Nowadays, the IDS is being used in conjunction with the IIoT system to reduce the security risk, but on the other hand, the false rate of the IDS is very high. Therefore, in this work, a pre-training method, making use of both a deep neural network and a deep auto-encoder, has been proposed for the quick prediction of assaults with increased accuracy and a reduced false rate. The replicas were expanded using hyperparameter optimization (HPO) techniques. The proposed model delivers an alternative to deep learning construction replicas through an HPO procedure incorporating the Archimedes optimization algorithm. This optimization technique can be used to determine the hyperparameter value and the ideal categorical hyperparameter combination for improved detection performance. The DS2OS dataset is used alongside numerous other indicators to evaluate the efficacy of the developed model. The various existing techniques of assault detection have also been considered to show the effectiveness of the proposed model. Through the comparative evaluation of the outcomes, it is shown that the developed model provides better performance than the other existing models. Eventually, it is discovered that the suggested security paradigm is successful in fending off a variety of internal and external threats.

Machine learning has emerged at the forefront of technological innovation with vast potential to transform and disrupt various industries and landscapes, particularly digital pricing and cryptocurrency. This paper has examined the futuristic impacts of machine learning on these landscapes, focusing on dynamic pricing strategy, personalized pricing, fraud detection, market analysis, algorithmic trading, risk mitigation, and regulation compliance. ML improves the dataset’s efficiency and data processing rate. This advanced technology helps companies develop better pricing plans, detect fraud and theft, predict market trends, automate trading strategies, develop risk management, and assist with other compliance regulations. Ultimately, this work has uncovered the various opportunities and challenges of the implications of ML on digital pricing and cryptocurrency landscapes.

In women, breast cancer stands as a leading cause of death among all types of cancer globally. Death rates can be mitigated through early prediction of breast cancer. Various machine learning techniques play a significant role in detecting breast cancer at an early stage. It is essential to develop a machine learning model with high accuracy and precision to predict breast cancer disease effectively. This paper employs various machine learning techniques to predict breast cancer. The performance evaluation of these techniques is conducted in terms of precision, accuracy, and F1-score. The Extra Trees Classifier emerged with the highest accuracy, reaching 97.23%.

Accurate evaluations of battery State of Health (SoH) and State of Charge (SoC) are critical for Electric Vehicles (EVs) safety, performance, and durability. This study proposes a novel hybrid diagnostic framework that combines statistical analysis, machine learning, and model-based estimation to improve battery monitoring capabilities. The cell-level voltage, temperature, current, and SoC were measured on 15S2P LiFePO₄ battery pack utilizing a CAN interface. By implemting the Extended Kalman Filter (EKF), the impact of sensor noise and model errors was reduced in-comarision with the convetional Coulomb Counting techniques. The Random Forest regression model was used to train for SoH assessment with duty cycles, cycle duration, temperature gradient, and voltage spread, the model performed better than linear regression techniques with projected accuracy. The k-means clustering is used to group the cells with comparable behaviors, and the cells with outlier behaviors were identified using dynamic time warping (DTW) based on temporal deviation. The principal component analysis (PCA) is used for voltage imbalance trends identification and analysis. With the data-driven algorithms, the battery health assessments can be made reliable and interpretable by combining physical estimating techniques. This is a practical and expandable solution for improved EV battery diagnostics and life-cycle management.

This survey paper investigates the profound influence of machine learning (ML) on reshaping digital marketing strategies and practices. As ML technologies evolve rapidly, they are revolutionizing how marketers understand, engage with, and convert consumers. The paper comprehensively analyzes the key ML algorithms and their applications in the digital marketing landscape, including predictive analytics, personalized recommendation systems, sentiment analysis, and customer segmentation. It explores how ML-driven approaches empower marketers with actionable insights, enhance customer experiences, and drive business growth. Additionally, the paper discusses emerging trends, challenges, and future directions in the integration of ML with digital marketing strategies, highlighting the importance of ethical considerations, privacy protection, and ongoing innovation to harness the full potential of ML in shaping the future of marketing.