A5067753860- Advanced Image and Video Retrieval Techniques
- Advanced Neural Network Applications
- Video Surveillance and Tracking Methods
- Robotics and Sensor-Based Localization
- Brain Tumor Detection and Classification
- Multimedia Communication and Technology
- Smart Grid Energy Management
- Machine Learning in Healthcare
- Real-time simulation and control systems
- Visual Attention and Saliency Detection
- Emotion and Mood Recognition
- Multisensory perception and integration
- Artificial Intelligence in Healthcare and Education
- Microgrid Control and Optimization
- Human Pose and Action Recognition
- Cardiac Imaging and Diagnostics
- Technology Adoption and User Behaviour
- Color perception and design
- Aerospace and Aviation Technology
- Artificial Intelligence in Healthcare
- Rice Cultivation and Yield Improvement
- Advanced Image Fusion Techniques
- Plant Micronutrient Interactions and Effects
- Automated Road and Building Extraction
- ECG Monitoring and Analysis
University of Poonch Rawalakot
2018-2024
Air University
2019-2023
University of Azad Jammu and Kashmir
2018-2023
Riphah International University
2019
With advances in machine vision systems (e.g., artificial eye, unmanned aerial vehicles, surveillance monitoring) scene semantic recognition (SSR) technology has attracted much attention due to its related applications such as autonomous driving, tourist navigation, intelligent traffic and remote sensing. Although tremendous progress been made visual interpretation, several challenges remain (i.e., dynamic backgrounds, occlusion, lack of labeled data, changes illumination, direction, size)....
Increased traffic density, combined with global population development, has resulted in increasingly congested roads, increased air pollution, and accidents. Globally, the overall number of automobiles expanded dramatically during last decade. Traffic monitoring this environment is undoubtedly a significant difficulty various developing countries. This work introduced novel vehicle detection classification system for smart that uses convolutional neural network (CNN) to segment aerial...
With the advancement of technology, intelligence capabilities machines are growing day by day. Researchers committed to equip with capability thinking humanly. Currently, can sense and process information gathered from sensors. However, still there is a huge gape improve understanding real scenes. Scene fiery area research now Therefore, we have proposed model understand recognize scene using depth data make capable interpreting time scenes like humans. The recognition technique novel...
Object recognition in depth images is challenging and persistent task machine vision, robotics, automation of sustainability. tasks are a part various multimedia technologies for video surveillance, human–computer interaction, robotic navigation, drone targeting, tourist guidance, medical diagnostics. However, the symmetry that exists real-world objects plays significant role perception both humans machines. With advances sensor technology, numerous researchers have recently proposed RGB-D...
To examine the features of complex visual world, sensor technology merged with objects characteristics to scenes well. These understanding are highly demanding task in different domains visionary technologies like autonomous driving, generic object detection, sports scene identification and security. In this paper, we proposed a novel statistical segmented framework that can learn robust model separate each component. Then, component is used extract geometrical concatenate extreme points...
The latest visionary technologies have made an evident impact on remote sensing scene classification. Scene classification is one of the most challenging yet important tasks in understanding high-resolution aerial and scenes. In this discipline, deep learning models, particularly convolutional neural networks (CNNs), outstanding accomplishments. Deep feature extraction from a CNN model frequently utilized technique these approaches. Although CNN-based techniques achieved considerable...
The increasing rate of cardiovascular diseases (CVDs) has posed a tremendous challenge to their early detection and personalized treatment. This research examines the potential Artificial Intelligence (AI) for management CVDs, in particular whether it can enhance diagnostic accuracy, personalize treatment guidelines, reduce healthcare costs. A quantitative methodology was adopted survey strategy employed collecting primary data from 300 professionals consisting cardiologists, general...
Alzheimer's Disease (AD) is a neurodegenerative disorder requiring early detection. This study compares AI models—Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Random Forest (RF)—in analyzing neuroimaging data (MRI, PET) to enhance AD prediction improve diagnosis using machine learning techniques. Through the application of multi-modal in form genetic, clinical, data, also investigates effectiveness combining different types predictability models for diagnosis....
Latest advancements in vision technology offer an evident impact on multi-object recognition and scene understanding. Such scene-understanding task is a demanding part of several technologies, like augmented reality-based integration, robotic navigation, autonomous driving, tourist guide. Incorporating visual information contextually unified segments, convolution neural networks-based approaches will significantly mitigate the clutter, which usual classical frameworks during In this paper,...
The advancement in technology especially the field of artificial intelligence has opened up novel and robust ways to reanalyze many aspects human emotional behavior. One such behavioral studies is cultural impact on expression perception emotions. In-group advantage makes it easy for people same group perceive each other's emotions accurately. goal this research re-investigate behavior regarding speech. theoretical basis grounded dialect theory For purpose study, six datasets audio speeches...
Traffic monitoring plays a vital role in the current world. Previously, stationary data collectors such as video cameras and induction loops were employed for this task. However, availability of unmanned aerial vehicles (UAV) has opened up new horizons task numerous research projects are being conducted field. But object detection tracking become challenging case images due to presence high density objects, view angles, different illumination changes, varying altitudes drone. In paper, we...
Introduction When it comes to interpreting visual input, intelligent systems make use of contextual scene learning, which significantly improves both resilience and context awareness. The management enormous amounts data is a driving force behind the growing interest in computational frameworks, particularly autonomous cars. Method purpose this study introduce novel approach known as Deep Fused Networks (DFN), comprehension by merging multi-object detection semantic analysis. Results To...
Aerodynamics Data Acquisition System (ADAS) acquires analog data from the sensors and converts it into digital signals by performing different operations such as signal conditioning, amplification, conversion. With increase in a number of processing cores, shift sequential to parallel ADAS has been observed recently. Therefore, this work, we have proposed developed Flexible (FADAS). The FADAS system aims achieve considerable performance, scalability, programmability. To validate performance...
Phytic acid is substance that stores most of phosphorus in seeds many cereals.It a strong chelating agent which can chelate essential nutrients like zinc, iron, calcium and magnesium resulting mineral deficiency masses developing countries.Low phytic acids therefore primary factor to enhance availability these nutrient combat related issues.In this study, the suitable crop stage combination zinc application was investigated decrease phytin content bioavailability rice grain.Three level...