Malware forensics is a field in digital forensics that is dedicated to collecting evidence of a malware attack on a system. Malware is also executable code, but unlike regular executables, it is designed to perform unauthorized actions on victim computers. Being an executable, the malware also performs designated tasks through several processes that can leave its traces (digital footprints) throughout different components of the operating system, such as registries, file systems, DLL calls, API calls, memory usage, CPU utilization, network traffic, etc. Some of these traces can exist even after the removal of the malware and can be used as evidence to verify the occurrence of an attack. The proposed malware forensic analysis technique combines both the static and dynamic features of the malware to trace its evidence. This information is collected using the Cuckoo sandbox, which executes the malware sample files in a virtual environment to generate a JSON-formatted analysis report. The Python script is used to extract the non-volatile features from the JSON format analysis report. However, combining both features results in a very long and sparse feature vector, which negatively affects the classifier’s performance. Therefore, the principal component analysis (PCA) algorithm is adopted to reduce the dimensionality of the feature vectors. The generated reduced dimensionality feature vectors are used to train the extreme learning machine (ELM). The ELM can be trained in a fraction of the time compared to the support vector machine (SVM), while achieving similar accuracy. This makes it a favorable choice, especially for malware forensic analysis, where the classifier needs to train with a large amount of data and requires quick decision-making capabilities. The performance of the proposed techniques is evaluated using 90 malicious code samples containing 41 Trojans, 28 worms, and 21 bots. To demonstrate the superiority of the proposed algorithm, a comparison with some state-of-the-art classification methods, named Hidden Markov Model (HMM), Support Vector Machine (SVM), Artificial Neural Network (ANN), and ELM, was also performed. The comparison results show that the proposed technique achieves approximately 11% higher precision, 10% higher recall, and a 10.5% higher F-score than the competitors.

Deep learning and self-supervised learning techniques have advanced, making it possible to diagnose medical images more accurately. Our goal in this work is to increase the accuracy of medical diagnosis using chest X-rays by utilising information distillation and model compression techniques. By extracting knowledge from a self-supervised model which is a teacher model (SWAV Model with ResNet-50 as backbone), we enhance inference speed and reduce the computational resources needed for correct diagnosis. Our strategy entails translating the knowledge acquired by self-supervised models to smaller, more efficient models without sacrificing accuracy. In addition, we look at how model compression and distillation affect the diagnosis’s interpretation. The results of this study may enhance medical diagnosis procedures and increase their accessibility in environments with limited resources. Our extensive experiments prove the efficacy with 97.34% accuracy for the student model with knowledge distillation.

The complexity of traffic flow patterns significant challenges in predicting traffic green signal timings using conventional methods. Most of conventional methods relied on vehicle counts and speeds. These methods often did not consider crucial factors such as Spatial Occupancy, long-term dependencies, and the non-linear relationships. Recent advancements in Convolutional Neural Networks (CNNs) have enabled better capturing of patterns in traffic data. These advancements are essential for effectively predicting vehicle Green Signal Time by considering accurate detection and tracking, Spatial Occupancy calculation, long-term dependencies, and non-linear relationships in traffic data. The PVD-GSTPS framework has been proposed as an innovative solution for predicting vehicle Green Signal Time with the help of advanced CNN. This framework leverages the capabilities of two fine-tuned object detection models YOLO v8 and Faster R-CNN for precise vehicle detection, while a Byte Sort Tracker monitors the trajectories of detected vehicles. Additionally, a vehicle counting module assesses the number of vehicles in specified areas, and a size assignment process estimates Green Signal Time based on Spatial Occupancy calculations. This study is limited by the fixed duration of the QMUL video dataset utilized. This restricts data availability and complicates the establishment of strong correlations between Green Signal Time and Spatial Occupancy. To mitigate this issue, we utilized a Generative Adversarial Network (GAN) to generate realistic synthetic data. Long Short-Term Memory (LSTM) networks and polynomial regression techniques are utilized to capture the relationships within this dataset. In this study, we used the QMUL dataset to validate our hypothesis. The results demonstrate that our PVD-GSTPS framework significantly outperforms Enhanced YOLO v8, Original YOLO v8, and Faster R-CNN.

Image captioning generates text in natural language to describe a given image. Recent advances in various object detection with attention mechanisms pushed for exploring multiple image captioning methodologies to create more meaningful and accurate captioning models. Although existing pipelines do well in describing the image, there has not been enough emphasis on relationship modeling between image features. Relationship modeling is crucial for establishing context between various objects in an image. We have proposed a novel Advanced Context-Aware Object Relational Model (ACAORM) that not only improves relationship modeling between image features but also better sentence generation due to Transformer architecture. ACAORM builds relation-aware visual representations for image description and builds captions with prior attention to relevant Regions of Interest (RoI). We tested the proposed methodology with three widely used datasets, Flickr8K, Flickr30K, and MS-COCO. The results show that it beats numerous cutting-edge techniques. ACAORM scores 0.3526, 0.4439, and 0.8813 in $$ BLEU-4 $$ on Flickr8k, Flickr30k and MS-COCO, respectively, outperforming popular cutting-edge models such as VitaCap on Flickr8k and AGF on Flickr30k and MS-COCO by 10.88%, 47.96% and 139.48% on the respective benchmarks.