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13 articles for “Deep Neural Networks (DNN)”
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AI-BASED SMART RESOURCE DEMANDAND PRICE PREDICTION USING MACHINE LEARNING FOR EFFICIENT RESOURCE MANAGEMENT
Abstract: AI and machine learning models now help manage resource supplies and price changes across key Indian markets. The development and application of advanced predictive analytics in India's agriculture supply chain and wholesale energy market systems are examined here. Although agriculture occupies over 40% of India's land area, it accounts for approximately 15–18% of the national Gross Domestic Product (GDP). In order to regulate the PDS, unify land information, and optimize …
Published in Trends in Electrical Engineering · Vol. 16, Issue 3, 2025 Read article
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Melody Extraction from Polyphonic Music Using Deep Neural Network: A Literature Survey
Abstract: Abstract: Melody extraction plays an important role in the field of Music Information Retrieval (MIR). It has emerged as one of the active research problems in the MIR applications. Nowadays, the music providers have to facilitate searching of music based on their contents or recommend music based on user’s interest having similar contents. Melody extraction is necessary to fulfil these user-interest driven searching and recommendation. The main objective of melody …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 6, Issue 3, 2019 · pp. 16–21 Read article
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A Review Of Deep Learning Applications For Speech Processing Improvement
Abstract: Improve the quality of the spoken word is a common goal for many audio and speech signal processing applications. A noisy voice signal's quality and understandability may be improved via speech augmentation. Speech augmentation is critical in a wide range of fields, including hearing aids, ASR, and mobile communication. DNN-based architectures for speech recognition and augmentation have shown to be quite effective in recent years, according to a new study. …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 9, Issue 2, 2022 · pp. 14–19 Read article
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AI-Driven Prediction of Square-Hole Laser Trepanning Performance in AA7075/15%SiC/15% Glass Fiber Hybrid Composites Using Taguchi–ANOVA and Deep Neural Networks
Abstract: Hybrid AA7075 composites reinforced with 15% silicon carbide (SiC) and 15% glass fiber were fabricated via the stir casting technique to improve machining and structural performance. The addition of dual reinforcements into the aluminum matrix was aimed at enhancing hardness, thermal stability, and surface quality during non-traditional drilling operations. Square-hole drilling was performed using a laser trepanning process, and the key responses—hole size accuracy, surface roughness, and taper angle—were systematically …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1932–1943 Read article
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An Intelligent AI–ML Framework for Deepfake Generation and Detection
Abstract: Recent developments in Artificial Intelligence (AI) and Machine Learning (ML) have led to the rise of deepfake technology, making it one of the most impactful innovations in the digital world. Deepfakes are synthetic forms of digital media generated using deep learning algorithms capable of realistically manipulating facial expressions, voice patterns, images, and video content. Although these technologies provide innovative applications in entertainment, virtual reality, healthcare, and education, their misuse has …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 2, 2025 · pp. 25–32 Read article
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Data Handling Algorithms for the Healthcare System for the Prediction of Diabetes in Health Data Science (HDS): A Review Report
Abstract: In recent years, diabetes has become the biggest disease in different countries around the world. This disease is caused by adulteration in food ingredients, unhealthy food habits, a lack of physical exercise, and changing the lifestyle every time without a routine chart. The main objective of this review paper is to provide a proper understanding of the machine learning algorithm used in the healthcare system to handle diabetic patients' data. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 2, 2024 · pp. 1–10 Read article
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A Study on The Impact of Artificial Intelligence in Pharmaceuticals
Abstract: The main goal of artificial intelligence (AI) is to create intelligent modeling, which facilitates knowledge imagination, problem-solving, and decision-making. AI is becoming more and more significant in several pharmacy domains, including polypharmacology, hospital pharmacy, drug discovery, and drug delivery formulation development. Various types of artificial neural networks (ANNs), including deep neural networks (DNNs) and recurrent neural networks (RNNs), are utilized in the development of drug delivery formulations and in drug …
