The diagnosis and prognosis of cardiovascular disease are crucial medical tasks to ensure correct classification, which helps cardiologists provide proper treatment the patient. Machine learning applications in niche have increased as they can recognize patterns from data. Using machine classify occurrence help diagnosticians reduce misdiagnosis. This research develops a model that correctly predict diseases fatality caused by diseases. paper proposes method k-modes clustering with Huang...

Abstract The main purpose of this work is to investigate and compare several deep learning enhanced techniques applied X-ray CT-scan medical images for the detection COVID-19. In paper, we used four powerful pre-trained CNN models, VGG16, DenseNet121, ResNet50,and ResNet152, COVID-19 binary classification task. proposed Fast.AI ResNet framework was designed find out best architecture, pre-processing, training parameters models largely automatically. accuracy F1-score were both above 96% in...

The predominant means of communication is speech; however, there are persons whose speaking or hearing abilities impaired. Communication presents a significant barrier for with such disabilities. use deep learning methods can help to reduce barriers. This paper proposes learning-based model that detects and recognizes the words from person’s gestures. Deep models, namely, LSTM GRU (feedback-based models), used recognize signs isolated Indian Sign Language (ISL) video frames. four different...

Sign language is the most common form of communication for hearing impaired. To bridge gap with such impaired people, a normal people should be able to recognize signs. Therefore, it necessary introduce sign recognition system assist people. This paper proposes Transformer Encoder as useful tool recognition. For static Indian signs, authors have implemented vision transformer. language, proposed methodology archives noticeable performance over other state-of-the-art convolution architecture....

In several fields nowadays, automated emotion recognition has been shown to be a highly powerful tool. Mapping different facial expressions their respective emotional states is the main objective of (FER). this study, expression (FER) was classified using ResNet-18 model and transformers. This study examines performance Vision Transformer in task contrasts our with cutting-edge models on hybrid datasets. The pipeline associated procedures for face detection, cropping, feature extraction most...

The remote sensing surveillance of maritime areas represents an essential task for both security and environmental reasons. Recently, learning strategies belonging to the field machine (ML) have become a niche interest community sensing. Specifically, major challenge is automatic classification ships from satellite imagery, which needed traffic systems, protection illegal fisheries, control systems oil discharge, monitoring sea pollution. Deep (DL) branch ML that has emerged in last few...

Deep learning has significantly aided current advancements in artificial intelligence. techniques have outperformed more than typical machine approaches, various fields like Computer Vision, Natural Language Processing (NLP), Robotics Science, and Human-Computer Interaction (HCI). models are ineffective outlining their fundamental mechanism. That's the reason deep model mainly consider as Black-Box. To establish confidence responsibility, applications need to explain model's decision...

One of the most critical issues that marine surveillance system has to address is accuracy its ship detection. Since it responsible for identifying potential pirate threats, be able perform duties efficiently. In this paper, we present a novel deep learning approach combines capabilities Graph Neural Network (GNN) and You Only Look Once (YOLOv7) framework. The main idea method provide better understanding ship’s presence in harbor areas. three hyperparameters are used development rate, batch...

Emotion recognition is a very challenging research field due to its complexity, as individual differences in cognitive–emotional cues involve wide variety of ways, including language, expressions, and speech. If we use video the input, can acquire plethora data for analyzing human emotions. In this research, features derived from separately pretrained self-supervised learning models combine text, audio (speech), visual modalities. The fusion representation biggest challenge multimodal...

Security in the blockchain has become a topic of concern because recent developments field. One most common cyberattacks is so-called phishing attack, wherein attacker tricks miner into adding malicious block to chain under genuine conditions avoid detection and potentially destroy entire blockchain. The current attempts at include consensus protocol; however, it fails when tries add new Zero-trust policies have started making rounds field as they ensure complete attempts; are still process...

Liveness detection for fingerprint impressions plays a role in the meaningful prevention of any unauthorized activity or phishing attempt. The accessibility unique individual identification has increased popularity biometrics. Deep learning with computer vision proven remarkable results image classification, detection, and many others. proposed methodology relies on an attention model ResNet convolutions. Spatial (SA) channel (CA) models were used sequentially to enhance feature learning. A...

ABSTRACT The development of deep learning algorithms has transformed medical image analysis, especially in brain tumor recognition. This research introduces a robust automatic microbrain identification method utilizing the VGG16 model. Microscopy magnetic resonance imaging (MMRI) scans extract detailed features, providing multi‐modal insights. VGG16, known for its depth and high performance, is utilized this purpose. study demonstrates model's potential precise effective diagnosis by...

Sign language is a common way of communication for people with hearing and/or speaking impairments. AI-based automatic systems sign recognition are very desirable since they can reduce barriers between and improve Human-Computer Interaction (HCI) the impaired community. Automatically recognizing still an open challenge itself has complex structure to convey messages. The key role played by isolated signs that refer single gestures carried out hand movements. In last decade, research improved...

In recent years, deep learning strategies started to outshine traditional machine methods in a few fields, with Computer Vision being one of the most noticeable ones.The is becoming more suitable nowadays at identifying patterns from images than human visual cognitive system.It ranges raw information recording and ideas that span digital image processing, learning, computer graphics.The wide utilization has attracted many researchers incorporate their different fields disciplines.The era...