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224 articles for “neural network prediction”
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Advancements in Agricultural Forecasting: A Review of Machine Learning Based Crop Yield Prediction
Abstract: Agricultural productivity plays a critical role in global food security, and accurate crop yield prediction is essential for optimizing resource allocation and decision-making in farming. The rapid advancements in Machine Learning (ML) and Deep Learning(DL)have transformed agricultural forecasting, enabling data-driven approaches for crop prediction. This review paper provides a comprehensive analysis of various ML and DL techniques applied in crop yield forecast, highlighting the ineffectiveness, challenges, and future directions. The …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 14, Issue 3, 2025 · pp. 32–38 Read article
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Integration of Soil Profile Data in Crop Prediction Models: A Comprehensive Review
Abstract: Farmers in many areas of India are having crop production issues due to soil and climate. There is no comprehensive guide accessible to assist them in developing the appropriate types of plants using modern technology. Farmers may be unable to benefit from agricultural scientific developments due to illiteracy and may continue to rely on human practises. This complicates getting the desired yield. Crop failure, for example, could be the result …
Published in Current Trends in Signal Processing · Vol. 13, Issue 1, 2023 · pp. 6–11 Read article
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Heart Disease Evaluation Through Echocardiography Using CNN, ResetNet50, VGG16, and Image Processing
Abstract: Heart conditions stand out as primary contributors to untimely mortality among adults aged 30 and above, notably among those grappling with elevated cholesterol levels and diabetes. Detecting such ailments often necessitates the use of an echocardiogram, providing an intricate portrayal of the heart. However, precise analysis hinges on both the proper functioning of the echocardiogram apparatus and the proficiency of a skilled radiologist, a condition not always met. Manual scrutiny …
Published in International Journal of Information Security Engineering · Vol. 2, Issue 2, 2024 · pp. 25–35 Read article
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Statistical and AI Approaches to Measure Sustainability Performance of Enterprises
Abstract: Measuring sustainability performance has become a critical priority for enterprises facing increasing regulatory pressure, stakeholder expectations, and global sustainability challenges. Traditional assessment methods, largely based on static indicators and manual reporting, often struggle to capture the multidimensional, dynamic, and data-intensive nature of sustainability. This study explores the integration of statistical and artificial intelligence (AI) approaches to evaluate and enhance the sustainability performance of enterprises in a more robust, accurate, and …
Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 1, 2026 · pp. 30–36 Read article
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Educating Compilers to Learn: Utilizing Machine Learning for More Brilliant Code Optimization
Abstract: This study explores the use of machine learning (ML) approaches to compiler optimization. The now-traditional static compilation techniques are transformed into adaptive, dynamic systems capable of making context-specific advancements. Traditional compilers rely mostly on heuristic or rule-based optimization techniques. While these techniques work well in general cases, they consistently fail to adapt well within the limits of code structures that modern machines display. This limitation is especially acute in today's …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 1, 2025 · pp. 50–54 Read article
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Environmental Impact Assessment of Ocean Energy Converters Using Quantum Machine Learning
Abstract: The accelerating deployment of ocean energy converters (OECs) across tidal, wave, osmotic, and thermal domains necessitates rigorous, data-intensive environmental impact assessment (EIA) frameworks capable of modelling multi-stressor marine ecosystems in real time. Classical machine learning approaches, while operationally mature, encounter scalability bottlenecks and feature correlation limitations when applied to the high-dimensional, non-linear datasets characteristic of offshore monitoring networks. This paper presents a comprehensive quantum machine learning (QML) framework for the …
Published in Journal of Energy, Environment & Carbon Credits · Vol. 16, Issue 1, 2026 · pp. 22–31 Read article
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Artificial Intelligence-Based Optimization of Mechanical and Biocompatible Properties in Polymer Composite Implants
