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Lung cancer, the second most common type of cancer worldwide, presents significant health challenges. Detecting this disease early is essential for improving patient outcomes and simplifying treatment. In this study, we propose a hybrid framework that combines deep learning (DL) with quantum computing to enhance the accuracy of lung cancer detection using chest radiographs (CXR) and computerized tomography (CT) images. Our system utilizes pre-trained models for feature extraction and quantum circuits for classification, achieving state-of-the-art performance in various metrics. Not only does our system achieve an overall accuracy of 92.12%, it also excels in other crucial performance measures, such as sensitivity (94%), specificity (90%), F1-score (93%), and precision (92%). These results demonstrate that our hybrid approach can more accurately identify lung cancer signatures compared to traditional methods. Moreover, the incorporation of quantum computing enhances processing speed and scalability, making our system a promising tool for early lung cancer screening and diagnosis. By leveraging the strengths of quantum computing, our approach surpasses traditional methods in terms of speed, accuracy, and efficiency. This study highlights the potential of hybrid computational technologies to transform early cancer detection, paving the way for wider clinical applications and improved patient care outcomes. [ABSTRACT FROM AUTHOR]

Addressing the intricacies of facial aging in forensic facial recognition, traditional sketch portraits often fall short in precision. This study introduces a pioneering system that seamlessly integrates a de-aging module and a sketch generator module to overcome the limitations inherent in existing methodologies. The deaging module utilizes a deepfake-based neural network to rejuvenate facial features, while the sketch generator module leverages a pix2pix-based Generative Adversarial Network (GAN) for the generation of lifelike sketches. Comprehensive evaluations on the CUHK and AR datasets underscore the system's superior efficiency. Significantly, comprehensive testing reveals marked enhancements in realism during the training process, demonstrated by notable reductions in Frechet Inception Distance (FID) scores (41.7 for CUHK, 60.2 for AR), augmented Structural Similarity Index (SSIM) values (0.789 for CUHK, 0.692 for AR), and improved Peak Signal-to-Noise Ratio (PSNR) metrics (20.26 for CUHK, 19.42 for AR). These findings underscore substantial advancements in the accuracy and reliability of facial recognition applications. Importantly, the system, proficient in handling diverse facial characteristics across gender, race, and culture, produces both composite and hand-drawn sketches, surpassing the capabilities of current state-of-the-art methods. This research emphasizes the transformative potential arising from the integration of de-aging networks with sketch generation, particularly for ageinvariant forensic applications, and highlights the ongoing necessity for innovative developments in de-aging technology with broader societal and technological implications. [ABSTRACT FROM AUTHOR]

The identification of bacterial colonies is deemed to be crucial in microbiology as it helps in identifying specific categories of bacteria. The careful examination of colony morphology plays a crucial role in microbiology laboratories for the identification of microorganisms. Quantifying bacterial colonies on culture plates is a necessary task in Clinical Microbiology Laboratories, but it can be time‐consuming and susceptible to inaccuracies. Therefore, there is a need to develop an automated system that is both dependable and cost‐effective. Advancements in Deep Learning have played a crucial role in improving processes by providing maximum accuracy with a negligible amount of error. This research proposes an automated technique to extract the bacterial colonies using SegNet, a semantic segmentation network. The segmented colonies are then counted with the assistance of blob counter to accomplish the activity of colony counting. Furthermore, to ameliorate the proficiency of the segmentation network, the network weights are optimized using a swarm optimizer. The proposed methodology is both cost‐effective and time‐efficient, while also providing better accuracy and precise colony counts, ensuring the elimination of human errors involved in traditional colony counting techniques. The investigative assessments were carried out on three distinct sets of data: Microorganism, DIBaS, and tailored datasets. The results obtained from these assessments revealed that the suggested framework attained an accuracy rate of 88.32%, surpassing other conventional methodologies with the utilization of an optimizer. [ABSTRACT FROM AUTHOR]

In the past, sketches were a standard technique used for recognizing offenders and have remained a valuable tool for law enforcement and social security purposes. However, relying on eyewitness observations can lead to discrepancies in the depictions of the sketch, depending on the experience and skills of the sketch artist. With the emergence of modern technologies such as Generative Adversarial Networks (GANs), generating images using verbal and textual cues is now possible, resulting in more accurate sketch depictions. In this study, we propose an adversarial network that generates human facial sketches using such cues provided by an observer. Additionally, we have introduced an Inverse Gamma Correction Technique to improve the training and enhance the quality of the generated sketches. To evaluate the effectiveness of our proposed method, we conducted experiments and analyzed the results using the inception score and Frechet Inception Distance metrics. Our proposed method achieved an overall inception score of 1.438 ± 0.049 and a Frechet Inception Distance of 65.29, outperforming other stateof-the-art techniques. [ABSTRACT FROM AUTHOR]

The process of identifying a criminal is extremely challenging if no eyewitnesses are available or closedcircuit television (CCTV) footage does not show the criminal's face. With the use of high-definition cameras and soft biometrics, forensic science has made great strides in identifying criminals based on their bodily traits. Due to the use of one type of soft biometric for identification, many researchers have focused on this area and have failed to achieve better matching. Despite the fact that similar soft biometrics share many of the same features, one type of soft biometric matching has been insufficient and resulted in inaccurate results. As a result, it becomes difficult to make distinct classifications. Therefore, this study proposes the implementation of a suspect identification system that uses mole pattern analysis and skin tone matching as soft biometrics. The deep residual pooling network is employed to analyze mole patterns, while the histogram equalization method is employed to analyze skin tones. We have also improved the performance of the deep seg-net and the histogram equalization technique by incorporating an aggregator operator and an adaptive pixel-wise noise cancellation filter. The experimental results show that the proposed method attains higher accuracy when the loss rate is gradually decreased. The proposed method attains 89.21% accuracy which is higher than the existing approaches and the efficiency of the proposed method is compared with various texture encoders. [ABSTRACT FROM AUTHOR]

Emotions are noteworthy signs to understand the intentions of others peoples during communication with them. Similarly, in order to identify the different emotional states of individual such as joy, sadness, and anger, using facial expressions and vocal tone are less effective due to the variations in facial and vocal outputs. Thus, for recognizing emotions intensely without variation physique postures analysis can give effective outputs. Thus, physique posture analysis of the individual can be obtained by mapping the joints using Reeb graph which plots all the joints into carvative structure to learn posture, and the angles between the joints of posture are detected by law of cosines which depicts the vigorous expression along with postures. In addition to that much more reliable recognition of emotions without distractions can be obtained through the detailed features by preprocessing the input image through the fusion of Median and Wiener filter which avoids all the five types of noise occurrence so detailed features such as Invariant, Depth sequential silhouettes and Spatiotemporal body joint can be obtained to aid efficient analysis of posture that can pave a way to identify the different emotions by tactic Tree based classifier to get better performance in terms of execution time and accuracy. [ABSTRACT FROM AUTHOR]