Brain tumor detection from MRI images plays a critical role in early diagnosis and effective treatment planning. Deep learning methods have shown promising results in medical image analysis, including brain tumor detection. In this paper, we propose a deep learning pipeline for accurate brain tumor detection in MRI images using the VGG-16 model. The proposed pipeline consists of multiple stages, starting with preprocessing to enhance image quality and reduce noise. The preprocessed images are then fed into the VGG-16 model, which has been pretrained on a large dataset of natural images, and fine-tuned on a specialized dataset of brain MRI images. The model leverages its deep architecture to automatically learn intricate features representative of tumor regions. To evaluate the effectiveness of the pipeline, extensive experiments were conducted on a diverse dataset of brain MRI scans. The results demonstrate that our approach achieved an impressive accuracy of 99.99

In response to the critical healthcare challenge posed by pneumonia, especially among vulnerable populations, we have developed a user-friendly Android-based application aimed at early pneumonia detection. Pneumonia, caused by various pathogens, disproportionately affects infants, the elderly, and individuals with weakened immune systems. Our innovative application serves as a valuable tool for both physicians and patients, facilitating the early assessment of pneumonia based on specific signs and symptoms. Leveraging inputs such as fever, cough, fatigue, chest pain, and difficulty breathing, our system generates a pneumonia severity level, categorized as high, medium, or low. This assessment is derived from a set of thirty-two rules, expressed as IF-THEN statements, meticulously crafted in alignment with medical expertise. Our application, featuring five multiple-choice questions, delivers actionable results, contributing to the early detection and management of pneumonia.

Handwritten digit recognition is used in computer vision and pattern recognition. This research categorizes MNIST digits using multiple neural network architectures. A typical machine learning benchmark is the MNIST dataset, which contains 28 × 28 grayscale photos of the numbers 0–9. Our major objective is to develop and test digit recognition models. Five network models, including an FCN and an ANN, were built, deployed, and assessed in this research. CNN Sequence with Dense Layers Sequential CNN with Convolution and MaxPooling, Flatten, and Dense Layers are alternative methods. These models extract characteristics from pictures using fully connected, convolutional, and pooling layers. We discuss loading, normalizing, and rearranging the dataset. The layers, activation functions, and output requirements of each model are specified in its design. Compilation involves selecting loss functions and optimizers. The validation set evaluates model performance, whereas the training set fine-tunes them. The results suggest that our models recognize digits. Other models have accuracy ranges from 93.70

The paper suggests a deep learning method using convolutional neural networks (CNNs) for real-time driver drowsiness detection. A large number of accidents worldwide are caused by drowsiness, which presents a serious risk to road safety. With approximately 20

A brain tumor is a dangerous condition that can be challenging to reliably identify using conventional techniques, such as by looking at MRI scans. To solve this problem, our convolutional neural network (CNN) and transfer learning models were developed to distinguish between the three types of brain cancers that are most frequently found: gliomas, meningiomas, and pituitary tumors. The 7023 MRI scans of the human brain that make up our dataset were separated into four groups based on their tumor status: pituitary, glioma, meningioma, and no tumor. We used an ensemble method to combine pre-trained models and achieved exceptional accuracy in identifying the presence of a tumor, a combination of CNN and VGG16 with an accuracy of 0.97687 in validation data and up to 0.9801 in test data. Our findings showed how effectively and accurately our method classified brain cancers from MRI images. For a brain tumor to be successfully treated and to be life-saving, it must be identified as early as possible.

Skin cancer poses a significant threat to human health, particularly when early identification and accurate diagnosis are lacking. Early detection is pivotal for successful treatment, given the potential lethality of this disease. While skin cancer is commonly associated with sun-exposed areas, it can also manifest on regions shielded from sunlight. In 2020, there were 150,000 new melanoma cases reported worldwide, making it the 17th most prevalent form of cancer. With a rapid increase in skin cancer cases, there is an urgent need for early and precise differentiation between cancerous and non-cancerous lesions. In response to this critical demand, we propose an advanced model for region-based skin cancer lesion classification utilizing the Faster CNN architecture. This innovative model aims to revolutionize skin cancer diagnosis, reducing reliance on dermatologists’ experience and conventional diagnostic tools. Our study presents an efficient automated system with enhanced evaluation and accuracy metrics, surpassing both prior research and expert dermatologists, achieving an impressive 92

Biofloc technology has gained prominence in aquaculture for its sustainable approach to waste decomposition and nutrient-rich microbial floc production. To ensure optimal conditions within Biofloc systems and maximize productivity, this study introduces a novel smart Biofloc monitoring system integrated with the Internet of Things (IoT). Our research centers on developing and implementing an IoT-based Biofloc monitoring system capable of real-time data collection and analysis. Specialized sensors continuously measure and track key environmental parameters, including water temperature, pH levels, and ammonia concentration. These data are efficiently transmitted through an IoT network and made accessible via the ThingSpeak server and dedicated Android applications. The presented Biofloc monitoring system provides aquaculturists with a comprehensive tool for maintaining ideal conditions, thereby improving yield and sustainability. With instant access to crucial environmental data, this IoT-enabled system empowers users to make informed decisions, take prompt corrective actions, and promote efficient Biofloc management practices.

