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Singh, Khushwant
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Computer Science & IT

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Pyramid Quantum Neural Network Based Resource Allocation with IoT: A Deep Learning Method Singh, Khushwant; Yadav, Mohit; Kirti; Kumar, Sunil; Sobirov, Bobur
JOIN (Jurnal Online Informatika) Vol 10 No 1 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i1.1578

Abstract

As more smart devices are connected and collecting massive quantities of data, the Internet of Things is growing rapidly. Resource management is another crucial issue since IoT networks are very diverse and often built and rebuilt dynamically. This study introduces a new kind of deep learning model known as the Pyramid Quantum Neural Network (PY-QNN) to solve the problem of resource allocation in Internet of Things systems. PY-QNN builds on quantum computing to improve the accuracy, scalability, and computation performance of Deep Learning. Because of superposition and entanglement, which increase generalization and provide faster convergence, QNNs enhance learning capabilities. The pyramid structure also helps manage the hierarchy of IoT networks. In order to forecast efficient resource assignment and implement this as soon as feasible to lower latency and boost efficiency, PY-QNN uses simulated resource and network requirements. Experimental findings demonstrate that PY-QNN outperforms baseline common deep learning techniques by reducing resource waste and offering online solutions, especially in large and complex IoT networks.
Analysis of Facial Emotion Recognition with Various Techniques Sethi, Garima; Sharma, Krishan Kant; Yadav, Mohit; Singh, Khushwant; Moreira, Fernando
JOIN (Jurnal Online Informatika) Vol 11 No 1 (2026)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v11i1.1674

Abstract

Facial emotion recognition (FER) is a prominent investigation area in computer vision and affective computing. It involves the automatic detection and analysis of human emotions based on facial expressions. The current work offers a broad analysis of the present state-of-the-art approaches, methodologies, and challenges in facial emotion recognition. The paper explores the various components involved in FER, including face detection, feature extraction, classification algorithms, and datasets. Additionally, it discusses the applications, limitations, and future directions of FER research. The aim of this research is to utilize Facial Emotion Recognition (FER) as an advancing technique with considerable ramifications across multiple sectors. Contemporary facial emotion recognition (FER) research extensively employs deep learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). To enhance the performance of the FER system, attempt various feature extraction strategies, model designs, and hyper-parameter setups. Advancements in deep learning and computer vision techniques have considerably enhanced the precision and efficacy of FER systems, allowing for the accurate detection and classification of emotions from facial expressions. Facial Emotion Recognition has advanced considerably in the precise identification and interpretation of emotions conveyed through facial expressions. Ongoing research and innovation in FER could transform multiple fields, including human-computer interface, healthcare diagnostics, market research, and beyond.
Co-Authors Kirti Moreira, Fernando Sethi, Garima Sharma, Krishan Kant Sobirov, Bobur Sunil Kumar, Sunil Yadav, Mohit