Automatic leaf disease detection techniques are effective for reducing the time-consuming effort of monitoring large crop farms and early identification symptoms plant leaves. Although tomatoes seen to be susceptible a variety diseases that can reduce production crop. In recent years, advanced deep learning methods show successful applications based on observed However, these have some limitations. This study proposed high-performance tomato approach, namely attention-based dilated CNN...

Deep learning methods have shown early progress in analyzing complicated ECG signals, especially heartbeat classification and arrhythmia detection. There is still a great deal of space for further research on this area before reaching definite decision. This study introduced novel hybrid framework based bidirectional recurrent neural network (BiRNN) with multilayered dilated convolution (CNN) classification. Initially, the raw signals are filtered using Chebyshev Type II method Daubechies...

With an increasing number of people on the planet today, innovative human-computer interaction technologies and approaches may be employed to assist individuals in leading more fulfilling lives. Gesture-based technology has potential improve safety well-being impaired people, as well general population. Recognizing gestures from video streams is a difficult problem because large degree variation characteristics each motion across individuals. In this article, we propose applying deep...

This paper focuses on the prediction of calories burned during exercise using machine learning techniques. Due to a growing number obesity and overweight people, healthy lifestyle must be adopted maintained. study explores compares several regression models namely LightGBM, XGBoost, Random Forest, Ridge, Linear, Lasso, Logistic assess their performance that can used in systems such as fitness recommender supporting lifestyle. Our findings show LightGBM for predicting calorie burn has good...

This paper presents a work in progress for proposed method Content Based Image Retrieval (CBIR) and Classification. The makes use of the interest points detector descriptor called Speeded-Up Robust Features (SURF) combined with Bag-of-Visual-Words (BoVW). combination yields good retrieval classification result when compared to other methods. Moreover, new dictionary building which each group has its own is also proposed. Our tested on highly diverse COREL1000 database shown more...

This research enhances crowd analysis by focusing on excessive and density predictions for Hajj Umrah pilgrimages. Crowd usually analyzes the number of objects within an image or a frame in videos is regularly solved estimating generated from object location annotations. However, it suffers low accuracy when far away surveillance camera. proposes approach to overcome problem taken camera at distance. The proposed employs fully convolutional neural network (FCNN)-based method monitor...

Abstract This article discusses an effective technique for detecting abnormalities in Hajj crowd videos. In order to guarantee the identification of anomalies scenes, a trained and supervised FCNN is turned into using FCNNs temporal data. By minimizing computational complexity, incorrect movement detection utilized achieve high performance terms speed precision. FCNN-based architecture designed handle two primary tasks: feature representation outliers. Additionally, overcome aforementioned...

Traffic signs images when captured in real environment are small size compared to other objects. Thus, making it difficult be accurately detected, more so identified. Of recent, the convolutional neural network (CNN) has been tremendous progress object detection due its high accuracy and fast execution. YOLO (You Only Look Once) is an method, which uses CNN core module suited for of traffic sign environment. In this paper, we implemented YOLOv3 framework identification Malaysian With...

This paper advances video analytics with a focus on crowd analysis for Hajj and Umrah pilgrimages. In recent years, there has been an increased interest in the advancement of visible surveillance to improve safety security pilgrims during their stay Makkah. It is mainly because entirely special event that involve hundreds thousands people being clustered small area. proposed convolutional neural network (CNN) system performing multitude analysis, particular counting. addition, it also...

Almost two million Muslim pilgrims from all around the globe visit Mecca each year to conduct Hajj. Each year, number of grows, creating worries about how handle such large crowds and avoid unpleasant accidents or crowd congestion catastrophes. In this paper, we introduced deep Hajj dilated convolutional neural network (DHCDCNNet) for density analysis. This research also presents augmentation technique create additional dataset based on hajj pilgrimage scenario. We utilized a single...

With the growing number of population in world nowadays, novel human-computer interaction systems and techniques can be used to help improve their quality life. A gesture based technology maintain safety needs disable as well general people. Gesture recognition from video streams is a challenging task due high changeability features each with respect different person. In this work, we propose vision-based hand RGB data using SVM. Gesture-based interfaces are more natural, spontaneous,...

In this paper, an automated system for grading the severity level of Diabetic Retinopathy (DR) disease based on fundus images is presented. Features are extracted using fast discrete curvelet transform. These features applied to hierarchical support vector machine (SVM) classifier obtain four types levels, namely, normal, mild, moderate and severe. levels determined number anomalies such as microaneurysms, hard exudates haemorrhages that present in image. The performance proposed evaluated...

Satellite image analysis has numerous useful applications in various domains. Extracting their visual information been made easier using remote sensing and deep learning technologies that intelligently interpret clear cues. However, satellite the potential for more complex tasks, such as recommending business locations categories based on implicit patterns structures of regions interest. Nonetheless, this task is significantly challenging due to absence obvious cues highly similar appearance...

<ns3:p>Background: This paper focuses on advances in crowd control study with an emphasis high-density crowds, particularly Hajj crowds. Video analysis and visual surveillance have been of increasing importance order to enhance the safety security pilgrimages Makkah, Saudi Arabia. is considered be a distinctive event, hundreds thousands people gathering small space, which does not allow precise video footage using advanced computer vision algorithms. research proposes algorithm based...

<ns3:p>Background: This paper focuses on advances in crowd control study with an emphasis high-density crowds, particularly Hajj crowds. Video analysis and visual surveillance have been of increasing importance order to enhance the safety security pilgrimages Makkah, Saudi Arabia. is considered be a distinctive event, hundreds thousands people gathering small space, which does not allow precise video footage using advanced computer vision algorithms. aims propose algorithm based...

Communication is essential in our daily life; however, senior citizens may experience communication difficulties due to declining abilities, making it difficult for them request assistance when needed.The aim of this project develop a vision-based hand gesture recognition system assist citizens, where the will recognise variety gestures and translate into their corresponding meanings each gesture.A collection videos consisting 14 classes used build models representing tasks.This solution...

Finding a suitable retail business with potential success in specific location can be challenging for retailers. The process is often lengthy and inconsistent due to the subjective nature of expert opinions. Previous research has demonstrated several techniques that consider numerous influential attributes optimization problems. However, while many studies rely on business's core data analytical purposes, accessing this information significant constraint. This study aims address challenge...

In power converters, semiconductor devices, mainly Insulated-Gate Bipolar Transistor (IGBT), are generally used as they less costly and capable of converting electrical energy into high frequency voltage. It is critical to accurately determine the switching conduction losses devices before assembled an electronic system. The accurate values help minimize loss in a converter With aid simulation, design problems identified eliminate device destruction, parameters monitored. addition, equipment...

Utility poles are crucial infrastructure components, and efficiently assessing the safety of these structures ensuring they adhere to clearance guidelines, which specify minimum distance between pole any surrounding objects, remains a challenge; current manual inspection process is time-consuming, costly, often subjective. This work proposes an automated deep learning-inspired solution improve utility detection measure distance. The biggest challenge was lack comprehensive dataset;...