Several computational and mathematical models of protein synthesis have been explored to accomplish the quantitative analysis of protein synthesis components and polysome structure. The effect of gene sequence (coding and non-coding region) in protein synthesis, mutation in gene sequence, and functional model of ribosome needs to be explored to investigate the relationship among protein synthesis components further. Ribosomal computing is implemented by imitating the functional property of protein synthesis.

The analysis of student performance is a data-driven process. This analysis helps to provide high-quality education, a strategic way to select quality students, predict a student's future, etc. A highly competitive and complex environment is observed due to the increase in the number of institutions and the large number of specifications in the educational area. In that scenario, the analysis of student performance faces the challenge of achieving high accuracy in examining factors like demographics, behavior, and academics for a student. We have observed that the regression technique in machine learning helps us solve this challenge. In the proposed work, we have analyzed the student performance using various regression techniques such as linear regression, lasso regression, and SVM regression. In the comparative analysis, we observed that linear regression is highly effective in real-time applications, whether the lasso regression can manage the overfitting through regularization or SVM regression can take care of high-dimensional data. In the proposed work, the maximum accuracy (98.20%) is achieved in the ANN technique, which is higher than other existing techniques. The comparative study is also shown in the results section of the paper.

The human visual system, characterized by a complex array of eye movements, plays a pivotal role in our interaction with the environment. This paper explores the three fundamental types of eye movements—fixation, saccades, and smooth pursuit—and their significance in understanding mental health and cognitive functioning. Fixation reveals patterns linked to OCD and attention disorders, while saccadic activity reflects emotional states like anxiety and depression. Smooth pursuit indicates sustained attention, with disruptions highlighting cognitive impairments. Eye tracking technology, which precisely monitors these movements, provides insights into cognitive processes and emotional states, aiding mental health diagnostics. Web-based eye tracking, using personal computers and webcams, democratizes access to this technology, making it particularly beneficial for individuals, such as those with ASD, who faces challenges in verbal communication.

Significant thoughtful research is really necessary to improve the patient outcomes and reduce the social and financial burdens associated with implant failure. The primary focus of the researchers is to minimize the major implant failure due to corrosion attributed to making orthopedic surgery safer and more effective. Hence, a critical review has been done in this present article on the various multiscale modelings based on machine learning algorithms (MLAs) to predict the corrosion behavior of magnesium (Mg) alloy implants. According to the best of the authors' knowledge, all the available multiscale modelings tools, such as artificial neural network (ANN), least absolute shrinkage and selection operator (LASSO) regression model, multiple linear regression and random forest regression (RFR) models, etc., are methodically presented and discussed in detailed here for the prediction of corrosion mechanism. Subsequently, various multiscale model tools and assessment metrics for models have been thoroughly compared and criticized for better understanding and optimizing of the corrosion behavior of implants. The comparison indicates that the RFR model may be the best option, whereas the LASSO regression model and ANNs show inefficient performance for the prediction of corrosion behavior. Apart from the multiscale modeling approach, the authors have also explored the physiology and properties of alloys, bone implant, immune and tissue system, and the corrosion control mechanisms of Mg alloy. Finally, the present review on multiscale modeling approach and assessment metrics models will enhance the knowledge and understanding of the corrosion behavior of Mg alloy for implant application.

Removing adverse weather conditions from images, such as haze, fog, rain, and snowfall, is a significant issue in several scenarios. Many techniques have been described in the literature that only involve removing specific types of adverse weather degradation. A convolutional neural network (CNN)-based all-in-one dehaze network was recently presented to remove all adverse weather conditions. But, this method contains many variables because it employs many encoder blocks for each adverse weather removal operation, and its efficiency still has to be improved. This paper concentrates on creating an effective solution to remove adverse weather from the foggy and rainy real-time images. The proposed research presented a single encoder–decoder-based transformer fusion with a multi-head attention module for real-time image dehazing. Also, the proposed method introduces a separated patches module fusion with a deep residual attention module to improve the different weather degradation problems and minimize the feature loss of degraded pixels in the transformer encoder block. The proposed method is validated and tested on real-time foggy and rainy images. The qualitative and quantitative evaluation demonstrates that the proposed method is more efficient than other methods.

