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20 articles for “fusion classifier”
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Performance Analysis of Fusion Classifier of Face Recognition System based on ANN and GA
Abstract: Though a lot of significant work has been done for recognizing and improving the performance in facial pattern, most techniques have been developed based on a single classifier. But most often, the performance of the classifier is not satisfactory in some case. It has been proposed that the performance of face recognition will beimproved by fusing two or more classifiers. We can combine the information/data in different level i.e., feature …
Published in Current Trends in Information Technology · Vol. 4, Issue 1, 2014 · pp. 4–13 Read article
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Improved Melanoma Recognition using Score Fusion Framework based on Deep Classifiers
Abstract: Melanoma is a fast-growing and malignant cancer that affects neural crest-derived cells of the skin. Early detection is the key to survival and rapid recovery; however, typical diagnosis is based on visual inspection by expert dermatologists. Many melanoma recognition methods have been proposed in the literature that can classify lesions, based on hand-crafted as well as deep learning-based features. However, an accurate, automated method for melanoma is still required. This …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 8, Issue 2, 2021 · pp. 25–33 Read article
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Cascade CNN Framework for Low Resolution Image Classification
Abstract: Abstract: Low resolution images contain less visual information, so classifications of these images are difficult. For overcoming this problem, cascade CNN framework for low resolution image classification is proposed. In this framework, super resolution CNN (SRCNN) enlarges low resolution image into super resolution image. The convolutional features of these super resolution images are fused with low resolution convolutional features which are extracted by low resolution CNN (LRCNN) feature extractor. A …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 6, Issue 1, 2019 · pp. 39–43 Read article
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Prediction of Likelihood of Contraceptive Method among Women using Machine Learning Techniques
Abstract: This paper presents the various machine learning techniques and early fusion approach for analyzing usage of contraceptive methods among women. The analysis has been done using various nine features that the usage of contraceptive among women is whether low, high or not at all. This paper also evaluates the effect of contraceptive prediction is affected by men, age, and religion. Finally, comparison of classification accuracy has been employed between 22 …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 6, Issue 1, 2019 · pp. 1–6 Read article
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A Study on Phased Array Technology for Polyethylene Pipe Butt Fusion Joint to Apply in Nuclear Power Plants
Abstract: Butt fusion welding has been widely used for its ease to fuse and suitability for high-quality High Density Polyethylene (HDPE) pipes. This paper studies background and advantages of ultrasonic phased array technology and positive attributes of high density polyethylene pipes to apply in nuclear power plants. We classified two types of flaws: cold-fusion and inclusion. Specimens have been designed and fabricated using butt fusion welding method, and potential flaws such …
Published in Journal of Nuclear Engineering & Technology · Vol. 2, Issue 1, 2012 · pp. 1–9 Read article
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A Novel Feature Level Fusion Method for Classification of Remote Sensing Images
Abstract: Feature level fusion approach is utilized in this paper to classify remote sensing images. Texture features are extracted from panchromatic images using mixed Gabor filter (GB), fast gray level co-occurrence matrix (GLCM) and linear binary pattern (LBP). The resultant texture features are classified using nearest neighbor (k-NN) classification method. Spectral features are extracted from the MS image and segmented using over segmented k-means algorithm with novel initialization (OSKNI). Finally the …
Published in Journal of Remote Sensing & GIS · Vol. 10, Issue 1, 2019 · pp. 58–65 Read article
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EMOTION RECOGNITION FROM ELECTROENCEPHALOGRAM SIGNAL AND EYE MOVEMENT BASED ON DEEP LEARNING
Abstract: Emotion recognition from electroencephalogram (EEG) signals has gained significant attention due to its potential in human- computer interaction (HCI), mental health monitoring, and personalized content delivery. This paper presents the use of Convolutional neural networks (CNNs) to classify emotions such as happiness, sadness, fear, neutral, and disgust by leveraging a fusion of EEG signals and eye movements data. Compared to conventional methods of emotion detection, such as those that rely …
