Deep Learning-Based model for Medical Image Compression

Saad H.; Tawfiq A.

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

Journal of Intelligent Systems and Internet of Things | 2024

Transfer Learning and Optimised Firefly Neural Network for Lung Cancer

Today's clinical analysis and precise illness detection are mandated requirements for the development of intelligent expert systems. Since lung cancer affects both men and women equally and has a greater mortality rate than other illnesses, a more complete examination is needed to diagnose lung cancer. More helpful information regarding a lung cancer diagnosis may be provided by images from a computer tomography (CT) scan. Various machine learning and deep learning algorithms are created to enhance the medical treatment process using CT scan input pictures. But research still has a bad side when it comes to creating a precise and intelligent system. In order to improve the detection of lung tumors from the CT input images, this paper presented Firefly optimized pre trained transfer learning. The previously trained model VGG-16 is used in this paper to extract features more effectively, using the features chosen via the firefly optimization approach to increase classification accuracy while reducing complexity. The thorough testing done with the “LUNA-16 & LIDC Lung image” datasets is assessed & studied along with other performance measures like "accuracy, precision, recall, specificity, and F1-score". Investigation results show that the suggested design outperformed the “DenseNet, AlexNet, Resnets-50, Resnets-100, VGG-16 & Inception models” and reached the top results with "98.5% accuracy, 99.0% precision, 98.8% recall, with 99.1% F1-score.

groups

A. Gopinath mail -

P.Gowthaman mail

link https://doi.org/10.54216/JISIoT.130213

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

LoRa Architecture-Enabled Intelligent for Agriculture with Deep Learning Architecture

The agricultural industry faces significant challenges in improving efficiency and productivity, particularly in monitoring crop health and environmental conditions. Traditional methods are often labor-intensive, time-consuming, and lack real-time data, leading to suboptimal decision-making. Recent advancements in Internet of Things (IoT) and Artificial Intelligence (AI) technologies offer promising solutions. Long Range (LoRa) communication, a type of low-power wide-area network (LPWAN), enables long-distance data transmission with minimal power consumption, making it ideal for rural and expansive agricultural areas. When combined with deep learning, which can analyze large volumes of data to generate predictive insights, these technologies have the potential to revolutionize agricultural practices by providing farmers with timely and accurate information to optimize crop management and resource utilization. This study introduces an intelligent mote for agricultural applications, leveraging Long Range (LoRa) communication and deep learning techniques to improve precision farming. Traditional agricultural monitoring methods are labor-intensive and lack real-time insights. To address this, the mote is equipped with sensors to monitor temperature, humidity, soil moisture, and light intensity, transmitting real-time data over long distances with minimal power consumption using LoRaWAN. The collected data is processed by deep learning models to predict crop yield and identify potential issues. Field tests demonstrated a 15% improvement in yield prediction accuracy and a 20% reduction in water usage compared to traditional methods. These results highlight the effectiveness of integrating LoRa and deep learning in enhancing agricultural resource management and productivity.

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K M Monica mail -

Anitha D mail -

S.Prabu mail -

B.Girirajan mail -

Arun M mail

link https://doi.org/10.54216/JISIoT.130214

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

American Journal of Business and Operations Research | 2024

An examination of the link between organizational culture and strategy formulation in a selection of Iraqi private universities

This study aims to investigate the connection between organizational culture and strategy formulation in several private colleges in Iraq, as organizational culture is a major factor in the success or failure of organizations, and it is a crucial element in organizational transformulation and growth, which is a characteristic of the modern age. This research seeks to explore the significance of organizational culture by looking at its resurgence, its cultural makeup, and the dimensions of strategy formulation in universities and private colleges. It will then examine the connection between organizational culture and strategy formulation among the study sample. The hypothesis is that there is no meaningful relationship between organizational culture and strategy formulation. The research sample of (100) lecturers from (10) private universities and colleges was surveyed using a questionnaire to assess the interest in organizational culture in the educational community. The results revealed that there is a relative interest in the culture, but it is not given an important role in formulating the strategy. It was suggested that mental and intellectual abilities and experiences should be harnessed through dialogue and direct training to transform them into a powerful tool for formulating educational strategy.

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Ahmed Abdel Qader Ismail Alnajem mail

link https://doi.org/10.54216/AJBOR.110201

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2025

A Note on Two-Fold Neutrosophic and Fuzzy Topological Space Based on Real Numbers

The objective of this paper is to introduce for the first time the concept of two-fold neutrosophic and fuzzy topological space defined over real numbers, where we combine the two-fold neutrosophic sets with real numbers to get a novel topological space based on them. Also, we present many of its elementary properties and special subsets such as two-fold neutrosophic open sets, two-fold neutrosophic closed sets, and two-fold neutrosophic closure. Many examples and theorems will be provided to clarify the validity of our approach.

groups

Rabaa Al-Maita mail

link https://doi.org/10.54216/IJNS.250137

Volume & Issue

Vol. Volume 25 / Iss. Issue 1

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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

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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

Details open_in_new

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