On a Novel Simulation of a Control Technique for Power Oscillation Damping and Applications

Talal

doi:https://doi.org/10.54216/NIF.030204

Journal of Intelligent Systems and Internet of Things | 2024

A Predictive Analysis of IMDb Movie Reviews Using LSTM and ANN Models

The Machine Learning domain has made a major process with the progression of state-of-the-art technologies. Since current algorithms often don’t provide palatable learning performance, it is necessary to continually upgrade them. This paper has illustrated the comparison of the Long Short-Term Memory (LSTM) model and the Artificial Neural Networks (ANN) model in the prediction of the Internet Movie Database (IMDb) website. These evaluations were then related to sentiment assessment approaches to evaluate their predicted accuracy and performances. The results demonstrate that the ANN model outperforms the LSTM model with a high accuracy rate in terms of the prediction accuracy and loss indicators for the IMDb movie review’s sentiment analysis task in terms of the prediction accuracy and loss indicators for the IMDb movie review’s sentiment analysis task. The accuracy of prediction on the test dataset of the ANN model is 83.5 % and the LSTM model is 83.5%. Therefore, it can be concluded that the standard artificial neural network model that was utilized is an appropriate technique for sentiment assessment tasks in IMDb rating text data.

groups

Noor alhuda A. Salih mail -

Osama A. Qasim mail -

Mohammed S. Noori mail -

Rabei Raad Ali mail -

Khawla Ahmad Wali mail

link https://doi.org/10.54216/JISIoT.130223

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

A Comprehensive Review of Real-Time Vehicle Tracking for Smart Navigation Systems

Vehicle tracking is one of computer vision's most important applications, with applications ranging from robotics and traffic monitoring to autonomous vehicle navigation and many more. Even with the significant advancements in recent research, issues like occlusion, fluctuating illumination, and fast motion still need to be addressed, calling for more investigation and creativity in this field. This study performs a thorough examination of various vehicle-tracking approaches and suggests a thorough classification scheme that divides them into four main categories: strategies that rely on features, segmentation, estimate, or learning. Two well-known methods are highlighted specifically in the estimation-based category: particle filters and Kalman filters.

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Veena R S mail -

Seema Rani mail -

Ch Madhava Rao mail -

Piyush Kumar Pareek mail -

Sandeep Dalal mail -

Shweta Bansal mail

link https://doi.org/10.54216/JISIoT.130224

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

Gazelle Optimized Visual Geometry Group Network with Resnet101 fostered Oral Squamous Cell Carcinoma Detection

Microscopic examination of tissues to detect oral cancer falls short as traditional microscopes struggle to easily differentiate between cancerous and non-cancerous cells. The identification of cancerous cells through microscopic biopsy images has the potential to alleviate concerns and improve outcomes if precise biological approaches are employed. However, relying solely on physical examinations and microscopic biopsy images for cancer identification increases the likelihood of human error and mistakes. Therefore, in order to obtain accurate results, a new research technique has been developed. In this manuscript, Gazelle Optimized Visual Geometry Group Network with Resnet101 fostered Oral Squamous Cell Carcinoma Detection (OCD-VGGNetCNN-GOA-Resnet101) is proposed. In this method initially, the images are taken from Kaggle repository benchmark dataset and preprocessed to improve image quality.  Then the result is given to the Visual Geometry group Network based CNN (VGGNetCNN) with Resnet101 for classification. Finally, the VGGNetCNN -ResNet 101 classifies image into normal and OSCC. Then the simulation performance of the proposed -VGGNetCNN-GOA-Resnet101 method attains 23.67%, 34.89%, 39.45% and 45.31% higher accuracy while compared with existing methods such as OCD-CNN-Alexnet, OCD-CNN-VGG19 and HI-OCD-CNN-INet respectively.

groups

Kumar R mail -

S Pazhanirajan mail

link https://doi.org/10.54216/JISIoT.130225

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

Brain Tumor Semantic Segmentation using U-Net and Moth Flame Optimization

Brain tumor is an abnormal development of brain cells that, if left untreated, can have severe consequences. Brain tumour semantic segmentation is the process of determining and distinguishing the impacted brain regions, which is essential for accurate diagnosis, treatment planning, as well as surveillance of the tumor's development over time. This paper presents a model for identifying and segmenting brain tumor using Unet architecture with the optimization of hyper parameters using the Moth Flame Optimization (MFO) algorithm. Due to its capacity to collect spatial information, the Unit architecture is a common choice for picture segmentation tasks. The MFO algorithm is an optimization technique that draws inspiration and replicates from the behavior of moths. Both techniques are developed to improve efficiency. The performance of the model has increased using the MFO method, which led to improved segmentation results. Based on comparative analysis report, the proposed model shows a percentage improvement of approximately 65.16% in MSE, 28.87% in PSNR, and 40.30% in Tversky compared to the Unet and Unet++ models. This method has demonstrated good results in identifying and segmenting brain tumors, which can be helpful in the early identification and treatment of brain tumor.

groups

B. Tapasvi mail -

E. Gnanamanoharan mail -

N. Udaya Kumar mail

link https://doi.org/10.54216/JISIoT.130226

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2024

Enhancing Tomato Leaf Disease Detection through Generative Adversarial Networks and Genetic Algorithm based Convolutional Neural Network