Published in International Journal of Optical Innovations & Research · Vol. 3, Issue 2, 2025 · pp. 8–15 Read article
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A Review: Fusion of Face and Iris by using Support Vector Machine
Abstract: AbstractA new approach for personal identification based on the fusion of iris & face recognition is presented in this paper. Biometric innovations are the establishment of the personal identification system. A biometric system recognizes a person based on a few characteristics or forms. This is in many instances watched in the region of security, privacy, and forensics. Even for the great of unimodal biometric systems, it is frequently no longer …
Published in Journal of Electronic Design Technology · Vol. 9, Issue 3, 2018 · pp. 1–10 Read article
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Inter-patient Heartbeat Classification Using Higher Order Statistics and Symbolic Dynamics of ECG Signal
Abstract: Computer assisted heartbeat classification techniques have gained significant importance in the recent years as they aid the cardiologists in identifying abnormal heartbeats in ECG recordings. These techniques are of vital importance especially for the long term ECG recordings where it is not feasible to manually analyze the numerous heartbeats. Electrocardiogram (ECG) is an electrical representation of human heart activities and change of these activities is reflected in the morphology of …
Published in Current Trends in Signal Processing · Vol. 7, Issue 2, 2017 · pp. 37–45 Read article
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Extra Philadelphia in CML Due to Isodicentric 22: A Case Report
Abstract: Introduction: Chronic Myelogenous Leukemia is a malignancy that affects the hematopoietic stems cells (HSC) of the bone marrow, leading to the rapid and continual proliferation of granulocytes and precursor blast cells. This is caused due to a reciprocal translocation that occurs between chromosomes 9 and 22, leading to the formation of derivative 22 also called as Philadelphia (Ph) chromosome. The Ph chromosome then encodes a fusion oncoprotein that functions as …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 14, Issue 3, 2025 · pp. 18–22 Read article
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A Multimodal AI-Based System for Real-Time Harassment and Violence Detection Using Surveillance Cameras
Abstract: Public safety in urban settings, as these areas are crowded and experience increasing incidents of harassment, verbal abuse, and physical violence. Traditional CCTV surveillance systems heavily rely on human operators who have to continuously monitor the video feeds, which is inefficient and prone to human error. AI-based harassment and violence detection systems automatically monitor public environments in real-time using video and audio analysis. The system is based on a multilayer …
Published in Journal of Advancements in Robotics · Vol. 13, Issue 2, 2026 · pp. 8–21 Read article
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Multi-Modal Iris and Retina Recognition System using Discrete Hidden Markov Model based Score Fusion Technique
Abstract: The contribution of this work is to propose a model of multi-modal iris and retina recognition system where Hidden Markov Model based score fusion technique has been used for decision fusion approach. Two different uni-modal techniques such as iris recognition and retina recognition outputs are combined by using baseline reliability ratio based score fusion technique. CASIA iris dataset and DRIVE retinal dataset have been used to acquire the iris and …
Published in Current Trends in Information Technology · Vol. 5, Issue 2, 2015 · pp. 7–12 Read article
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An Analysis of Multimodal Fusion in Deepfake Detection for Video Samples
Abstract: In today’s rapidly evolving digital landscape, deepfake technology stands as both a marvel and a threat to privacy and security. Deepfakes, hyper-realistic synthetic media created using artificial intelligence (AI), can deceive and manipulate on an unprecedented scale, from political propaganda to compromising videos of public figures. This research navigates deepfake detection, focusing on two advanced methodologies: the vision transformers (ViT) image classifier and the Meso4 method. The ViT model utilizes …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 11, Issue 3, 2024 · pp. 19–27 Read article
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Fusion of deep learning autoencoders with random forest for wetland classification using Sentinel-2A data: A case study on Sirpur wetland
