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147 articles for “Neural networks”
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Predictive Models for Drinking Water Quality: Leveraging Machine Learning for Improved Safety and Efficiency
Abstract: Quality of drinking water is one of the critical determinants of public health, leading to severe environmental and health-related issues when poor. The paper examines the integration of machine learning techniques that could improve accuracy and efficiency in assessing drinking water quality. The traditional techniques of monitoring drinking water are resource-intensive yet lack timely insight into contamination events. In this scenario, an evaluation of the performance of ML models such …
Published in International Journal of Microelectronics and Digital integrated circuits · Vol. 11, Issue 1, 2025 Read article
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Advances in Gender Classification from Facial Images Using CNNs: A Comprehensive Review
Abstract: Predicting age and gender from facial images is important for various uses, like social interactions, security, and customizing user experiences. Despite progress in facial recognition technology, getting accurate and reliable predictions is still a challenge. This paper reviews recent advancements in Convolutional Neural Networks (CNNs) for age and gender classification, comparing their effectiveness to traditional methods. CNNs excel in extracting features from images and have shown notable improvements in accuracy …
Published in International Journal of Digital Communication and Analog Signals · Vol. 10, Issue 2, 2024 · pp. 1–5 Read article
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Facial Emotion Based Stress Detection Using CNN and Haar Cascade Algorithms
Abstract: Understanding human conduct requires the ability to recognize facial emotions, which has applications in everything from human-computer interaction to psychological wellness monitoring. This research provides a new approach to stress detection using Convolutional Neural Networks (or CNNs) and Haar Cascade classifiers. The suggested method uses CNN to recognize facial expressions and Haar Cascade algorithm for face detection. The methodology begins with preliminary processing the input photos, followed by face detection …
Published in International Journal of Microwave Engineering and Technology · Vol. 10, Issue 2, 2024 · pp. 41–50 Read article
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Automated Image Captioning Using Xception–CNN and LSTM for Descriptive Sentence Generation
Abstract: Generating descriptive texts for images is a complex and challenging task that requires advanced deep learning techniques. This paper presents a descriptive sentence generator for images using a combination of convolutional neural networks and recurrent neural networks. The proposed model utilizes a pretrained convolutional neural network, Xception, to extract image features, which are then processed by a long short-term memory network to generate meaningful textual descriptions. The model is trained …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 11, Issue 2, 2025 · pp. 20–28 Read article
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Genre Classification of Music Using Machine Learning
Abstract: One of the most important technological advances of the 21st century is artificial intelligence (AI) and machine learning. They are revolutionizing computers, banking, healthcare, agriculture, music and travel. A strong model has mastered many difficult learning tasks. Speech analysis is one area of artificial intelligence. This includes finding information about music, creating music, and categorizing music. Music data is one of the most complex source data available today. This is …
Published in International Journal of Distributed Computing and Technology · Vol. 8, Issue 2, 2022 · pp. 17–28 Read article
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Advanced Classification of Diabetic Retinopathy Using GAN and Deep Residual CNN Model
Abstract: Diabetic retinopathy (DR) is a leading cause of blindness worldwide, impacting the largest number of individuals who have diabetes. Early identification of diabetic retinopathy may help to prevent severe effects; however, the very limited availability of labeled datasets, and the possibility of overfitting of the model, presents obstacles to obtaining an accurate diagnosis. In this research, a hybrid deep learning strategy is introduced, where Generative Adversarial Networks (GANs) are used …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 12, Issue 1, 2026 · pp. 8–16 Read article
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Facial Expressions Detection Using Machine Learning
Abstract: Facial expressions are necessary in human communication and understanding emotions. First and foremost, identifying emotions is a critical factor for many businesses to comprehend how their customers are responding to the things they have introduced. It can also be utilized to find out if the amenities provided to their staff are meeting their needs. Additionally, it has a plethora of other applications, such as utilizing a camera to identify someone's …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 10, Issue 1, 2024 · pp. 11–16 Read article
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Image Caption Generator Implementation
Abstract: Image caption generation is a software technology that takes an image as an input and produces a descriptive caption in text form. In the modern era, this technology finds application in various fields. For instance, automatically generating captions for medical images aids in diagnosis and enhances reporting efficiency, helping healthcare professionals to quickly interpret complex visuals. In the realm of autonomous vehicles, image captioning enables these vehicles to understand and …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 10, Issue 2, 2024 · pp. 18–23 Read article
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A Review on Computational Intelligence and its Applications
Abstract: Abstract:Computational intelligence (CI) is a term used to describe a computer's capacity to learn a particular task via data or experimental observation. Although it is frequently used as a synonym for soft computing, computational intelligence is still not well defined. Soft computing, commonly referred to as computational intelligence, is a type of computing that is based on how people learn. Computers become more intelligent when they learn from procedures based …
Published in International Journal of Computer Aided Manufacturing · Vol. 8, Issue 1, 2022 · pp. 27–30 Read article
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Short-Term Load Forecasting By Using ANN
Abstract: Load forecasting play an impartment role in power system planning, operation and control. Short-term load forecasting is an important basis of the secure and economic operation in power systems. Accurate load forecasting is helpful to improve the security and economic effect of power systems and can reduce the cost of generation. Therefore, finding an appropriate load forecasting method to improve accuracy of forecasting has important application value. This paper explains …
Published in International Journal of Microelectronics and Digital integrated circuits · Vol. 1, Issue 1, 2015 · pp. 31–36 Read article