Industrial sectors are reinventing in automation, stability, and robustness due to the rapid development of artificial intelligence technologies, resulting in significant increases in quality and production. Visual-based sensor networks capture various views of the surrounding environment and are used to monitor industrial and transportation sectors. However, due to unclean suspended air particles that damage the whole monitoring and transportation systems, the visual quality of the images is degraded under adverse weather conditions. This research proposed a convolutional neural network-based image dehazing and detection approach, called end to end dehaze and detection network (EDD-N), for proper image visualization and detection. This network is trained on real-time hazy images that are directly used to recover dehaze images without a transmission map. EDD-N is robust, and accuracy is higher than any other proposed model. Finally, we conducted extensive experiments using real-time foggy images. The quantitative and qualitative evaluations of the hazy dataset verify the proposed method's superiority over other dehazing methods. Moreover, the proposed method validated real-time object detection tasks in adverse weather conditions and improved the intelligent transportation system.

Vision gets obscured in adverse weather conditions, such as heavy downpours, dense fog, haze, snowfall, etc., which increase the number of road accidents yearly. Modern methodologies are being developed at various academics and laboratories to enhance visibility in such adverse weather with the help of technologies. We review different dehazing techniques in many applications, such as outdoor surveillance, underwater navigation, intelligent transportation systems, object detection, etc. Dehazing is achieved in four primary steps: the capture of hazy images, estimation of atmospheric light with transmission map, image enhancement, and restoration. These four dehazing procedures allow for a step-by-step method for resolving the complicated haze removal issue. Furthermore, it also explores the limitations of existing deep learning-based methods with the available datasets and the challenges of the algorithms for enhancing visibility in adverse weather. Reviewed techniques reveal gaps in the case of remote sensing, satellite, and telescopic imaging. In the experimental analysis of various image dehazing approaches, one can learn the effectiveness of each phase in the image dehazing process and create more effective dehazing techniques.

Real-time emotion detection based on facial expression is an innovative research field that has been applied in several areas, such as health, human–machine vision, and autonomous safety. Researchers in object detection are involved in developing methods to interpret, code facial expressions, and extract these features to be better predicted by machines. Furthermore, the success of deep learning with different architectures is exploited to achieve better performance. But these methods drastically fail in excessive sweating in different health conditions. We aim to create a dataset in different health conditions and detect facial emotion using the encoder and decoder-based deep learning methodology. The proposed architecture and the dataset present the progress made by comparing the other proposed methods and the quantitative and qualitative results obtained. The major benefit of our study is to enhance the emotion detection efficiency with other proposed methods and real-time applications for different health conditions. We propose the application of feature extraction of facial expressions with an end-to-end attention module-based fusion network for detecting different facial emotions (happy, angry, neutral, surprised, etc.) with an accuracy of 99.68%. The proposed system depends upon the human face; as we know, the face reflects human brain activities or emotions.

Single image defogging that aims to restore a fog-free image from its appropriately unconstrained hazy environment is a fundamental yet complex work that has recently achieved enormous interest. However, images reconstructed by certain available haze-removal approaches frequently retain artefacts, and color distortions, drastically degrading the visual quality and adversely affecting vision tasks. To that aim, we propose an encoder-decoder model that combines feature fusion with channel and color attention to improve real-time dehazing performance. Feature fusion block analyzes distinct features and pixels unequally, allowing for greater mobility in handling multiple types of input features and increasing model efficiency. The detailed quantitative and qualitative evaluation findings show that the suggested technique outperforms state-of-the-art techniques on dehazing data sets and real-time hazy images.

Transformers have been extensively employed in various vision issues, particularly visual recognition and detection. Detection transformers are connected to end-to-end networks for object detection. Self-attention modules in the transformer give huge efficiency, making excellent object detection models. The decoder transformer fails to initialize query content properly and also fails to provide specific prior knowledge, which might potentially enhance inductive bias. This paper uses encoder and decoder transformers for object detection in deep foggy conditions. High-Resolution Network (HRNet) has been used in the backbone of this architecture to extract deep feature representation. The proposed method validates and compares with other detection techniques in terms of average precision (AP), the variety of factors, and frames per second (FPS) using the Foggy Cityscapes dataset. The qualitative results indicate that the proposed technique improves detection accuracy in deep foggy conditions.

A microgripper has been designed here. The 12V D.C. Servomotor and Radial Cam-Knife edge follower mechanism carry out actuation. Two jaws are being deployed, out of which one is fixed, and another is movable. The fixed Jaw has straight fingers, while the movable Jaw hinged with the base carries a pivoted knife edge follower and curved fingers.

