Search
28 articles for “Brain training”
-
The Cognitive Renaissance: Transforming Your Mind with Innovative Techniques
Abstract: Cognitive abilities play a crucial role in our daily lives, as they impact our capacity to concentrate and stay focused on tasks. When cognitive impairments occur, domains such as working memory, executive function, attentiveness, and knowledge acquisition can be negatively affected. Fortunately, intervention programs have been shown to enhance cognitive abilities and improve daily functioning. Regardless of age, various intervention programs can improve multiple aspects of cognitive functioning. In this …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 13, Issue 1, 2023 · pp. 47–56 Read article
-
Exploring Practical Applications of Artificial Neural Networks: A Review
Abstract: Computational models called artificial neural networks (ANNs) are modeled after the structure of the human brain. These models are designed to process information and learn from data. Artificial neural networks, or ANNs, are composed of interconnected artificial neurons layered to resemble the brain's neural network.. Through training, ANNs adjust the connections between neurons based on labeled data, enabling them to recognize patterns and perform specific tasks. Despite their efficacy in …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 11, Issue 2, 2024 · pp. 1–11 Read article
-
Pyogenic-Cerebral (Brain) Abscess
Abstract: Abscesses in the brain have been one of the most challenging wounds, both for surgeons and trainees. It is a packet full of pus of infected material in the part of the brain. It is an important neurological disease and can produce deadly diseases. Since the beginning of the era of computed tomography (CT), the diagnosis and treatment of these organizations has become simpler and less invasive. The results have …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 12, Issue 1, 2022 · pp. 33–46 Read article
-
Risk After Pediatric MRI Scanning: A Nation-Wide, Population Based Case-Control Study
Abstract: This paper investigates the potential association between pediatric MRI (Magnetic resonance imaging) exposure and the risk of developing childhood brain tumors, using action-wide, population-based and case-control methodology. The increasing use of MRI in pediatric healthcare has raised concerns about potential long-term health risks, including the risk of developing brain tumors. Detecting the presence or absence of brain tumor through traditional methods might require a lot of time as well as …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 3, Issue 2, 2025 Read article
-
Advancing Brain Tumor MRI Segmentation
Abstract: Segmentation of brain tumors in MRI scans is an integral part of neuroimaging carried out for diagnostic and therapeutic interventions. Given that manual segmentation is cumbersome and highly variable, there arises a need for automated, more precise segmentation solutions. This project, ‘Machine Learning and Deep Neural Networks to Advance Brain Tumor MRI Segmentation’ will develop a better, efficient, and accurate segmentation model to help clinicians identify brain tumors with greater …
Published in Recent Trends in Electronics Communication Systems · Vol. 12, Issue 2, 2025 · pp. 28–33 Read article
-
Brain Tumor Detection Through CNN: Techniques, Dataset Insights, and Methodology
Abstract: Computer technologies are playing huge roles in some areas of the medical domain like surgery and therapy of different diseases. Researchers are doing studies and trying to experiment to detect different diseases like cancer, virus infections, and leprosy. There are many different medical imaging datasets that are publicly available for medical research purposes of diseases like cancer, virus infections, and leprosy, etc. where we can be able to access large …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 12, Issue 1, 2025 · pp. 30–40 Read article
-
BRAIN TUMOR DETECTION USING MACHINE LEARNING AND GAUSSIAN MIXTURE MODEL
Abstract: This paper provides an machine learning approach for the detection of brain tumors. The modified GMM approach is used for the detection of tumors. The ARFF dataset of tumor image is created. The training dataset is created and the efficiency of the approach is tested against the test dataset. The detection rate is 98.5.Keywords: GMM, Machine learning, technology, brain tumor, ARFF datasetCite this Article: Parimala Geetha K, C. Anna Palagan, …
Published in Journal of Computer Technology & Applications · Vol. 11, Issue 2, 2020 · pp. 1–4 Read article
-
The Role of Artificial Intelligence in Mental Health: Applications in Neurodegenerative Disorders
Abstract: Artificial intelligence (AI) has significantly changed many aspects of medical care, particularly the early evaluation, therapy, and management of neurodegenerative illnesses like Alzheimer's, disease, Parkinson's diseases, and Huntington's diseases. The current research explores the application of AI in mental health with respect to neurological disorders, especially advancements in cognitive examination, neuroimaging analysis, predictive modeling, and customized therapy modalities. Artificial intelligence (AI) systems have shown enormous potential in detecting minute biomarkers …
Published in Research and Reviews : A Journal of Biotechnology · Vol. 15, Issue 3, 2025 · pp. 34–40 Read article
-
Fuzzy C-Means Clustering for Effective Segmentation and Classification of Brain Tumors in MRI Scans
Abstract: The paper discusses the importance of detecting and classifying brain tumors via MRI for effective treatment. It proposes a framework utilizing the Fuzzy C-means clustering algorithm for segmentation, demonstrating improved performance through real dataset validation. The model is trained on a large, annotated MRI dataset to identify and classify different tumor types, enabling machine learning-based classification into benign and malignant tumors. The MATLAB-based solution automates brain tumor feature extraction, aiding …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 2, 2024 · pp. 23–28 Read article
-
Brain Tumor Detection Using RestNet50 Architecture
Abstract: This paper presents a novel deep learning model for brain tumor diagnosis from MRI scans on the basis of ResNet50 with some modifications. Optimizing the modified layers and pre-trained ResNet50 for improved diagnostic accuracy and reliability in real-world clinical settings is one of the key contributions of this paper. The model was trained on an extremely well-balanced data of 2,577 MRI scans, which were split equally among the tumor and …
