This paper addresses the significant problem of identifying the relevant background and contextual literature related to deep learning (DL) as an evolving technology in order to provide a comprehensive analysis of the application of DL to the specific problem of pneumonia detection via chest X-ray (CXR) imaging, which is the most common and cost-effective imaging technique available worldwide for pneumonia diagnosis. This paper in particular addresses the key period associated with COVID-19, 2020–2023, to explain, analyze, and systematically evaluate the limitations of approaches and determine their relative levels of effectiveness. The context in which DL is applied as both an aid to and an automated substitute for existing expert radiography professionals, who often have limited availability, is elaborated in detail. The rationale for the undertaken research is provided, along with a justification of the resources adopted and their relevance. This explanatory text and the subsequent analyses are intended to provide sufficient detail of the problem being addressed, existing solutions, and the limitations of these, ranging in detail from the specific to the more general. Indeed, our analysis and evaluation agree with the generally held view that the use of transformers, specifically, vision transformers (ViTs), is the most promising technique for obtaining further effective results in the area of pneumonia detection using CXR images. However, ViTs require extensive further research to address several limitations, specifically the following: biased CXR datasets, data and code availability, the ease with which a model can be explained, systematic methods of accurate model comparison, the notion of class imbalance in CXR datasets, and the possibility of adversarial attacks, the latter of which remains an area of fundamental research.

The rise of deep-fake technology has sparked concerns as it blurs the distinction between fake media by harnessing Generative Adversarial Networks (GANs). This has raised issues surrounding privacy and security in the realm. This has led to a decrease in trust during online interactions; thus, emphasizing the importance of creating reliable methods for detection purposes. Our research introduces a model for detecting deepfakes by utilizing an Enhanced EfficientNet B0 structure in conjunction with Temporal Convolutional Neural Networks (TempCNNs). This approach aims to tackle the challenges presented by the evolving sophistication of deep-fake techniques. The system dissects video inputs into frames to extract features comprehensively by using Multi Test Convolutional Networks (MTCNN). This method ensures face detection and alignment by focusing on facial regions. To enhance the model’s adaptability, to different scenarios and datasets we implement data augmentation techniques such as CutMix, MixUp and Random Erasing. These strategies help the model maintain its strength, against distortions found in deepfake content. The backbone of EfficientNet B0 utilizes Mobile Inverted Bottleneck Convolutions (MBConv) and Squeeze and Excitation (SE ) blocks to enhance feature extraction by adjusting channels to highlight details effectively. A Feature Pyramid Network (FPN) facilitates the fusion of scale features capturing intricate details as well, as broader context. When tested on the FFIW 10 K dataset, which comprises 10,000 videos evenly split between manipulated content, the model attained a training accuracy of 91.5 % and a testing accuracy of 92.45 %, after 40 epochs. The findings showcase the model’s proficiency, in identifying videos with precision and tackling the issue of class imbalances found in datasets – a valuable contribution, to advancing dependable deepfake detection solutions. Furthermore, the model achieves an impressive balance between accuracy and computational efficiency, attaining 92.45% testing accuracy with a lightweight computational cost of 0.45 GFLOPs, making it a highly practical choice for real-world deployment.

Sign language fills the communication gap for people with hearing and speaking ailments. It includes both visual modalities, manual gestures consisting of movements of hands, and non-manual gestures incorporating body movements including head, facial expressions, eyes, shoulder shrugging, etc. Previously both gestures have been detected; identifying separately may have better accuracy, but much communicational information is lost. A proper sign language mechanism is needed to detect manual and non-manual gestures to convey the appropriate detailed message to others. Our novel proposed system contributes as Sign Language Action Transformer Network (SLATN), localizing hand, body, and facial gestures in video sequences. Here we are expending a Transformer-style structural design as a “base network” to extract features from a spatiotemporal domain. The model impulsively learns to track individual persons and their action context in multiple frames. Furthermore, a “head network” emphasizes hand movement and facial expression simultaneously, which is often crucial to understanding sign language, using its attention mechanism for creating tight bounding boxes around classified gestures. The model’s work is later compared with the traditional identification methods of activity recognition. It not only works faster but achieves better accuracy as well. The model achieves overall 82.66% testing accuracy with a very considerable performance of computation with 94.13 Giga-Floating Point Operations per Second (G-FLOPS). Another contribution is a newly created dataset of Pakistan Sign Language for Manual and Non-Manual (PkSLMNM) gestures.

The use of Bluetooth Low Energy (BLE) beacons is becoming widespread for context-aware learning environments. This technology can be used in indoor and outdoor settings such as museums, shops, home and schools in order to identify the location of learners and their contexts. Specifically, in the constructivist view of learning, learners can use such sensing technology for constructing knowledge and experiencing learning at any time, anywhere. However, exploration of such technology has been limited in the constructivist context-aware ubiquitous learning (U-learning) literature. For this purpose, the authors evaluated two android-based U-learning applications: one was an outdoor learning environment in a garden, the other was about locating books in a library. The applications were implemented and tested with school and university students. The qualitative results are presented in this article, and show how such sensing technology is promising for both pedagogical environments.

The world’s most significant yield by production quantity is sugarcane. It is the primary source for sugar, ethanol, chipboards, paper, barrages, and confectionery. Many people are affiliated with sugarcane production and their products around the globe. The sugarcane industries make an agreement with farmers before the tillering phase of plants. Industries are keen on knowing the sugarcane field’s pre-harvest estimation for planning their production and purchases. The proposed research contribution is twofold: by publishing our newly developed dataset, we also present a methodology to estimate the number of sugarcane plants in the tillering phase. The dataset has been obtained from sugarcane fields in the fall season. In this work, a modified architecture of Faster R-CNN with feature extraction using VGG-16 with Inception-v3 modules and sigmoid threshold function has been proposed for the detection and classification of sugarcane plants. Significantly promising results with 82.10% accuracy have been obtained with the proposed architecture, showing the viability of the developed methodology.

The latest standard for video coding is versatile video coding (VVC) / H.266 which is developed by the joint video exploration team (JVET). Its coding structure is a multi-type tree (MTT) structure. There are two types of trees under the umbrella of the MTT structure. The first one is called a ternary tree (TT) and the second one is a binary tree (BT). Due to the use of brute force quest for residual rate distortion the quad tree and multi-type tree (QTMT) structure of coding unit (CU) split and contributes over 98% of the encoding time. This structure is efficient in coding, however increases computational complexity. Current paper proposes a deep learning technique to predict the QTMT based CU split rather than just the brute-force QTMT method to substantially speed up the time of encoding process for VVC/H.266 intra mode. In the first phase we developed an extensive database containing the ample CU splitting patterns with various streaming videos that is able to encourage the significant decrease of VVC/H.266 complexity by using data driven methods. in the Second phase, in accordance with the dynamic QT-MT structure at numerous locations, we suggest a multi-level exit CNN (MLE-CNN) model with a redundancy removal mechanism at different levels to determine the CU partition. In the third phase, for the training of MLE-CNN model we have established the adaptive loss function and analyzing the both unknown number of partition modes and the focus on RD cost minimization. Finally, a variable threshold decision system is established to achieve the targeted low complexity and RD performance. Ultimately experimental findings show that VVC/H.266 encoding time has reduced to 69.11% from 47.91% with insignificant bjontegaard delta bit rate (BDBR) to 2.919% from 1.023% which performs better than the existing futuristic and modern approaches.