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

Vasima; Seema; Janjhyam Venkata Naga; Piyush Kumar; Prashant Kumar; Shraddha V.

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

Journal of Cybersecurity and Information Management | 2024

Credit Card Fraud Detection Model Based on Correlation Feature Selection

Credit card fraud is a widespread cybercrime that threatens financial security. Effective cybersecurity measures are essential to mitigate these risks. Machine learning has shown promising results in detecting credit card fraud by analyzing transaction data and identifying patterns of suspicious behavior. Feature selection is crucial in machine learning because it simplifies the model, improves its performance, and prevents overfitting. This research introduces a machine learning model designed for credit card fraud detection. The model makes use of three types of correlations. Pearson, Spearman, and Kendall, to identify features and enhance the fraud detection process. Testing on datasets yielded impressive results achieving category accuracies of 99.95% and 99.58% surpassing alternative approaches. Also, the results showed that Kendall correlation is the best among the three types of correlation in selecting attributes in all approved datasets.

groups

Ahmad Salim mail -

Salah N. Mjeat mail -

Daniah Abul Qahar Shakir mail -

Mohammed Awad Alfwair mail

link https://doi.org/10.54216/JCIM.140224

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

Enhanced Face Detection in Videos Based on Integrating Spatial Features (LBP, CS-LBP) with CNN Technique

Face detection is a crucial aspect of computer vision and image processing, in order to enable the automatic detection and identification of human faces in video streams, face detection is an essential component of computer vision and image processing. Applications for facial recognition, video analytics, security systems, and surveillance all depend on it. Face identification techniques face many obstacles and issues, such as positional fluctuations, illumination changes, resolution and scale issues, facial emotions, and cosmetics. Robust algorithms are required for efficient face detection. This field looks at the feature extraction process using a variety of techniques. These consist of the center symmetric local binary patterns (CS_LBP) approach and the local binary patterns (LBP) method. The YouTube Face database provided the video frames that we used for our study. In order to train the convolutional neural network (CNN) to detect human faces in the video and draw a bounding box around them. The experimental results of the suggested approaches show that. The accuracy rate was 94% higher with the LBP techniques. However, the CS_LBP technique showed the best level of accuracy in both face detection and face rectangle recognition, with an accuracy rate of 95%.

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Faqeda Hassen Kareem mail -

Mohammed Abdullah Naser mail

link https://doi.org/10.54216/JCIM.140225

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

An Efficient Algorithm for Stock Market Prediction Using Attention Mechanism

Forecasting the stock market is a significant challenge in the financial industry due to its time series' complicated, noisy, chaotic, dynamic, volatile, and non-parametric nature. Nevertheless, due to computer advancements, an intelligent model can assist investors and expert analysts mitigate the risk associated with their investments. In recent years, substantial research has been conducted on deep learning models. Many studies have investigated using these techniques to anticipate stock values by analyzing historical data and technical indications. However, since the goal is to create predictions for the financial market, validating the model using profitability indicators and model performance is crucial. This article incorporates the attention mechanism model, incorporating attention from both feature and time perspectives. Utilize artificial neural networks. This approach addresses issues in time series prediction. The issue is the varying degrees of influence that many input features have on the target sequence. To tackle this, the method utilizes a feature attention mechanism to obtain the weights of distinct input features. An enhanced feature association relationship is achieved, whereas the data before and following the sequence exhibit a significant time correlation. An attention technique is employed to address this issue, allowing for the acquisition of weights at various time intervals to enhance robustness and temporal dependence. The system is applied to the three global SMs (TESLA, S&P500, and NASDAQ) datasets, the best enhancement results are 99% in Acc, and the better results improvement to minimize error in MSE, MAPE, and RMSE are 0.004, 0.004 and 0.01 respectively.

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Zena Kreem Minsoor mail -

Ali Yakoob Al-Sultan mail

link https://doi.org/10.54216/JCIM.140226

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

Innovative Approaches to Bank Security in India: Leveraging IoT, Blockchain, and Decentralized Systems against Loan Scams

This research paper explores the significant impacts of multiple loan fraud on Indian banks and financial institutions, emphasizing the resulting bad debts and financial losses. The issue is exacerbated in the real estate sector, where influential developers exploit system vulnerabilities to secure multiple loans using the same collateral. Consumers also face challenges in accessing credit due to these fraudulent practices. The study underscores the need for enhanced regulatory measures and internal controls within financial institutions. Additionally, it introduces IoTBlockFin, a decentralized system that integrates block chain and IoT technologies to securely assess customer reliability and mitigate fraud. IoTBlockFin's Advanced Proof of Work (APOW) mechanism, combined with IoT data for real-time monitoring, offers superior security, latency, and cost-effectiveness compared to centralized systems, as demonstrated by experimental results.

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Akhtar Hasan Jamal Khan mail -

Syed Afzal Ahmad mail

link https://doi.org/10.54216/JISIoT.130221

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

An Ensemble Boosting Algorithm based Intrusion Detection System for Smart Internet of Things Environment

An influx of smart spaces that are now connected to the IoT network has increased new forms of cyber threats; thus, a need for more effective IDS to deal with these complex cyber threats. Traditional security measures cannot solve the modern problem of protecting IoT devices as they are a complex and homogeneously distributed network. Advancements and development of Artificial intelligent (AI) and machine learning technologies have provided new hope to make more reliable IDS. Our study presents Particle Swarm Optimization integrated Light-Weight Gradient Boosting Machine, abbreviated as LGBM-PSO in which, the PSO algorithm is applied for hyper parameters optimization in the model training. Based on the ensemble methodology, a new model for network intrusion detection is proposed in this study to improve the accuracy of the technique proposed. As for the current study project, the “DS2OS” dataset was employed to execute the suggested task. All of the data obtained from the traces of the smart devices placed in a smart home environment are incorporated in this dataset. The IDS model comprises several stages, one of which comprises data preprocessing that entails data cleaning, normalization, and encoding of network traffic data. Feature selection and dimensionality reduction are used which leads to the optimization of the dataset in this case. The core of the model comprises four classifiers: The compared models are Decision Tree (DT), LGBM-PSO, Light Gradient Boost Machine (LGBM), and Extreme Gradient Boost (XGB). Each of these classifiers can be combined with a majority voting ensemble method to increase the reliability of the predictions. The suggested model's accuracy that is LGBM-PSO is the highest with a value of 99.89%. The corresponding figures for the training data are 99.79%. Stand on the testing data proving the efficiency and stability of the algorithm. The use of the ensemble approach is superior especially when using a complex model like LGBM-PSO in the field of intrusion detection. As a result, high accuracy, optimized time, and effective threat identification ensure that it is a useful tool in strengthening security in the different applications.

groups

Rami Baazeem mail

link https://doi.org/10.54216/JISIoT.130222

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

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.

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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