RPW pests. Employing computer vision (CV) technology with pattern detection is verified that further productive once utilized for identifying and classifying insects. Thus, the automated method decreases either the problem or labor effort required for enhancing the farmer's income. The farmers can be stimulated to enhance the productivity of date fruit once this has been done. With this motivation, this article focuses on the design of automated RPW pest detection using sparrow search optimization with deep learning (RPWPD-SSODL) technique. The presented RPWPD-SSODL algorithm mostly focused on the detection and classification of RPW using computer vision approaches. To accomplish this, the RPWPD-SSODL technique employs bilateral filtering (BF) for noise removal. Next, the RPWPD-SSODL technique uses Dense-RefineDet object detector with ShuffleNet model as a backbone network. For improving the recognition solution, the hyperparameter tuning of the ShuffleNet model can be optimally adjusted using the SSO algorithm. To validate the simulation results of the RPWPD-SSODL technique, a wide-ranging simulation outcome is implemented. The simulation values potrayed the improvement of the RPWPD-SSODL algorithm over other approaches under several measures.

Doi: https://doi.org/10.54216/IJAACI.060202

Vol. 6 Issue. 2 PP. 16-27, (2024)

Algorithms for Cybersecurity in CAVs Based On Deep Learning and Their Applications

Sara Sawalmeh

This paper is concerned with the study of some novel techniques that using artificial intelligence to protect networks of CAVs from cyberattacks, where we use some machine learning algorithms to detect attacks and compare the machine learning algorithms used for this in terms of accuracy and required operating time. Also, WEKA tool will be used for the desired comparison, as the experiments are carried out on a new dataset, which is a dataset abbreviated from the KDD99 dataset.

Doi: https://doi.org/10.54216/IJAACI.060203

Vol. 6 Issue. 2 PP. 28-36, (2024)

On The Computational Properties of 3-Cyclic and 4-Cyclic Refined Matrices and the Diagonalization Algorithm

Hasan Sankari , Mohammad Abobala

This paper is concerned with studying the matrix computations of 3-cyclic refined neutrosophic matrices and 4-cyclic refined neutrosophic matrices with 3cyclic/4-cyclic real entries, where we introduce a novel method to compute eigenvalues and vectors of these matrix classes. Also, we provide a novel algorithm for diagonalization these matrices and to determine whether an n-cyclic refined matrix is diagonalizable or not for n=3, 4.

Doi: https://doi.org/10.54216/IJAACI.060204

Vol. 6 Issue. 2 PP. 37-45, (2024)

Revolutionizing Unmanned Aerial Vehicle Imagery Classification: A Deep Learning Approach Empowered by Computer Vision

Lee Xu

Recently, computer vision, unmanned aerial vehicles (UAV) based remote sensing (RS) and deep learning (DL) technologies have been instrumental in global food productivity and future agriculture. UAV provides several advantages over other possible RS platforms like real-time data acquisition, high flexibility, and the best tradeoff between spatial, low cost, small size, spectral, and temporal resolution. One possible advantage of using UAVs for crop classification is that they can efficiently and quickly cover large areas, and could gather data from different angles and at different times. This might assist in providing detailed knowledge of the crops and their conditions. Earlier research is limited to finding a single crop from the RGB images taken by the UAV and hasn’t explored the possibility of multi-crop classification by carrying out DL algorithms. Thus, this study presents a new Automated Crop Type Classification using Adaptive African Vulture Optimization with Deep Learning (ACCT-AAVODL) technique. The ACCT-AAVODL algorithm aims to investigate the UAV images and determine different types of food crops. To accomplish this, the presented ACCT-AAVODL method uses a densely connected network (DenseNet121) for generating feature vectors. Since the trial and error hyper parameter tuning is a challenging task, the AAVO model is employed for hyper parameter optimization. The ACCT-AAVODL technique involves a sparse auto encoder (SAE) with a Nadam optimizer for crop type classification, the stimulation analysis of the ACCT-AAVODL approach on the drone imagery dataset shows the remarkable performance of the ACCT-AAVODL method over other approaches.

Doi: https://doi.org/10.54216/IJAACI.060205

Vol. 6 Issue. 2 PP. 46-61, (2024)

Enhancing Financial Fraud Detection using Temporal Patter Mining Technique

Ahmed Aziz , Sanjar Mirzaliev

Examining the temporal behavior of common patterns, obtaining appropriate clusters, and reducing the size of discovered patterns are three significant challenges in temporal data mining. Among the available methods, the constraint-based pattern mining approach has achieved remarkable progress in this domain. Apriori and Interleaved algorithms, which are both slow and outdated, are nonetheless used by present time-granularity pattern exploration approaches. To address these issues, we propose the Frequent Pattern Growth method with Special Constraints. The system incorporates a method for generating patterns on a regular basis. It mandates that transactional datasets adhere to complete and partial cyclic criteria. To locate all possible periodic patterns within the Spatio temporal database, we redefine the task as periodic pattern mining in this thesis. The proposed method makes use of a periodic pattern tree miner. To begin, the clustering method uses an innovative global pollination artificial fish swarm technique to create the most effective dense clusters.

Doi: https://doi.org/10.54216/IJAACI.060206

Vol. 6 Issue. 2 PP. 62-72, (2024)

Hybrid Stochastic-Deterministic Path Planning Based Robotic Navigation Analysis