In the agricultural sector, tomato leaf diseases signify a lot because they result in a lower crop yield and quality. Timely detection and classification of diseases help to ensure early interventions and effective treatment solutions. Nonetheless, the existing methods are confined by the dataset imbalance which affects class distribution negatively and thus results in poor models, especially for rare diseases. The research is designed to improve the capability of tomato leaf disease identification by investing a new deep-learning method beyond the challenge of imbalanced class distribution. By balancing the dataset, we aim to improve classification accuracy as we pay more attention to the under-represented classes. The proposed GAN-based method that combines the Weighted Loss Function to produce tomato leaf disease synthetic images is underrepresented. They improve the quality of the entire dataset, and the images from every class are now in a more balanced proportion. A CNN, which is the convolutional neural network, is trained for the classifier, with the weighted loss function as a part of the model. We used Genetic Algorithm (GA) for hyperparameter optimization of the CNN. It helps in emphasizing the learning process from the under-represented class. The suggested one will not only decrease the accuracy of tomato leaf disease detection but also increase it. Therefore, the synthetic images created by GAN enhance the dataset since the class distribution is brought to equilibrium. The incorporation of the weighted loss function into the model’s training process makes it very effective in handling with the class instability problem and consequently, the model can identify both common and rare diseases. From the outcomes of this study, it can be concluded that it is feasible to employ GAN and one loser weights function to solve the problem of class imbalance in tomato leaf disease recognition. A suggested approach that increases the model’s accuracy and reliability could be a good move to enhancing a reliable method of disease detection in the agricultural sector.

groups

Vasima Khan mail -

Seema Sharma mail -

Janjhyam Venkata Naga Ramesh mail -

Piyush Kumar Pareek mail -

Prashant Kumar Shukla mail -

Shraddha V. Pandit mail

link https://doi.org/10.54216/FPA.160210

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

Systematic Analysis of threats, Machine Learning solutions and Challenges for Securing IoT environment

The Internet of Things (IoT) has revolutionized our daily lives, impacting everything from healthcare to transportation and even home automation and industrial control systems. However, as the number of connected devices continues to rise, so do the security risks. In this review, we explore the different types of attacks that target various layers of IoT infrastructure. To counter these threats, researchers have proposed using machine learning (ML) and deep learning (DL) techniques for detecting different types of attacks. However, our examination of existing literature reveals that the effectiveness of these techniques can vary greatly depending on factors like the dataset used, the features considered, and the evaluation methods employed. Finally, we delve into the current challenges facing Intrusion Detection Systems (IDS) in their mission to protect IoT environments from evolving threats.

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Bharti Yadav mail -

Deepak Dasaratha Rao mail -

Yasaswini Mandiga mail -

Nasib Singh Gill mail -

Preeti Gulia mail -

Piyush Kumar Pareek mail

link https://doi.org/10.54216/JCIM.140227

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Neutrosophic and Information Fusion | 2024

Finding the complete List of Different K-Brackets for the Projective Plane PG (2, 8)

A k-arc in a plane PG (2, q) is a set of k point such that every line in the plane intersect it in at most two points and there is a line intersect it in exactly two points. A k-arc is complete if there is no k+1-arc containing it. This thesis is concerned with studies a k-arcs, k=4, 5,…., 10 and classification of protectively distinct k-arcs and distinct arcs under collineation. We prove by using computer program that the only complete k-arcs is for, k= 6, 10.

groups

Khaled Moaz mail

link https://doi.org/10.54216/NIF.030201

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

Details open_in_new

Neutrosophic and Information Fusion | 2024

On the Effect of the Layers' Number of Deep Neural Network for Improving the Reward of a Reinforcement Learning Robot

The Q learning algorithm in reinforcement learning is one of the algorithms that allows the robot to learn the surrounding environment without the need for prior training samples with the principle of reward and punishment for the robot through interaction with the environment. Increasing the number of hidden layers of the deep neural network used and adjusting some of the higher parameters in it can increase the reward of the robot and thus obtain the best path to achieve the goal.

groups

Talal Markabi mail -

Bahaa Mansoura mail

link https://doi.org/10.54216/NIF.030202

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

Details open_in_new

Neutrosophic and Information Fusion | 2024

On the Stability Analysis of the Fisher Equation Based on Some Numerical Galerkin Techniques

We studied the stability of the steady state solutions for Fisher Equation in two cases, the First one with constant amplitude and we show that the steady state solution u1=1 is always stable under any condition, but the other two solutions u1=0 and u1 (x)=A cos (nπX)are conditionally stable. In the Second case, we studied the steady state solutions for various amplitude by using two Methods. The First is analytically by direct Method and the second is numerical method using Galerkin technique which shows the same results, that is the steady state solution u1=1 is always stable under any conditions, but the other two solutions u1=0 and u1 (x)=A cos (nπX) are conditionally stable.

groups

Murat Ozcek mail

link https://doi.org/10.54216/NIF.030203

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

Details open_in_new

Neutrosophic and Information Fusion | 2024