Abstract: Artificial Intelligence (AI) has already become a ground-breaking tool of streamlining polymer composite implants to enhance both mechanical strength and biocompatibility simultaneously. This paper recommend an AI-based multi-objective optimization model, which integrates the selection of materials, structural modelling, and biological evaluation. The in vitro biocompatibility indicators, including cytotoxicity and cell adhesion, can be used to model mechanical behavior, e.g. stress-strain behavior and fatigue behavior. To arrive at an optimal material …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Avian Echoes: Convolutional Neural Network for Bird Vocalization Detection
Abstract: Bird species identification is a complex task within ornithology that demands advanced technological solutions. This research presents an approach leveraging Convolutional Neural Networks (CNNs) for bird species recognition based on identification of bird sound, each employing unique datasets and methodologies. The objective involves a two-stage identification process, beginning with the construction of an ideal dataset. The crucial step involves converting 1D audio waveforms to 2D spectrograms, enhancing CNNs' ability to …
Published in Journal of Aerospace Engineering & Technology · Vol. 14, Issue 2, 2024 · pp. 26–37 Read article
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Enhanced Sustainable Concrete Mix Design Using LLMs and Advanced Machine Learning Techniques
Abstract: Large Language Models (LLMs) are emerging as transformative tools in materials science, offering human-like reasoning, zero-shot problem solving, and the ability to integrate fuzzy laboratory knowledge with structured data. This study extends and reinterprets the original systematic benchmark for using LLMs in sustainable concrete design, particularly for Alkali-Activated Concrete (AAC). We introduce an enhanced, multi-model framework combining LLM-based inverse design, Random Forest regression, Gaussian Process Regression (GPR), and a lightweight …
Published in Recent Trends in Civil Engineering & Technology · Vol. 16, Issue 2, 2026 · pp. 26–33 Read article
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Structure–Property Modeling of Cement-Based Multi-Component Composites Using Ensemble Machine Learning and Explainable Feature Attribution
Abstract: Accurate prediction of compressive strength is central to structure–property optimization, quality control, and sustainability-driven design in cement-based composite materials. Cementitious systems represent heterogeneous multi-phase composites composed of reactive binder matrices and dispersed aggregate phases, whose macroscopic mechanical performance emerges from complex nonlinear interactions among constituents and curing-dependent microstructural evolution. This study develops a data-driven structure–property modeling framework to quantify the nonlinear dependence of compressive strength on multi-component composite composition and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 112–131 Read article
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Effect of Various Process Parameters on MRR in Manual Air Plasma Arc Cutting of AISI 1017 Mild Steel using ANN
Abstract: As per recent industrial surveys it is investigated that manufacturing companies define the quality of thermal cutting process by the dimension of work material and cutting surface appearance. Therefore, the surface roughness and material removal rate (MRR) are primarily considered. In this work the effect of three input parameters air pressure (P), cutting current (I) and cutting velocity (v) on material removal rate (MRR) is obtained experimentally. An artificial neural …
Published in Journal of Mechatronics and Automation · Vol. 2, Issue 2, 2015 · pp. 8–14 Read article
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Age, Gender And Emotion Detection
Abstract: The main reason is the development of a method to automatically estimate the age and gender of the human face. It continues to play an important role in computer vision and pattern recognition. In addition to age determination, facial emotion recognition also plays an important role in computer vision. Nonverbal communication methods such as facial expressions, eye movements, and gestures are used in many human-computer interaction applications. Much research has …
Published in Recent Trends in Sensor Research & Technology · Vol. 9, Issue 1, 2022 · pp. 1–6 Read article
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Symmetry Breaking in Mathematical Models: Bifurcation, Chaos, and Pattern Formation
Abstract: Symmetry breaking serves as a central organizing principle in the understanding of nonlinear systems across physics, biology, chemistry, and engineering. When a system transitions from a symmetric state to an asymmetric configuration, it often signals the onset of new structures, dynamic behaviors, or even chaotic regimes. This review explores symmetry breaking from the theoretical and mathematical perspectives of bifurcation theory, chaos theory, and pattern formation. We discuss how small parameter …
Published in Emerging Trends in Symmetry · Vol. 1, Issue 2, 2025 · pp. 25–30 Read article
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Implementation of Human Gesture Recognition Using CNN
