During the Covid-19 pandemic, widespread use of social media platforms has facilitated dissemination information, fake news, and propaganda, serving as a vital source self-reported symptoms related to Covid-19. Existing graph-based models, such Graph Neural Networks (GNNs), have achieved notable success in Natural Language Processing (NLP). However, utilizing GNN-based models for propaganda detection remains challenging because challenges mining distinct word interactions storing...

Object detection is an important aspect for autonomous driving vehicles (ADV), which may comprise of a machine learning model that detects range classes. As the deployment ADV widens globally, variety objects to be detected increase beyond designated Continual object essentially ensure robust adaptation detect additional classes on fly. This study proposes novel continual method learns new class(es) along with cumulative memory from prior rounds avoid any catastrophic forgetting. The results...

On-road object detection is a critical component in an autonomous driving system. The safety of the vehicle can only be as good reliability on-road Thus, developing fast and robust algorithm has been primary goal many automotive industries institutes. In recent years, multi-purpose vision-based driver assistance systems have gained popularity with emergence deep neural network. A monocular camera developed to locate image plane estimate distance said real world or plane. this work, we...

Software project outcomes heavily depend on natural language requirements, often causing diverse interpretations and issues like ambiguities incomplete or faulty requirements.Researchers are exploring machine learning to predict software bugs, but a more precise general approach is needed.Accurate bug prediction crucial for evolution user training, prompting an investigation into deep ensemble methods.However, these studies not generalized efficient when extended other datasets.Therefore,...

Head pose estimation is one of the sensing systems needed for some intelligent surveillance, such as human behavior analysis, driver assistance, visual attention, and monitoring. These require accurate alignment head movement direction prediction. The previous methods are greatly dependent on facial landmarks depth information. Usually, measured by estimating several keypoints that a correct mapping to get results. Moreover, have detrimental effect performance when face occluded or not...

Deep learning models have revealed outstanding performance on image classification and object detection tasks. However, there is a crucial drop in when they are subject to learn new data incrementally the absence of previous training data. They suffer from catastrophic forgetting—abrupt performance. This phenomenon affects implementation artificial intelligence practical scenarios. To overcome forgetting, method has either saved memory or generated these methods computationally complex...

Monocular 3-D object detection is a low-cost and challenging task for autonomous vehicles robotics. Utilizing monocular image served as an auxiliary module growing concern recently. Currently, the expensive lidar stereo cameras have predominant performance on accurate detection, whereas monocular-based methods are considerably lower in performance. This gap minimized by reforming method single internal network. We exploit correlation between 2-D spaces, enabling boxes to leverage feature...

The Internet of Medical Things (IoMT) has become a novel paradigm for real-time healthcare applications. Artificial Intelligence (AI) based efforts have been made to address the security challenges IoMT, problem imbalance data still exists, due which AI algorithms cannot sufficiently learn malicious traffic behavior and fail identify rare anomalies in accurately. Therefore, this article, we propose an intelligent model on Software Defined Networking (SDN) Deep Learning (DL) handle...

Human emotions are variant with time, non-stationary, complex in nature, and invoked as a result of human reactions during our daily lives. Continuously detecting from one-dimensional EEG signals is an arduous task. This paper proposes advanced signal processing mechanism for emotion detection using continuous wavelet transform. The space time components the raw converted into 2D spectrograms followed by feature extraction. A hybrid spatio-temporal deep neural network implemented to extract...

The deep neural network shows excellent performance on a single task. However, networks degraded when trained continuously sequence of new tasks. This phenomenon is known as catastrophic interference. To overcome this problem, the model must be capable learning tasks and preserving old We introduce architecture with static memory to mitigate Our proposed method usage complexity. learns quickly without forgetting previously learned results show that obtains good tradeoff between previous...

Blockchain came because of the occurrence incredulity to single authorities by introducing concept network decentralization and data distribution saved in a ledger. Decentralization is used validate discrepancies majority data. The consensus mechanism collectively maintains consistency A blockchain set blocks containing transaction interconnected each other using cryptography. mining process an effort add new blockchain. computer carries out after passing several complex mathematical...

<p>Artificial intelligence (AI) has advanced rapidly and is becoming a cornerstone technology that drives innovation efficiency in various industries. This paper examines the real-world application of AI multiple sectors, including healthcare, finance, agriculture, retail, energy, automotive. Several case studies are described to understand better practical applications, results, challenges implementing AI. While many industries have reaped enormous benefits from AI, inherent include...

Unsupervised cross-domain adaptation is a challenging task for person re-identification due to the unavailability of target domain labels. Among existing methods, pseudo-Iabels-based methods have considerable performance but most them use data without labels which are difficult model learn enough features. In this paper, we generative based models that generate more data. cooperation with model, mutual learning used transfer knowledge one another ultimately improves overall performance....

Abstract The evergrowing diversity of encrypted and anonymous network traffic makes management more formidable to manage the traffic. An intelligent system is essential analyse identify accurately. Network needs such techniques improve Quality Service ensure flow secure However, due usage non‐standard ports encryption data payloads, classical port‐based payload‐based classification fail classify secured To solve above‐mentioned problems, this paper proposed an effective deep learning‐based...

Introduction Alzheimer's disease (AD) is a neurodegenerative disorder and the most prevailing cause of dementia. AD critically disturbs daily routine, which usually needs to be detected at its early stage. Unfortunately, detection using magnetic resonance imaging challenging because subtle physiological variations between normal patients visible on imaging. Methods To cope with this challenge, we propose deep convolutional generative adversarial network (DeepCGAN) for detecting early-stage...