Some results on approximation in neutrosophic normed space

Alaa Adnan; Mohammed A.

doi:https://doi.org/10.54216/IJNS.240429

International Journal of Advances in Applied Computational Intelligence | 2024

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

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.

groups

Noor Edin Rabeh mail

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

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2024

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

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.

groups

Narek Badjajian mail -

Warshine Barry mail

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

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2024

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

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.

groups

Sara Sawalmeh mail

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

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2024

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

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.

groups

Hasan Sankari mail -

Mohammad Abobala mail

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

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2024

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

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.

groups

Lee Xu mail

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

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2024

Deep Learning-Based model for Medical Image Compression

Efficient compression algorithms are required to handle the growing amount of medical picture data, ensuring that storage and transmission requirements are met without compromising diagnostic quality. This research presents a hybrid image compression framework that integrates deep learning alongside standard lossless compression techniques. A convolutional autoencoder (CAE) learns a compact representation of medical images, which are subsequently compressed using the Brotli algorithm. Our technique beats conventional approaches, like JPEG, JPEG2000, and wavelet-based ones, according to an analysis of a brain MRI dataset. It maintains competitive compression ratios while producing higher (PSNR) and (MSE), indicating higher picture integrity and low information loss. To strike a good balance between the critical need for accurate diagnosis and the economical use of resources, this study offers a possible method for compressing medical images.

groups

Saad H. Baiee mail -

Tawfiq A. AL-Assadi mail

link https://doi.org/10.54216/JISIoT.130215

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2024

A New Method for Intelligent Multimedia Compression Based on Discrete Hartley Matrix

Multimedia data (video, audio, images) require storage space and transmission bandwidth when sent through social media networking. Despite rapid advances in the capabilities of digital communication systems, the high data size and data transfer bandwidth continue to exceed the capabilities of available technology, especially among social media users. The recent growth of multimedia-based web applications such as WhatsApp, Telegram, and Messenger has created a need for more efficient ways to compress media data. This is because the transmission speed of networks for multimedia data is relatively slow. In addition, there is a specific size for sending files via email or social networks, because much high-definition multimedia information can reach the Giga Byte size. Moreover, most smart cameras have high imaging resolution, which increases the bit rate of multimedia files of video, audio, and image.  Therefore, the goal of data compression is to represent media (video, audio, images, etc.) as accurately as possible with the minimum number of bits (bit rate). Traditional data compression methods are complex for users. They require a high processing power for media data. This shows that most of the existing algorithms have loss in data during the process of compressing and decompressing data, with a high bitrate for media data (video, audio, and image). Therefore, this work describes a new method for media compression systems by discrete Hartley matrix (128) to get a high speed and low bit rate for compressing multimedia data. Finally, the results show that the proposed algorithm has a high-performance speed with a low bit rate for compression data, without losing any part of data (video, sound, and image). Furthermore, the majority of users of social media are satisfied with the data compression interactive system, with high performance and effectiveness in compressing multimedia data. This, in turn, will make it easier for users to easily send their files of video, audio, and images via social media networks.

groups

Noor Mezher Sahab mail -

Qusay Abboodi Ali mail

link https://doi.org/10.54216/FPA.160207

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

Neutrosophic Sets in Big Data Analytics: A Novel Approach for Feature Selection and Classification

Big Data Analytics are said to help in transforming huge amounts of raw data towards valuable information that can be used, but there are formidable challenges in feature selection and classification due to the complexity and high dimensionality of the data. Traditional methods are usually too weak to handle the built-in uncertainty, imprecision, and inconsistency within big data and they often fail to perform well. This paper aims to induce the new methodology on these problems using the sets of neutrosophic in dealing with more flexible and nuanced data analysis. The key contributions to the current approach proposed are threefold. First, generalization of the classical set through extension of the notions of truth, indeterminacy, and falsity by allowing representations of uncertainty in data. The second combines a powerful process for selecting features based upon neutrosophic set theory that is optimal by genetic algorithms and advances a step further by applying these features in training and validating the classification models across a set of different domains. Therefore, the major aim from this study is to increase accuracy and reliability in feature selection and classification in big data analytics. This methodology has been implemented and tested over datasets of the following types: healthcare, finance, social media, and more. Results have proved great improvement against conventional performance metrics, for example, the classification accuracy with an SVM classifier over the Cleveland Heart Disease dataset increases from 83.5% to 87.2%, and of a Random Forest classifier over a financial dataset from 76.4% to 81.9%. For instance, the accuracy of social media sentiment analysis changed to 82.7% from 78.3%. All these findings establish that the neutrosophic set-based method holds good advantages in addressing the limitations of classical alternatives. The proposed approach of neutrosophism, through an explicit model, enhances performances in classifications and, at the same time, augments overall robustness and reliability in big data analytic. The importance of this study lies in establishing the groundwork for further research and practical applications, thus indicating possible further development in this field.

