Journal of Soft Computing and Computer Applications

In robotics, efficient path planning makes robots work independently and move through changing environments over time. This study combines the Rapidly-exploring Random Tree (RRT) architecture with the Firey Algorithm (FA) to make robot’s path-planning better. The proposed ERRT-FA, which stands for “Enhanced RRT with Firey Algorithm”, generates better routes using Firefly social habits. Plan routes using Firefly social habits can effectively aid in exploring configuration space. The role of the FA is to enhance the RRT algorithm by providing an optimized exploration of the search space, ultimately leading to optimizing the path found by
the RRT algorithm and better paths in complex environments. The basic idea of the FA is to refine the resulting path by the RRT algorithm through optimizing the positions of Fireflies based on their intensity. Various tests show that ERRT-FA works better than the RRT algorithm in many
robotic situations. It indicates a significant reduction in computation time, exploration efficiency, and route length, with statistical analysis showing a mean decrease. Such a result denotes that the proposed ERRT-FA is an alternative solution for optimizing ERRT-FA as a perfect path plan.

In recent years, with the rapid development of the current classification system in digital content identification, automatic classification of images has become the most challenging task in the field of computer vision. As can be seen, vision is quite challenging for a system to automatically understand and analyze images, as compared to the vision of humans. Some research papers have been done to address the issue in the low-level current classification system, but the output was restricted only to basic image features. However, similarly, the approaches fail to accurately classify images. For the results expected in this field, such as computer vision, this study proposes a deep learning approach that utilizes a deep learning algorithm. In this research, a proposed model based on a Convolutional Neural Network (CNN) which is a machine learning
tool that can be used for the automatic classification of images. The model is concerned with the classification of images, and for this, it employs the COREL Image dataset (Corel Gallery Image Dataset) as a reference. The images in the dataset used for training are harder than the classification of the images since they need more computational resources. In the experimental part, training the images using the CNN network achieved 98.52% accuracy, proving that the model has high accuracy in the classification of images.

Deep learning and machine learning play an important role in the medical field, helping doctors make accurate, fast and effective diagnosis. Despite the progress achieved in the use of modern technologies in detecting cancerous nodes, current studies still suffer from some challenges and
limitations that must be addressed to obtain high efficiency in identifying cancerous nodes. These challenges include using image pre-processing, combining deep learning and machine learning techniques, and constantly adapting to clinical changes, in order to address this. A hybrid methodology has been proposed for detecting cancerous nodules in the lung in medical Computed Tomography (CT) images. It includes three stages, the first of which is pre-processing the images used, identifying nodes, balancing samples, and extracting features using Local Binary
Pattern (LBP) and Histogram of Oriented Gradients (HOG) from the images, and the second is based on building a deep learning model consisting of Convolutional Neural Network (CNN) and Long-Short-Term Memory (LSTM) to extract the trained features and combine them with the
features extracted by LBP and HOG to be input for the next stage. In the next stage, the improved eXtreme Gradient Boosting (XGBoost) machine learning classifier is built using the Particle Swarm Optimization (PSO) algorithm. The highest accuracy results of 99.3%, and ROC-AUC were
obtained 99.8%. The proposed methodology has proven its eciency in detecting cancerous nodes accurately using CT images.

Dimensionality reduction techniques streamline machine learning by reducing data complexity, improving model accuracy, and cutting computational costs. They remove noise and irrelevant features, making models faster and more ecient. These techniques also enhance data visualization and interpretation by condensing data into manageable, insightful dimensions. Ultimately, dimensionality reduction leads to simpler, more interpretable models without sacrificing critical information, making it a cornerstone of efficient data analysis and machine learning applications. Theoretically, feature extraction tends to create new features that encapsulate more information by combining multiple existing features, resulting in more concentrated and informative features.
In contrast, feature selection involves choosing a subset of the original features without altering their content. In this paper, a feature selection method based on Principal Component Analysis (PCA) is proposed, along with a comparative study of PCA performance as a feature extraction
technique and the newly proposed feature selection method. The proposed feature selection method utilizes the variance scored by the principal component to identify the features with the most effect on the principal component’s variance and selects them as the best set of features. Experimental results demonstrate that the proposed PCA-based feature selection achieves comparable or improved performance across various classifiers, while maintaining high accuracy and precision, even with fewer features. For instance, when using the proposed method with Network Security Laboratory - Knowledge Discovery in Databases (NSL-KDD) to select only one feature and employing six different classifiers (Decision Tree, Naive Bayes, Logistic Regression, K-Neighbors Classier, XGBoost, and AdaBoost) to evaluate performance, the accuracy of 80.88%, 81.29%, 43.07%, 44.53%, 84.94%, and 82.87% were obtained using the listed classifiers in the same order. On the other hand, when using PCA for feature extraction the following accuracy values, listed in the same classifier order, were obtained: 76.64%, 76.10%, 43.07%, 47.40%, 80.57%, and 82.05%, demonstrating that the proposed method delivers higher accuracies. Similarly, for the mushroom dataset, the accuracies were 51.38%, 51.38%, 48.62%, 51.06%, 87.08%, and 86.22%, compared to 50.14%, 50.30%, 50.88%, 50.59%, 73.60%, and 71.51%.

One area of study between computer science and biology is bioinformatics, which deals with methods for collecting, processing, storing, and evaluating biological data. Sequences of RiboNucleic Acid (RNA), DeoxyriboNucleic Acid (DNA), and proteins make up biological data, which has a wide range of uses in domains such as feature extraction, data segmentation, data security, and more. In cryptography, DNA sequences are used as data carriers, enhancing the unique properties of biomolecules. This approach involves using DNA sequences to enhance the security of confidential data that must be transmitted over networks or stored securely. Several DNA-based security techniques have been developed, each with its own strengths and weaknesses. The bioinformatics offers innovative solutions for data security, especially in the realm of cryptography. By employing DNA sequences as data carriers, bioinformatics techniques can significantly enhance the protection of confidential data. The goal of This survey aims to summarize the existing literature on DNA-based security techniques. It highlights various methods that use DNA sequences to protect confidential data, either during transmission or in storage and identifies the strengths and weaknesses of these methods, providing a comprehensive overview
for researchers to assist in planning and designing secure techniques based on bioinformatics methods. By understanding the existing approaches and their limitations, researchers can develop more efficient and robust security techniques that leverage the power of DNA sequences and bioinformatics. This survey serves as a valuable resource for researchers seeking to explore and develop secure methods based on the intersection of bioinformatics and cryptography.