Despite extensive research efforts in Facial Expression Recognition (FER), achieving consistent performance across diverse datasets remains challenging. This challenge stems from variations in imaging conditions such as head pose, illumination, and background, as well as demographic factors like age, gender, and ethnicity. This paper introduces NIFER, a novel facial expression database designed to address this issue by enhancing racial diversity in existing datasets. NIFER comprises 3,455 images primarily featuring individuals with dark skin tones, collected in real-world settings. These images underwent preprocessing through face detection and histogram equalization before being categorized into five basic facial expressions using a deep learning model. Experiments conducted on both NIFER and FER-2013 datasets revealed a decrease in performance in multiracial FER compared to single-race FER, underscoring the importance of incorporating diverse racial representations in FER datasets to ensure accurate recognition across various ethnicities.

In recent years, the rising prevalence of chronic illness has led to an increase in disability of patients. Extensive research has been done to enhance both the functional abilities as well as the quality of the affected individuals’ lives. Researchers have worked on the effects of numerous scholars, keywords and countries of these specific fields. However, a few state-of-the-art bibliometric analyses have been done in this research to reduce the quantitative aspects of the vast research fields of rehabilitation. We have precisely selected 427 core papers from the Web of Science database spanning from 1999 to 2022 where Machine Learning (ML) or Deep Learning (DL) is used in the rehabilitation field. Consequently, our analysis focuses on citation patterns, trend analysis and collaborations between countries or influential keywords offering a detailed overview of global trends in this interdisciplinary domain. Additionally, we visualize the research trends of various authors and countries which provide invaluable insights into research impact as well as collaboration networks. Overall, this paper aims to shape the evolving field of rehabilitation by providing in depth analysis of the citation landscape, key researchers, and international collaborations.    

The aim of the paper is the development of a design catalog and sustainability analyses of road layers. In this paper, the material and thickness of the layers for three different traffic load classes will be determined based on the pavement design of the Hungarian and Pakistani standards. This was achieved using the Hungarian design method and the AASHTO method adopted by the National Highway Authority in Pakistan. "This will enable engineers in the field to choose pre-established designs from the catalog.". The forefront of pavement design is the direction in which ongoing research endeavors in the field are guiding us. The empirical design, as outlined in the AASHTO 1993 version, relies on statistical models derived from road tests. Moving beyond this, the mechanistic-empirical design involves assessing stresses and strains alongside empirical models, such as the MEPDG approach. Looking ahead, a mechanistic design encompasses models based on mechanics and represents the frontier where researchers are advancing the future of pavement design. The Hungarian pavement design method (eÚT 2-1.202:2005, 2005) primarily relies on mechanistic-empirical pavement design principles. However, it limits practicing engineers to choosing predefined designs from the catalog. The Comparison was carried out between Hungarian and Pakistani pavement designs. Subsequently, comparative calculations between GRT and SBS will be made for CO2 emissions and other sustainability parameters. To achieve this aim, the Pavement LCA tool by the US Department of Transportation Federal Highway Administration was employed.

Background: Globally, Breast cancer is the utmost predominant cancer and it affects millions of women every year. Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) has been familiar as an efficient modality for diagnosing breast cancer. In spite of DCE-MRI modality being majorly utilized for the classification of breast cancer, the diagnostic performance is still deficient and misclassification occurs. 
Method: This research proposes the Deep Learning (DL) approach of Dual Attention Deep Convolutional Neural Network (DADCNN) for the classification of breast cancer into two types named benign and malignant. Initially, the DCE-MRI, Rider Breast MRI and Breast MRI datasets are utilized for estimating the effectiveness of the classifier. After collecting the dataset, pre-processing is performed by utilizing data augmentation technique. Then, the augmented data is input for the feature extraction process, which is performed by using DenseNet-121 and ResNet-101 architectures. Then, the extracted features are concatenated by using the feature fusion model and finally, classification is performed to categorize the breast cancer. The DADCNN approach deals with the long input features to selectively focus on the most relevant parts in breast cancer, so it enhances the results. The presented DADCNN approach significantly outperforms the existing methods like MUM-Net-joint prediction, UDFS + SVM, XGBoost, Multivariate Rocket and BI-RADS. The greater accuracy of the proposed DACNN approach suggests DL approach to effectively enhance the classification accuracy in breast cancer.
Results: The experimental results establish that the proposed method attains greater results in all performance metrics as compared to the exiting methods like Multi-modality Network (MUM-Net) and Multivariate Rocket algorithm, The suggested DADCNN approach attains the maximum accuracy of 0.931, specificity of 0.924, sensitivity of 0.925, AUC of 0.962, PPV of 0.853 and NPV of 0.902 in breast cancer classification, which denotes that the DACNN effectively classify the cancer into benign and malignant.  
Concluding Remarks: The DADCNN approach deals with the long input features to selectively focus on the most relevant parts in breast cancer, so it enhances the accuracy, specificity, sensitivity, AUC, PPV and NPV in breast cancer classification. 

The proposal entitled as “Wireless Vehicular Communication” deals with communication between vehicles and infrastructural units. This proposal is designed to enable communication between vehicles and the surrounding infrastructure, such as traffic signals and road sensors. The aim of this project is to improve traffic flow, reduce congestion and enhance overall road safety. This project primarily aims at providing instantaneous communication between two vehicles or between vehicles and infrastructure to ensure safety of the passenger. This system is also known as Crucial Message Exchange System (CMES). CMES not only helps in message transmission but also in collection, storage and monitoring of the messages. This system processes data and has the ability to extract crucial information for future machine language-based processing. The CMES system uses the ESP8266 module as the on-board unit in a vehicle to implement Vehicle to Infrastructure Communication (V2I). This module interacts with the Raspberry Pi board using Message Queue Telemetry Transport (MQTT) protocol to relay information. The Raspberry Pi board is considered as the embedded unit in the infrastructure. Raspberry Pi not only facilitates the collection of data but also is responsible for storage and retrieval in cloud systems. All the said components working together ensures a reliable message passing system which will be of immense help to the drivers/passengers on road.