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CNN-Based Traffic Sign Recognition with Voice Alerts
Abstract: Traffic sign recognition is a crucial component of intelligent transportation systems, significantly enhancing road safety by aiding both autonomous and human drivers. This paper presents a convolutional neural network-based traffic sign recognition system integrated with voice alerts. Our approach utilizes a deep learning model trained on a comprehensive dataset of traffic signs to accurately identify various road signs in realtime. Upon recognition, the system issues corresponding voice alerts to inform …
Published in International Journal of Embedded Systems and Emerging Technologies · Vol. 10, Issue 2, 2024 · pp. 1–9 Read article
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AI-Based Noise Reduction Techniques for Digital and Analog Communication Systems
Abstract: Noise is one of the major challenges in modern communication systems because it significantly affects signal quality, transmission efficiency, and overall system reliability. Both digital and analog communication systems are influenced by different types of noise such as thermal noise, impulse noise, quantization noise, atmospheric noise, and channel interference. Conventional noise reduction techniques including filtering, adaptive equalization, and statistical signal processing have been extensively used to improve communication performance. However, …
Published in International Journal of Broadband Cellular Communication · Vol. 11, Issue 1, 2026 · pp. 40–48 Read article
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Satellite Image Classification using Deep Learning
Abstract: Satellite imagery is essential for lots of applications along with catastrophe response, regulation enforcement, and monitoring an environment. These packages require the guide identity of objects and facilities in imagery. Because the geographical areas to be protected are large and the analysts needed to do the searches are scarce, automation is required. Yet traditional item detection and category algorithms are too inaccurate and unreliable to clear up the problem. Deep …
Published in International Journal of Software Computing and Testing · Vol. 8, Issue 1, 2022 · pp. 29–36 Read article
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Evaluation of Machine Learning and Deep Learning Models for Classification Tasks on Crime Data: A Comparative Study
Abstract: The classification of crime data has grown more and more important for public safety and law enforcement due to the increasing demand for precise and effective predictive models. To classify crime data, this study assesses and contrasts the effectiveness of several machine learning (ML) and deep learning (DL) models, such as decision trees, support vector machines, neural networks, and convolutional neural networks. The study evaluates the models' accuracy, efficiency, and …
Published in International Journal of Digital Communication and Analog Signals · Vol. 11, Issue 1, 2025 · pp. 1–15 Read article
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Detection of Leaf Diseases Using Image Processing Techniques
Abstract: Agricultural field plays a vital role in the earning to feed ever growing population. The productivity of the crop depends on pest attack, disease, weather, etc. The disease in plants will affect the production of crops. Detecting diseases in plants through visualization is a traditional method which is not relevant. We are focusing on reliable, fast and accurate detection method that can be done using image processing techniques to monitor …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 5, Issue 2, 2019 · pp. 14–19 Read article
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Object Detection using Machine Learning: A Survey
Abstract: ABSTRACTComputer vision techniques have become increasingly popular in the modern era. One of the applications of computer vision is in the detection of object in an image or video. In object detection a target object is detected, localized and labelled based on the type of object. Real time detection of objects in harsh environments, accuracy and speed are some of the major challenges in computer vision technology. A variety of …
Published in International Journal of Embedded Systems and Emerging Technologies · Vol. 5, Issue 2, 2019 · pp. 7–11 Read article
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Forecasting Trends in the Stock Market by Using Deep Learning Approaches
Abstract: Accurately predicting stock market trends remains a significant challenge in the field of finance. Deep learning, a subset of machine learning, has emerged as a powerful tool for capturing complex relationships within data. What sets deep learning apart is its ability to handle vast amounts of data, recognize intricate features, and adapt to evolving patterns, all while uncovering hidden insights. At its core, deep learning uses artificial neural networks that …
Published in International Journal of Software Computing and Testing · Vol. 10, Issue 2, 2024 · pp. 22–30 Read article
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Maculopathy Detection and Recommendation using Machine Learning
Abstract: Maculopathies, a diverse group of eye diseases affecting the central portion of the retina, the macula, are a leading cause of vision loss globally. Early detection and accurate diagnosis are critical for timely intervention and preventing irreversible vision loss. However, traditional methods such as manually detecting the disease are time-consuming, tiring, and often lack sensitivity and specificity, hindering optimal patient care. Here we delve into the applications of machine learning …
Published in International Journal of Embedded Systems and Emerging Technologies · Vol. 10, Issue 1, 2024 · pp. 10–16 Read article
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Application of Artificial Neuron Network in roughness prediction: A case study in turning of stainless steel
Abstract: - Artificial neural networks offer a practical and efficient way to choose the best machining parameters for the turning process in order to reduce surface roughness, the resulting cutting forces, and maximize tool life. Surface roughness is a significant aspect in the evaluation of cutting performance and plays a significant role in the manufacturing process. The goal of this project is to create a model based on an Artificial Neural …
Published in International Journal of Computer Aided Manufacturing · Vol. 8, Issue 1, 2022 · pp. 21–26 Read article
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Using LapSRN (Image Resolution Deep Learning Model) with Transfer Learning
Abstract: In order to create high-resolution photographs, Super Resolution (SR) tries to transform low-resolution photos. SR methods can be categorized into two categories: Single Image Super Resolution (SISR) and Video Super Resolution (VSR). SISR initially needs to upscale low-resolution photos to high-definition images. VSR, which stands for "image super resolution," is used to transform low-quality videos into ones with higher resolution. Deep learning techniques use Convolutional Neural Networks (CNN), a special …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 8, Issue 1, 2022 · pp. 18–24 Read article