human-computer interaction
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Evaluating the Usability and Effectiveness of Desktop Voice Assistants on Windows Operating Systems
Abstract: This paper presents an in-depth empirical study of usability and effectiveness regarding the use of desktop voice assistants in Windows operating systems. While a lot of research has been done on mobile-based assistants like Siri and Google Assistant, or smart speaker platforms like Amazon Alexa, desktop-based assistants remain comparatively under-explored. A total of 176 participants were surveyed to analyze their experiences with DVA in usability, multitasking, accuracy, accessibility, and overall …
Published in Journal of Operating Systems Development & Trends · Vol. 13, Issue 1, 2026 Read article
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An Efficient Vision-Based Algorithm for Hand Pose Estimation and Cursor Control
Abstract: Conventional input devices like the keyboard and mouse are no longer necessary because gestures are becoming more popular as the most natural way to interact with computers. This project has demonstrated a novel real-time system that lets users control their computer cursors using simple hand gestures. The system facilitates an easy operation for us in terms of the use of a cursor by using techniques and hand mark detections. The …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 13, Issue 1, 2026 · pp. 24–35 Read article
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Generic Virtual Mouse
Abstract: The mouse and keyboard have been replaced by new input mechanisms brought about by the quick development of Human-Computer Interaction (HCI). Using computer vision and deep learning techniques, this study explores the possibility of replacing actual mouse inputs in a computer system with hand gestures. We created a virtual mouse system that uses a normal webcam to record hand motions. Computer vision algorithms then process these gestures to handle mouse …
Published in Current Trends in Signal Processing · Vol. 15, Issue 2, 2025 · pp. 26–32 Read article
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Revolutionary Cursor Control Systems: Enhancing Human-Computer Interaction
Abstract: This project delves into leveraging artificial intelligence within virtual mouse systems to elevate human-computer interaction. The main objective is to improve user experience, increase efficiency, and introduce new ways of interaction. Employing artificial intelligence techniques such as computer vision, gesture recognition and speech recognition, the virtual mouse system adeptly tracks hand movements, interprets voice commands, recognizes gestures, and processes user inputs for seamless interactions. Tailoring features for design, gaming, accessibility, …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 45–52 Read article
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Facial Emotion Detection and Its Applications
Abstract: Facial emotion detection (FED) is an interdisciplinary field that integrates artificial intelligence, computer vision, and machine learning to recognize and interpret human emotions based on facial expressions. The development of FED systems has been propelled by advancements in deep learning, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which enhance recognition accuracy. Feature extraction techniques, including geometric and appearance-based methods, play a crucial role in classifying emotional states. …
Published in International Journal of Optical Innovations & Research · Vol. 3, Issue 1, 2025 · pp. 8–12 Read article
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Gesture-Based Virtual HCI: Enhancing Computer Interaction
Abstract: In a time when human-computer interaction (HCI) is changing quickly, generating user-friendly interfaces is essential. This paper presents a new technique to HCI using virtual mouse and keyboard systems based on gesture recognition. This system replaces classical input devices by permitting users to control their computers with hand gestures using computer vision techniques. We created an optical mouse and keyboard technique that can recognize hand gestures recorded by a regular …
Published in Journal of Microelectronics and Solid State Devices · Vol. 11, Issue 2, 2024 Read article
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Controlling Media Player Through Hand Gesture Recognition System Using CNN and RNN Models
Abstract: Artificial intelligence markup language (AIML) project represents a pioneering endeavor in the realm of media player control through hand gesture recognition, merging advanced technologies like convolutional neural networks (CNN) and recurrent neural networks (RNN). By harnessing the image analysis capabilities of CNN, our system ensures accurate, real-time detection, and interpretation of intricate hand gestures, enabling users to interact with their media content naturally and seamlessly. What sets our project apart …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 1, 2024 · pp. 29–34 Read article