Published in Research & Reviews: A Journal of Drug Design & Discovery · Vol. 12, Issue 1, 2025 · pp. 24–32 Read article
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Comparison and Analysis of Facial Emotion Detection Using Various Deep Learning Neural Networks
Abstract: Facial emotion recognition employs Convolutional Neural Networks (CNNs), Residual Networks (ResNet), Long Short-Term Memory (LSTM) networks, and Deep Neural Networks (DNNs) to automatically identify various emotions, including disgust, anger, fear, happiness, sadness, surprise, and neutrality. This study utilizes transfer learning along with data preprocessing techniques such as rotation, flipping, brightness adjustment, and enhancement methods. Traditional machine learning models achieve an accuracy range of 45 to 50%. In contrast, our proposed …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 2, 2025 · pp. 37–42 Read article
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A study in Leveraging Deep Learning and IoT Arrays for Dynamic, Hyper-Local Atmospheric Intelligence
Abstract: The critical demand for high-resolution, actionable atmospheric data is challenged by the high cost and sparse coverage of traditional regulatory monitoring stations. This paper explores the synergistic paradigm shift enabled by integrating low-cost, dense Internet of Things (IoT) sensor arrays with advanced Artificial Intelligence (AI) methodologies, specifically Deep Learning (DL) models. We address the primary limitations of low-cost sensors—inherent bias, sensitivity to environmental drift (temperature/humidity), and calibration inconsistency—by utilizing AI …
Published in International Journal of Atmosphere · Vol. 2, Issue 2, 2025 · pp. 50–62 Read article
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Polypyrrole-Based Conductive Polymer-Gated Single Electron Transistor with Deep Neural Network Assistance for Biomedical Energy Harvesting and Charge Detection
Abstract: The increasing demand for intelligent biomedical monitoring systems has accelerated research into ultra-low-power sensing technologies capable of operating with high sensitivity and minimal energy consumption. Conductive polymers have attracted considerable attention for biomedical and nanoelectronic applications due to their tunable electrical properties, biocompatibility, and environmental stability. Among them, Polypyrrole (PPy) is a promising functional polymer that can enhance charge transport and electrostatic coupling in nanoscale devices. In this work, a …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Adaptive and Explainable ML Framework for Personalized E-Commerce User Experience and Satisfaction
Abstract: The user experience and user satisfaction have taken the center of the stage as a determinant of platform success in the fast-changing e-commerce ecosystem. Conventional recommendation systems are usually run in black-box models that give minimal transparency and dynamicity to altered user preferences. The study will introduce a personalization framework of explainable machine learning (XML) based on explainable artificial intelligence (XAI) and hybrid recommendation strategies. The model uses Collaborative Filtering …
Published in Current Trends in Information Technology · Vol. 16, Issue 2, 2025 · pp. 23–32 Read article
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A Survey on Neural Network based Classifier for Arrhythmia Detection
Abstract: Electrocardiogram (ECG) is one of the important diagnostic tool for the detection of the heart problem. Increasing number of cardiac patients need automatic detection techniques for various abnormalities or arrhythmias of the heart to reduce pressure on physicians and share their load. Coronary Care Units (CCUs) emphasizes on the task of accurate analysis of ECG signal at an early stage that can prevent disease, like tachycardia, to escalate there by …
Published in Journal of Communication Engineering & Systems · Vol. 8, Issue 2, 2018 · pp. 88–97 Read article
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Classification and Detection of Brain Tumor using Convolutional Neural Network
Abstract: Tumors are masses created when brain cells multiply uncontrollably. A brain tumor is the medical term for this condition. Brain tumors are a serious and aggressive disease that can lead to a reduced life expectancy. Developing a treatment plan is essential to raising a patient's standard of living. Tumors in different regions of the body are evaluated using a variety of imaging techniques, with MRI pictures being utilized mostly for …
Published in International Journal of Cheminformatics · Vol. 1, Issue 1, 2023 · pp. 8–13 Read article