groups

Azmi Shawkat Abdulbaqi mail -

Ahmed Dheyaa Radhi mail -

Lateef Abd Zaid Qudr mail -

Harshavardhan Reddy Penubadi mail -

Ravi Sekhar mail -

Pritesh Shah mail -

Mrinal Bachute mail -

Jamal Fadhil Tawfeq mail -

Hassan muwafaq Gheni mail

link https://doi.org/10.54216/IJNS.250138

Volume & Issue

Vol. Volume 25 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2024

Efforts of Neutrosophic Logic in Medical Image Processing and Analysis

Medical image processing is indispensable for correct diagnosis and planning of treatment. However, it is susceptible to many errors due to noise, artifacts, and the variability innate in anatomical structures themselves. Traditional image analysis methods hence suffer from these complexities in the images themselves and lead to probable inaccuracies in image analysis. This paper probes into the role of neutrosophic logic in the domain of medical image processing to seek better handling of these problems. The main objectives of the work were to optimize the noise reduction, image segmentation, feature extraction, and classification using the special capabilities of neutrosophic logic directed toward handling uncertainty and indeterminacy. Contributions The contributions of this study are multifaceted: it contributes by introducing detailed support for applying neutrosophic logic in a number of medical image processing tasks and integrates neutrosophic logic with prior techniques and evaluates their performance with traditional methods. The experimental results in the study are complete and demonstrate significant improvements in key metrics. For example, applying neutrosophic logic in noise reduction increased the peak signal-to-noise ratio of MRI images from 25 dB to 35 dB. In some segmentation tasks, the Dice coefficient for liver CT scans increased from 0.85 to 0.92. It increases the accuracy of feature extraction in breast cancer detection from 88% to 95%, while integrating neutrosophic logic with convolutional neural networks improves the accuracy in retinal image classification from 92% to 97%. All these results underline the strong role that neutrosophic logic can play in enhancing accuracy, robustness, and reliability in the processing of medical images. The result of the study concludes that neutrosophic logic not only improves the current limitations but also holds great promise for handling uncertainty in many medical fields, opening a promising way for future advancements in the field of medical imaging and health applications.

groups

Azmi Shawkat Abdulbaqi mail -

Bourair Al-Attar mail -

Lateef Abd Zaid Qudr mail -

Harshavardhan Reddy Penubadi mail -

Ravi Sekhar mail -

Pritesh Shah mail -

Sushma Parihar mail -

Sushmitha Kallam mail -

Jamal Fadhil Tawfeq mail -

Hassan muwafaq Gheni mail

link https://doi.org/10.54216/IJNS.240428

Volume & Issue

Vol. Volume 24 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2024

Some results on approximation in neutrosophic normed space

Neutrosophic normed linear spaces are the main significant notion in the study of classical functional analysis under a neutrosophic environment to handle indeterminate and inconsistent information. Where the neutrosophic norm function assigns to each vector in the linear space a neutrosophic number, which is a number with a truth, indeterminacy, and falsity component. The main aim of this work is to study and discuss the important properties of proximinality of specific sets and new results for a large class in neutrosophic normed space. Moreover, we show some results closely related proximainality of classes to the normed construction in the space. Also, we prove achieved for generalized sets in neutrosophic normed space, most marks on convexity and Cheby-shevity classes are considered.

groups

Alaa Adnan Auad mail -

Mohammed A. Hilal mail

link https://doi.org/10.54216/IJNS.240429

Volume & Issue

Vol. Volume 24 / Iss. Issue 4

Details open_in_new