Published in Current Trends in Signal Processing · Vol. 15, Issue 2, 2025 · pp. 1–13 Read article
-
Detection of Driver Emotion Using Deep Learning
Abstract: High level Driver-Help Frameworks (ADASs) are utilized for expanding security in the auto space, yet momentum ADASs quite work without considering drivers' states, e.g., whether she/he is genuinely able to drive. Feelings are a significant way of behaving of people and may emerge in driving circumstances. Uncontrolled feelings can prompt unsafe impacts. To control and decrease the adverse consequence of conduct. In this paper we will distinguish the driver’s conduct. …
Published in International Journal of Electronics Automation · Vol. 1, Issue 1, 2023 · pp. 01–06 Read article
-
Deep Learning Based Detection and Classification of Brain Tumors Using MRI Images
Abstract: Brain tumor detection using magnetic resonance imaging (MRI) is a critical task in the early detection and treatment of brain tumors. Manual analysis of brain tumor detection using MRI is a tedious task that requires expertise in the field. Therefore, this study proposes a deep learning-based approach for brain tumor detection and classification using Convolutional Neural Networks (CNN). The proposed approach preprocesses the MRI image using normalization, resizing, and noise …
Published in International Journal of Brain Sciences · Vol. 3, Issue 2, 2026 Read article
-
Virtual Method to Predict Dental Disease
Abstract: The integration of technology and medicine in the healthcare domain has led to the emergence of inventive strategies to improve patient care and diagnostics. One such groundbreaking methodology is the utilization of Convolutional Neural Networks (CNNs) within the domain of deep learning, particularly for image recognition and processing tasks. In this paper, we propose a novel approach to image recognition that employs state-of-the-art deep learning algorithms to create a user-friendly …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 1, 2024 · pp. 8–15 Read article
-
Uncertainty Prediction in Brain Tumour Segmentation
Abstract: Gliomas are one of the most common brain tumour at different levels of the province, with Magnetic Resonance Imaging (MRI) used for diagnosis. In this project, It was asked to try to find uncertainty in the Brain Tumour Segmentation on MRI images using the BraTs19 Dataset and to look at how machine learning algorithms can work with these MRI images. Since these tissues are so large in shape and appearance, …
Published in Journal of Instrumentation Technology & Innovations · Vol. 13, Issue 2, 2023 · pp. 40–52 Read article
-
Brain Tumor Detection by Aggregating Deep Learning and GAN Models for Faster MRI image Synthesis
Abstract: Brain tumors comprise a global health challenge that, in order to be treated and organized, need early and accurate diagnosis. Usually conducted through medical imaging, brain tumor detection techniques have problems of accuracy, efficiency, and confidentiality. Issues of limited datasets, strict privacy laws that provide restrictions on data sharing, and the necessity for specialized expertise on medical image analysis relegates modern methodologies to vulgar charades. For patient prognosis, treatment planning, …
Published in International Journal of Brain Sciences · Vol. 2, Issue 2, 2025 · pp. 45–53 Read article
-
Unveiling Insights and Strategies for Cognitive Mental Acuity
Abstract: This comprehensive review focuses on the intricate facets of cognition, emphasizing its multifaceted nature rooted in thought, experience and sensory perception. The role of key brain regions, such as the prefrontal cortex, temporal lobes, parietal cortex, occipital cortex and cerebellum, is elucidated in shaping various mental functions. The pathophysiology of cognitive decline is examined, linking specific impairments to damage in distinct brain regions. The study delves into pharmacological and non-pharmacological …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 14, Issue 1, 2024 · pp. 1–10 Read article
-
Mind-Machine Synergy: The Evolution and Future of Brain-Computer Interfaces
Abstract: Brain-Computer Interfaces (BCIs) represent a transformative technology that enables the direct communication between the human brain and external devices, bypassing the traditional output mechanisms, such as speech or physical movement. BCIs hold the potential to revolutionize fields, such as healthcare, neuroscience, and human-computer interaction by providing new ways to restore lost functions, enhance cognitive abilities, enable seamless communication, and create novel user experiences across various platforms and environments. This article …
Published in International Journal of Brain Sciences · Vol. 2, Issue 2, 2025 · pp. 19–31 Read article
-
ENHANCING CONTROL WITH EMBEDDED SSVEP-BCI
Abstract: Brain–Computer Interface (BCI) technology establishes a direct communication link between the human brain and external devices without relying on muscular activity. Among various BCI paradigms, the Steady-State Visually Evoked Potential (SSVEP)-based approach has gained significant attention due to its high signal-to-noise ratio, minimal user training, and suitability for real-time applications. However, implementing such systems on embedded hardware presents challenges such as limited computational resources, signal noise, and latency in processing. …
Published in Recent Trends in Electronics Communication Systems · Vol. 12, Issue 3, 2025 · pp. 41–52 Read article
-
U-Net Based Approach for Automated Brain Tumor Classification
Abstract: Brain tumor detection and identification play vital roles in diagnostic procedures in the field of medicine, with the conventional analysis of MRI images requiring a lot of time and also subject to variability. The proposed study involves the use of a CNN-U-Net based approach for brain tumor detection and identification automatically. The study uses a database of 3,064 contrast-enhanced T1-weighted MRI images from 233 patients with the tumors of meningioma, …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 14, Issue 2, 2025 Read article
-
Face Recognition using LDA based Support Vector Machine
Abstract: AbstractRecognition of a person is an easy task for human brains. From different experiments, we have found that even one to three days old babies are able to distinguish between known faces. So, it is very difficult task for a computer to recognize an unknown human face although a computer has been trained with a huge set of human face. Now-a-days face recognition has received significant attention of researchers. Lots …
Published in Journal of Computer Technology & Applications · Vol. 5, Issue 2, 2014 · pp. 1–7 Read article