Multi-focus image fusion (MIF) uses fusion rules to combine two or more images of the same scene with various focus values into a fully focused image. An all-in-focus image refers to a fully focused image that is more informative and useful for visual perception. A fused image with high quality is essential for maintaining shift-invariant and directional selectivity characteristics of the image. Traditional wavelet-based fusion methods, in turn, create ringing distortions in the fused image due to a lack of directional selectivity and shift-invariance. In this paper, a classical MIF system based on quarter shift dual-tree complex wavelet transform (qshiftN DTCWT) and modified principal component analysis (MPCA) in the laplacian pyramid (LP) domain is proposed to extract the focused image from multiple source images. In the proposed fusion approach, the LP first decomposes the multi-focus source images into low-frequency (LF) components and high-frequency (HF) components. Then, qshiftN DTCWT is used to fuse low and high-frequency components to produce a fused image. Finally, to improve the effectiveness of the qshiftN DTCWT and LP-based method, the MPCA algorithm is utilized to generate an all-in-focus image. Due to its directionality, and its shift-invariance, this transform can provide high-quality information in a fused image. Experimental results demonstrate that the proposed method outperforms many state-of-the-art techniques in terms of visual and quantitative evaluations.

Radio Frequency Identification (RFID) is primarily used to resolve the problems of taking care of the majority of nodes perceived and tracking tags related to the items. Utilizing contactless radio frequency identification data can be communicated distantly using electromagnetic fields. In this paper, the comparison and analysis made between the Clustered RFID with existing protocols Ad hoc On-demand Multicast Distance Vector Secure Adjacent Position Trust Verification (AOMDV_SAPTV) and Optimal Distance-Based Clustering (ODBC) protocols based on the network attributes of accuracy, vulnerability and success rate, delay and throughput while handling the huge nodes of communication. In the RFID Network, the clustering mechanism was implemented to enhance the performance of the network when scaling nodes. Multicast routing was used to handle the large number of nodes involved in the transmission of particular network communication. While scaling up the network, existing methods may be compromised with their efficiency. However, the Clustered RFID method will give better performance without compromising efficiency. Here, Clustered RFID gives 93% performance, AOMDV_SAPTV can achieve 79%, and ODBC can reach 85% of performance. Clustered RFID gives 14% better performance than AOMDV_SAPTV and 8% better performance than ODBC for handling a huge range of nodes.

This research aims at developing a data driven marketing communication plan on AI proper utilization for offering vehicle safety features with major emphases on normalization, embedded feature selection and neural networks. First, the identified challenge responds to the problem of analyzing consumer information and improving the marketing strategy in car-related industries that produce significant amounts of data. Normalization methods allow to scale different data sets in the same way, thus improving model performances and decreasing variability. The obvious techniques of the feature selection which include LASSO regression and the scoring of the feature importance from gradient-based boosting models are integrated in the analysis to keep it concise by suggesting that only important predictors should be accounted for, thereby reducing computational expense. Neural networks are used due to their performance in Non-linear mapping and interaction identification in big data for predicting the consumer profile and market trends.

The utilization of Brain-Computer Interface (BCI) technology in economics offers a unique perspective on policy analysis by accurately capturing the affective and cognitive responses of individuals to economic stimuli in real-time. Biases and delayed feedback mechanisms frequently compromise the behavioral data, surveys, and simulations that traditional economic models use to assess the effects of policies. By offering a direct means to evaluate brain activity, brain-computer interfaces (BCIs) address these challenges, thereby providing real-time insights into emotional responses, preferences, and decision-making processes. This research concentrates on the potential of BCI to enhance the development, implementation, and assessment of fiscal policies. Policymakers can enhance their comprehension of the intricacies of human behavior, adjust policies to align with actual responses, and enhance the accuracy of economic forecasts by incorporating brain data.