The emergence of deepfake technology has spurred the need for robust and adaptive methods to detect manipulated media content. This study explores the integration of the Integrate-backward-integrate (IbI) Logic Optimization Algorithm with Convolutional Neural Networks (CNNs) for enhanced deepfake detection. The proposed approach involves a multi-phase iterative process: the CNN initially trained on a diverse dataset encompassing both real and deepfake images. The CNN serves as the foundation for the IbI-driven optimization. The integration phase employs the trained CNN to forward-integrate images, classifying them as real or deepfake. Subsequently, the IbI Logic Optimization Algorithm engages in the backward phase, utilizing feedback from the CNN's performance to iteratively refine the network's parameters, architecture, and feature extraction capabilities. This iterative optimization process aims to adaptively enhance the CNN's ability to discern subtle nuances between authentic and manipulated visuals. The re-integration phase evaluates the refined CNN's performance through multiple iterations, seeking to iteratively improve deepfake detection accuracy. Validation occurs using separate datasets to prevent overfitting and ensure the model's generalizability. The proposed method aims to enhance the CNN's adaptability to evolving deepfake techniques, addressing the dynamic nature of manipulative media creation. This fusion of IbI Logic Optimization with CNNs presents a promising avenue for bolstering deepfake detection capabilities. However, the effectiveness of this approach relies on dataset quality, network architecture, and the dynamic nature of deepfake generation techniques. Continuous refinement and validation are essential to adapt the model to new challenges posed by advancing deepfake technologies.

Nowadays, Finite Impulse Response (FIR) filters are used to change the attributes of a signal in the time or frequency domain. Among FIR filters, a reconfigurable filter has the advantage of changing the coefficient in real-time, while performing the operation. In this paper, the Anti-Symmetric Product Coding (APC) and Odd Multiple Storage (OMS) modules are utilized to implement the reconfigurable FIR filter (RFIR–APC–OMS). Herein, the APC–OMS module is used to reduce the area of the RFIR architecture. The performance of the RFIR–APC–OMS is analyzed in terms of: area, power, delay, LUT, flip flop, slices, and frequency. RFIR–APC–OMS has reduced 3.44% of area compared to the existing RFIR architecture employing the Dynamic Reconfigurable Partial Product Generator (DRPPG) module.

The first stage is to extract fine details from a picture using Red Green Blue (RGB) colour space is colour image segmentation. Most grayscale and colour picture segmentation algorithms use original or updated fuzzy c-means (FCM) clustering. However, due to two factors, the majority of these methods are inefficient and fail to produce the acceptable segmentation results for colour photos. The inclusion of local spatial information often results in a high level of computational complexity due to the repetitive distance computation between clustering centres and pixels within a tiny adjacent window. The second reason is that a typical neighbouring window tends to mess up the local spatial structure of images. Color picture segmentation has been improved by introducing Deep Convolution Neural Networks (CNNs) for object detection, classification and semantic segmentation. This study seeks to build a light-weight for object detector that uses a depth and colour image from a publically available dataset to identify objects in a scene. It's likely to output in the depth way by expanding the YOLO network's network architecture. Using Taylor based Cat Salp Swarm algorithm (TCSSA), the weight of the suggested model is modified to improve the accuracy of region extraction findings. It is possible to test the detector's efficacy by comparing it to various datasets. Testing showed that the suggested model is capable of segmenting input into multiple metrics using bounding boxes. The results shows that the proposed model achieved 0.20 of Global Consistency Error (GCE) and 1.85 of Variation of Information (VOI) on BSDS500 dataset, where existing techniques achieved nearly 1.96 to 1.86 of VOI and 0.25 to 0.22 of GCE for the same dataset.

Cognitive Radio (CR) is very essential in day-to-day life as there is a growing demand for usage of frequency spectrum for different wireless applications. Being an opportunistic communication scenario, cognitive radio performance analysis attains prime significance. Performance analysis of a cognitive radio scenario in a wireless channel is done by a well-known performance metric Bit Error Rate (BER). However, the wireless channel coefficients or channel state information (CSI) are always unknown in any practical cognitive scenario. Hence, it needs to be estimated by estimation approaches like least squares (LS), minimum mean square error (MMSE) and maximum likelihood (ML). Least squares approach of estimation of wireless channel coefficients satisfies linearity property and is simple in computation, however it produces a lesser Mean Square Error (MSE) in comparison to MMSE and ML based approaches. But MMSE and ML approaches are based on the probability density function (PDF) which generally leads to increased computational complexity to determine the estimate of the wireless channel. However, a moderate MSE performance is sufficient for performance analysis of a CR scenario due to spectrum sensing. Least squares based approach provides an estimate of the wireless channel with very less computational complexity. Hence, this paper uses LS to estimate the wireless channel coefficients. In addition bit error rate performance of a cognitive radio scenario is analyzed using the obtained LS estimate of the wireless channel. Simulation results are obtained to analyze the LS performance of CR scenario using mean square error and BER performance metric. The obtained simulation results can be used as a benchmark for analysis of cognitive radio environments.