Abstract: Present study analyses the performance of deep leaning algorithm-autoencoder to reduce data dimension as compared to conventional models. Classification accuracies of Sirpur wetland using Sentinel 2A dataset with different inputs have also been studied. These inputs sets comprise the reconstructed data through compression of original 13 bands into 4 bands using decoder algorithm, first four Principal Components, all spectral bands, and spectral indices. Random Forest classifier (RF) is used to …
Published in Journal of Remote Sensing & GIS · Vol. 17, Issue 1, 2026 · pp. 25–35 Read article
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Multi-Parameter Biomedical Sensor-Based Mental State Classification Using EEG And Deep Learning Techniques
Abstract: With mental health concerns becoming increasingly widespread, there is a strong need for systems that can monitor conditions like stress, anxiety, and fatigue in a continuous and non- invasive manner. This research proposes a novel multi-parameter biomedical sensing framework for mental state classification by integrating electroencephalography (EEG) signals with physiological parameters, including body temperature acquired using LM35 sensors, heart rate from pulse sensors, and blood oxygen saturation (SpO₂) measurements. The …
Published in Current Trends in Signal Processing · Vol. 16, Issue 2, 2026 · pp. 1–8 Read article
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Dual-Stream Deep Learning Framework for Brain CT Image Classification and Implications for Polymer Composite Neuro Implant Evaluation
Abstract: Early and accurate classification of brain CT images is critical for diagnosing conditions such as aneurysms, tumors, and related lesions. We present a dual-stream image-classification framework that fuses convolutional neural network (CNN) features with handcrafted Histogram of Oriented Gradients (HOG) descriptors to jointly capture global semantics and local textural cues. The pipeline begins with modality unification via pixel-wise averaging to form a fused input, which is then processed in parallel …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 172–179 Read article
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Early Alzheimer's Disease Detection Using Deep Ensemble Learning and MRI Image Analysis
Abstract: Early detection of Alzheimer's disease (AD) is crucial to slowing cognitive decline and enabling timely clinical interventions. Traditional diagnostic methods, including cognitive tests and single-model classifiers, have limited sensitivity during early stages of the disease. This paper presents a deep ensemble learning approach that integrates multiple convolutional neural networks (CNNs) for accurate Alzheimer's disease detection using structural Magnetic Resonance Imaging (MRI) data. The proposed framework utilizes ResNet50, VGG16, and DenseNet121 …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 1, 2026 · pp. 1–9 Read article
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Stacked Generalization-Based Deep Learning Approach for Pneumonia Detection
Abstract: The proposed work focuses on a stacked generalization-based approach for diagnosing pneumonia from chest X-ray images. It utilizes regularization, early stopping, and data augmentation to deal with overfitting. It uses safe level SMOTE to deal with class imbalance and attention-based feature fusion to adaptively weigh features based on their importance. It uses two publicly available datasets (RSNA and Kermany) with ground truth provided by expert radiologists. The proposed work used …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 3, 2025 · pp. 20–31 Read article
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A Systematic Study of AI-Powered Robotics for Ocean Cleanup of Plastics
Abstract: The escalating crisis of plastic pollution in marine ecosystems demands innovative solutions beyond conventional cleanup methods. This paper presents a systematic study of artificial intelligence (AI)-powered robotics for ocean plastic cleanup, evaluating their efficiency, technological advancements, and challenges. Autonomous systems, such as AI-driven surface drones (ASVs), underwater robots (autonomous underwater vehicles/remotely operated vehicles [AUVs/ROVs]), and swarm robotics, leverage machine learning (ML) and computer vision to detect, classify, and collect plastic …
Published in Journal of Advancements in Robotics · Vol. 12, Issue 2, 2025 · pp. 1–10 Read article
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A Profound Analysis on Complex Multiclass BCI Signals Classification by an EEG Device
Abstract: With a major shift in the technology paradigm today in the area of deep learning and artificial intelligence, one of its important applications today is found in brain-computer interface (BCI). Brain computer-interface, also known as neural control interface or mind-machine interface is a device that helps a user to interact with a computer using only his brain-activity, which is measured usually by Electroencephalography (EEG). The usage of such mechanism has …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 8, Issue 1, 2021 · pp. 22–44 Read article