Abstract: A gesture popularity system based entirely on convolutional neural networks (CNNs). Preprocessing techniques include segmentation, polygonal approximation, contour construction, morphological filters, and resource characteristic extraction. Various convolutional neural networks are employed for training and testing, with results compared to existing architectures and protocols. All generated measurements and convergence graphs produced at any point during education are examined and contested in order to verify the reliability of the approach offered. Our …
Published in Journal of Control & Instrumentation · Vol. 15, Issue 2, 2024 · pp. 24–37 Read article
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Using Machine Learning to Guess Photochemical Reaction Pathways
Abstract: Photochemical reactions are crucial to many activities in the fields of energy conversion, environmental cleanup, and synthetic chemistry. However, predicting their causes and results effectively is still very hard since they entail excited electronic states, nonadiabatic transitions, and complicated potential energy surfaces. Machine learning (ML) has been a powerful technique to go along with classic quantum chemistry methods in the last few years. It offers better prediction capability and lower …
Published in International Journal of Photochemistry and Photochemical Research · Vol. 3, Issue 2, 2025 · pp. 01–12 Read article
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Measuring Microstructure, Wear Resistance, and Mechanical Reliability Enhancement in Polymer Nanocomposites via Data-Driven Analysis with Deep Learning
Abstract: Polymer nanocomposites have gained great attention owing to their superior mechanical performance, better wear resistance and customizable microstructural properties for aerospace, automotive, medicinal and industrial engineering applications. However, the correct evaluation of the link between the microstructure evolution and the material reliability is a huge issue due to the intricacy of nanoscale interactions and diverse material characteristics. In this study, we propose a data-driven approach that integrates deep learning and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Analysis of Various Tools of Natural Language Processing Based on Developers Perspective
Abstract: The subject of natural language method has visible mind-blowing development in current years, with neural network community substitution numerous of the traditional structures. A subset of artificial intelligence known as “natural language processing”, or NLP, analyses, comprehends, and generates natural human languages so that computers can reuse written and spoken human language without resorting to computer-generated language. Semantics and syntax are used in natural language processing, which is sometimes referred …
Published in Journal of Communication Engineering & Systems · Vol. 12, Issue 2, 2022 · pp. 1–5 Read article
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Early Warning Flood Forecasting Using Long Short-Term Memory Network
Abstract: AbstractFlooding is the natural disaster which leads to massive loss of life and property as well. India faces this situation every year and millions of people are being displaced due to loss of shelter. Early warning of flood disaster in corresponding locality provides sufficient time to protect their precious life and property. However, the range of flood prediction introduces the issue of cost, reliability and maintenance. We are proposing the …
Published in Current Trends in Information Technology · Vol. 10, Issue 3, 2020 · pp. 30–34 Read article
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Explainable Sentiment Mining Model in Mental Health Forums for Emotion Classification and Justification
Abstract: Understanding and interpreting emotions expressed in online mental health discussions plays a crucial role in enabling early detection of psychological distress and facilitating timely interventions. As individuals increasingly turn to digital platforms to share personal experiences and seek support, automated systems capable of accurately identifying emotional states can significantly assist clinicians, moderators, and support communities. This paper presents a deep learning–based sentiment mining and emotion classification framework specifically designed to …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 4, Issue 1, 2026 · pp. 22–32 Read article
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A Hybrid Mathematical Model for Epidemic Outbreak Forecasting Using Machine Learning and Cloud Computing
Abstract: The increasing frequency of infectious disease outbreaks has emphasized the necessity for intelligent epidemic surveillance systems capable of predicting disease spread at an early stage. Conventional outbreak detection approaches rely heavily on delayed statistical reporting and manual monitoring techniques, resulting in reduced responsiveness during critical periods. This paper presents a mathematical predictive framework for epidemic outbreak detection using machine learning and cloud computing technologies. The proposed framework integrates the Susceptible–Infected–Recovered …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 13, Issue 2, 2026 · pp. 01–06 Read article