Design and analysis of reinforcement learning models for automated penetration testing

Our paper proposes a framework to automate penetration testing by utilizing reinforcement learning (RL) capabilities. The framework aims to identify and prioritize vulnerable paths within a network by dynamically learning and adapting strategies for vulnerability assessment by acquiring the network data obtained from a comprehensive network scanner. The study evaluates three RL algorithms: deep Q-network (DQN), deep deterministic policy gradient (DDPG), and asynchronous episodic deep deterministic policy gradient (AE-DDPG) in order to compare their effectiveness for this task. DQN uses a learned model of the environment to make decisions and is hence called model-based RL, while DDPG and AE-DDPG learn directly from interactions with the network environment and are called model-free RL. By dynamically adapting its strategies, the framework can identify and focus on the most critical vulnerabilities within the network infrastructure. Our work is to check how well the RL technique picked security vulnerabilities. The identified vulnerable paths are tested using Metasploit, which also confirmed the accuracy of the RL approach's results. The tabulated findings show that RL promises to automate penetration testing tasks

Traffic flow prediction using long short-term memory-Komodo Mlipir algorithm: metaheuristic optimization to multi-target vehicle detection

Multi-target vehicle detection in urban traffic faces challenges such as poor lighting, small object sizes, and diverse vehicle types, impacting traffic flow prediction accuracy. This study introduces an optimized long short-term memory (LSTM) model using the Komodo Mlipir algorithm (KMA) to enhance prediction accuracy. Traffic video data are processed with YOLO for vehicle classification and object counting. The LSTM model, trained to capture traffic patterns, employs parameters optimized by KMA, including learning rate, neuron count, and epochs. KMA integrates mutation and crossover strategies to enable adaptive selection in global and local searches. The model's performance was evaluated on an urban traffic dataset with uniform configurations for population size and key LSTM parameters, ensuring consistent evaluation. Results showed LSTM-KMA achieved a root mean square error (RMSE) of 14.5319, outperforming LSTM (16.6827), LSTM-improved dung beetle optimization (IDBO) (15.0946), and LSTM-particle swarm optimization (PSO) (15.0368). Its mean absolute error (MAE), at 8.7041, also surpassed LSTM (9.9903), LSTM-IDBO (9.0328), and LSTM-PSO (9.0015). LSTM-KMA effectively tackles multi-target detection challenges, improving prediction accuracy and transportation system efficiency. This reliable solution supports real-time urban traffic management, addressing the demands of dynamic urban environments

Image analysis and machine learning techniques for accurate detection of common mango diseases in warm climates

Mangoes are valuable crops grown in warm climates, but they often suffer from diseases that harm both the trees and the fruits. This paper proposes a new way to use machine learning to detect these diseases early in mango plants. We focused on common issues like mango fruit diseases, leaf diseases, powdery mildew, anthracnose/blossom blight, and dieback, which are particularly problematic in places like Bangladesh. Our method starts by improving the quality of images of mango plants and then extracting important features from these images. We use a technique called k-means clustering to divide the images into meaningful parts for analysis. After extracting ten key features, we tested various ways to classify the diseases. The random forest algorithm stood out, accurately identifying diseases with a 97.44% success rate. This research is crucial for Bangladesh, where mango farming is essential for the economy. By spotting diseases early, we can improve mango production, quality, and the livelihoods of farmers. This automated system offers a practical way to manage mango diseases in regions with similar climates

Optimization control design and simulation of furnace-fired boiler exit pressure: leveraging disruptive technology

The efficient operation of furnace-fired drum boilers is critically dependent on the precise control of downstream exit pressure, especially in the presence of stochastic heat fluctuations. This paper presents a stochastic control approach for regulating the downstream exit pressure in a furnace-fired boiler subject to random heat fluctuations. A stochastic model of the boiler dynamics is developed, incorporating heat transfer and combustion uncertainties. By leveraging disruptive technology, such as the model predictive control (MPC), strategies were designed to optimize the downstream exit pressure in real-time, and minimizing deviations from the set point. Simulation studies demonstrated the effectiveness of the proposed approach in maintaining a stable exit pressure despite random heat fluctuations. Results show significant improvements in boiler performance and efficiency compared to traditional proportional integral derivative (PID) control. The proposed stochastic control strategy offers a promising solution for reliable and efficient operation of furnace-fired boilers under uncertain conditions

Survey on 3D biometric traits for human identification

Individuals are verified and identified using Biometric technology based on their biological or behavioral traits. Biometric-based personal authentication systems are more reliable and user friendly, overruns the traditional personal authentication systems. The physiological biometric traits get abraded due to aging and massive work, while the behavioral biometric traits are having high variations due to external factors such as fatigue, and mood. Among the physiological biometric traits, Finger geometry patterns are widely deployed authentication system reason being its stability, user acceptability and uniqueness. Recent trends in Biometrics attempt to incorporate 3D domain traits, 3D reconstruction is done using 2D multiple images. 3D images are usually more robust and illumination invariant as compared to their 2D counterparts. 3D reconstruction algorithms are compared by finding mean square error (MSE)