In recent years, salient object detection (SOD) has achieved significant progress with the help of convolution neural network (CNN). Most of the state-of-the-art methods segment the salient object by either aggregating the multilevel features from the CNN module or introducing the refinement module along with the baseline network. However, these models suffer from simplicity bias, where neural networks converge to global minima using the simple feature and remain invariant to complex predictive features. Very few methods concentrate on the neurophysiological behaviour of visual attention. As per Gestalt psychology, humans tend to perceive the objects as a whole rather than focus on the discrete elements of that object. The law of Closure (closed contour) is one of the Gestalt axioms that states that if there is a discontinuity in the object’s contour, we perceive the object as continuous in a smooth pattern. This paper proposes a two-way learning network, where Closure-guided Attention Network (CGAN) and the Coarse Saliency Networks (CSN) jointly supervise the feature-channel to mitigate the simplicity bias. Furthermore, a channel-wise attention residual network is incorporated in the Closure Guided module to alleviate the scale-space problem and generate smooth object contour. Finally, the closure map from CGAN fused with the coarse saliency map of the Coarse Saliency Network generates a salient object. Experimental result on five benchmark datasets demonstrates the significant improvements in our approach over the state-of-the-art method.

Industry 4.0 offers a radical transformation to increase cost-effective, flexible, and efficient production of higher-quality fully automated systems by collecting and analyzing data across machines. From the last few decades, power industry has started to focus on real-time systems instead of using static methodology in periodical boiler inspection. The power plant undergoes sudden break down due to cracks and foreign bodies causing huge economic loss to the plant as well as the country. To avoid such unforeseen breakdown, most of the power plants has adopted inspection and monitoring system as a regular solution. Visual inspection is one of the most popular techniques for such inspections using a tiny camera with high-power LEDs (Known as Borescope). But it has several limitations for circumferential (360°) and longitudinal (2000mm) coverage and also equidistance inspection from the center of the header is not possible using a conventional Borescope. A specific Digital Video Recorder (DVR) used for the inspection and monitoring is not sufficient to resolve multipurpose requirements such as position of the foreign body and crack, feature of magnification, and more important is data log including plant information and crack details with images. A real-time inspection module has been developed integrated with robotic (AI) based on computer vision to make the inspection dynamic and fully automated.

Depth is one of the primary visual cues which distinguish an object from its background. In recent years, salient object detection has achieved great success with the help of a convolution neural network and its corresponding depth map. Previous methods have already utilized depth map to improve the precision of the results; however, all of the previous methods are only concentrating on the available RGB-D datasets to train their network. In this paper, we used a depth estimation network to find the depth map of 2D images. That depth map has been used to train the depth-guided saliency network, which produces the intermediate depth saliency map. Finally, the depth saliency map has been fused with the coarse saliency map to obtain the final saliency map. Experiments demonstrate the effectiveness of the proposed method, which achieves state-of-the-art performance on six popular benchmarks.

Scientists and researchers worldwide are developing systems that prove functional over more extended periods and overcome a certain level of terrain undulations. The problems faced while designing a system are issues regarding compliance, endurance, communication, and feedback. Systems taking care of all these issues in a coherent manner are rare. This paper demonstrates the development and analysis involved in designing a Terrain Adaptive Mobile Robot (TAMR) that can successfully address all the above-mentioned issues. MSC ADAMS was used to test the robot's virtual prototype while moving over an obstacle, and Matlab Simulink was used to design the Control System Architecture. The individual systems incorporated in the robot are explained in the different sections of the paper lucidly.

Visual attention has a primary role in salient object detection. This paper presents a visual saliency detection method that extracts the salient region from an image based on Human Visual Attention System. The core idea of the proposed method contemplates the laws of the Gestalt principle for object-based visual attention. According to the law of Gestalt psychology, the convexity of an object is the most important cue to attract human attention. The convexity of an object plays a vital role to segregate the figureure from its background. This proposed method aims to unify the contrast information from the various colour channels and generate an intermediate saliency map of the desired object. Then Convex Hull-based object priors have been evaluated to estimate the final saliency map. Our approach has been validated using publicly available datasets, i.e. ECSSD and MSRA. Experimental results show that the proposed method is outperforming the existing state-of-art method.

Cognitive radio is a method where secondary user searches for a free band to utilize when licensed frequency band is not utilized. Spectrum sensing is the fundamental necessity of a cognitive radio that empowers to look for the free band and utilize accordingly. The expanded interest for portable correspondences and new remote applications raises the need to proficiently utilize the accessible range assets. This paper manages Cyclostationary based spectrum detecting in Cognitive Radios to empower unlicensed secondary users to craftily get to an authorized band. The alternative FRESH (Frequency Shift) filtering technique using knowledge of the signal cyclostationarity is used to detect the desired signal from the spectrum overlapping. Directions for improvements of these filters are given in this paper. The outcomes demonstrate that for signals which spectrally overlap, the versatile FRESH filter can perform exceptionally well while normal filters come up short.