Non-small cell lung cancer active compounds discovery holding on protein expression using machine learning models

Computational methods have transformed the field of drug discovery, which significantly helped in the development of new treatments. Nowadays, researchers are exploring a wide ranger of opportunities to identify new compounds using machine learning. We conducted a comparative study between multiple models capable of predicting compounds to target non-small cell lung cancer, we focused on integrating protein expressions to identify potential compounds that exhibit a high efficacy in targeting lung cancer cells. A dataset was constructed based on the trials available in the ChEMBL database. Then, molecular descriptors were calculated to extract structure-activity relationships from the selected compounds and feed into several machine learning models to learn from. We compared the performance of various algorithms. The multilayer perceptron model exhibited the highest F1 score, achieving an outstanding value of 0,861. Moreover, we present a list of 10 drugs predicted as active in lung cancer, all of which are supported by relevant scientific evidence in the medical literature. Our study showcases the potential of combining protein expression analysis and machine learning techniques to identify novel drugs. Our analytical approach contributes to the drug discovery pipeline, and opens new opportunities to explore and identify new targeted therapies

A reinforcement learning paradigm for Vietnamese aspect-based sentiment analysis

This paper presents the task of aspect-based sentiment analysis (ABSA) that recognizes the sentiment polarity associated with each aspect of entities discussed in customers’ reviews, focusing on a low-resourced language, Vietnamese. Unlike conventional classification approaches, we leverage reinforcement learning (RL) techniques by formulating the task as a Markov decision process. This approach allows an RL agent to handle the hierarchical nature of ABSA, sequentially predicting entities, aspects, and sentiments by exploiting review features and previously predicted labels. The agent seeks to discover optimal policies by maximizing cumulative long-term rewards through accurate entity, aspect, and sentiment predictions. The experimental results on public Vietnamese datasets showed that the proposed approach yielded new state of the art (SOTA) results in both hotel and restaurant domains. Using the best model, we achieved an improvement of 1% to 3% in the F1 scores for detecting aspects and the corresponding sentiment polarity

Laurent series intelligent multidimensional object optimization classification for crop disease detection

Rice crop disease detection and its diagnosis methods are vitally important for the agriculture field to be sustainable. Traditional methods suffer from paddy yield, complex issues, and crop diseases, leading to inefficiencies in the agriculture domain. Our research provides space for a novel approach, combining the Laurent series with an intelligent multidimensional object optimization (LIMO) classification framework based on generative adversarial networks (GANs) to recognize various types of crop diseases in agricultural fields. Through our proposed research work, IoT nodes sense the values of the field crop, and gathered information is shared with processing units through base station communication. Multi-objective and cognitive learning routing (MOCLEAR) protocol supports choosing the optimal path for data transmission improvement. Then, for image segmentation, GAN combined with cognitive residual convolution network (CRCNet) is modified to segment values from input images. After receiving segment input images, perform feature extraction and classification using significant attributes. The proposed Laurent series with IMO is newly formulated by integrating the Laurent series with Intelligent IMO algorithms. Through extensive experimentation and analysis, the proposed LIMO-based GAN network provides effective and improved performance metrics with overall accuracy, sensitivity, and specificity values at 91.5%, 92.6%, and 92.41%, respectively.

Novel framework for downsizing the massive data in internet of things using artificial intelligence

The increasing demands of large-scale network system towards data acquisition and control from multiple sources has led to the proliferated adoption of internet of things (IoT) that is further witnessed with massive generation of voluminous data. Review of literature showcases the scope and problems associated with data compression approaches towards massive scale of heterogeneous data management in IoT. Therefore, the proposed study addresses this problem by introducing a novel computational framework that is capable of downsizing the data by harnessing the potential problem-solving characteristic of artificial intelligence (AI). The scheme is presented in form of triple-layered architecture considering layer with IoT devices, fog layer, and distributed cloud storage layer. The mechanism of downsizing is carried out using deep learning approach to predict the probability of data to be downsized. The quantified outcome of study shows significant data downsizing performance with higher predictive accuracy

CycleGAN for day-to-night image translation: a comparative study

Computer vision tasks often fail when applied to night images, because the models are usually trained using clear daytime images only. This creates the need to augment the data with more nighttime image for training to increase robustness. In this study, we consider day-to-night image translation using both traditional image processing approaches and deep learning models. This study employs a hybrid framework of traditional image processing followed by a CycleGANbased deep learning model for day-to-night image translation. We then conduct a comparative study on various generator architectures in our CycleGAN model. This research compares four different CycleGAN models; i.e., the orginal CycleGAN, feature pyramid network (FPN) based CycleGAN, the original U-Net vision transformer based UVCGAN, plus a modified UVCGAN with additional edge loss. The experimental results show that the orginal UVCGAN obtains an Frechet inception distance (FID) score of 16.68 and structural similarity index ´ measure (SSIM) of 0.42, leading in terms of FID. Meanwhile, FPN-CycleGAN obtains an FID score of 104.46 and SSIM score of 0.44, leading in terms of SSIM. Considering FPN-CycleGAN’s bad FID score and visual observation, we conclude that UVCGAN is more effective in generating synthetic nighttime images