Stroke is a disease that affects the arteries leading to and within the brain. Detecting stroke early and conveniently is much more difficult as there is no portable system to detect it. Most of the time the expensive diagnosis method of stroke is out of reach for low- and middle-income countries like ours. Hence, there is a significant necessity for an effective and labor-saving self-diagnosis platform. For the last few years, machine learning and deep learning are used to study medical-related information. Lately, deep learning has very quickly become transformative for health care, offering the ability to analyze data with a speed and much precision. This study gives an automated system to detect the stroke from prepossessed data using CNN and other deep learning models. The proposed methodology is to mainly classify the stroke person’s face from the normal or expressions face. For classification, we passed prepossessed stroke images for training, fed them into various deep architecture, and finally based on the classified expression, we classified normal and stroke patient. The experimental result shows that CNN classification model achieves accuracy 97.145

This paper introduces a groundbreaking approach to driver drowsiness detection using the AdaBoost algorithm, aiming to significantly improve road safety. Leveraging facial motion analysis, decision-level fusion, and feature scale consideration, our system achieves impressive accuracy rates of 94.86

Alzheimer’s disease is a significant public health concern, and early detection is crucial for effective intervention. In this paper, we explore the application of ensemble learning approaches to classify Alzheimer’s disease in brain imaging data (MRI images). We employed several pre-trained deep learning models, including VGG-19, ResNet-152, EfficientNetB1, and EfficientNetB2, to extract valuable features from the imaging data. These models were individually trained for ten epochs, resulting in impressive training and validation accuracies. Specifically, VGG-19 achieved 99.22 and 93.88

This paper introduces an innovative IoT-based solar irrigation system designed to revolutionize agriculture. It enhances productivity and crop quality through intelligent auto-irrigation mechanisms, soil moisture sensors, and an Arduino Mega controller, all connected via a global mobile communication network. Practical implementation successfully met its goals. Solar energy powers the system, improving energy efficiency and cost-effectiveness. IoT technology enables real-time monitoring and control, reducing water wastage and enhancing water management. Soil moisture sensors provide accurate data for tailored irrigation. Automation reduces manual labor, freeing time for other tasks. Evaluation reveals significant improvements in crop yield, quality, and water consumption compared to traditional methods. This demonstrates IoT’s potential for sustainable agriculture in resource-constrained regions, offering valuable insights for diverse settings, fostering a more efficient and sustainable agriculture sector.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects millions of individuals worldwide, causing severe cognitive decline and memory impairment. The early and accurate diagnosis of AD is crucial for effective intervention and disease management. In recent years, deep learning techniques have shown promising results in medical image analysis, including AD diagnosis from neuroimaging data. However, the lack of interpretability in deep learning models hinders their adoption in clinical settings, where explainability is essential for gaining trust and acceptance from healthcare professionals. In this study, we propose an explainable AI (XAI)-based approach for the diagnosis of Alzheimer’s disease, leveraging the power of deep transfer learning and ensemble modeling. The proposed framework aims to enhance the interpretability of deep learning models by incorporating XAI techniques, allowing clinicians to understand the decision-making process and providing valuable insights into disease diagnosis. By leveraging popular pre-trained convolutional neural networks (CNNs) such as VGG16, VGG19, DenseNet169, and DenseNet201, we conducted extensive experiments to evaluate their individual performances on a comprehensive dataset. The proposed ensembles, Ensemble-1 (VGG16 and VGG19) and Ensemble-2 (DenseNet169 and DenseNet201), demonstrated superior accuracy, precision, recall, and F1 scores compared to individual models, reaching up to 95%. In order to enhance interpretability and transparency in Alzheimer’s diagnosis, we introduced a novel model achieving an impressive accuracy of 96%. This model incorporates explainable AI techniques, including saliency maps and grad-CAM (gradient-weighted class activation mapping). The integration of these techniques not only contributes to the model’s exceptional accuracy but also provides clinicians and researchers with visual insights into the neural regions influencing the diagnosis. Our findings showcase the potential of combining deep transfer learning with explainable AI in the realm of Alzheimer’s disease diagnosis, paving the way for more interpretable and clinically relevant AI models in healthcare.

Autism, a complex neurological disorder, presents lifelong challenges that often emerge during early childhood, typically between the ages of 12 to 18 months. Accurate assessment of autism is essential, but conventional diagnostic approaches are hindered by inherent issues of vagueness, imprecision, randomness, ignorance, and incompleteness. In this context, our primary goal is to develop a fuzzy rule-based expert system to enhance the diagnosis of various autism types, including Attention-Deficit/Hyperactivity Disorder (ADHD), Autism Spectrum Disorder (ASD), Down Syndrome, Cerebral Palsy, and Intellectual Disability. Utilizing MATLAB software, we have integrated a wide array of indicators and symptoms drawn from inputs provided by parents of autistic children and domain experts. This expert system streamlines the diagnostic process for healthcare professionals, leveraging fuzzy rule-based expertise to uncover significant signs and symptoms associated with autism. Importantly, our approach effectively accommodates uncertainties that often challenge conventional diagnostic methods.

Retinal disorders pose a significant threat to human vision, with delayed or untreated cases leading to irreversible vision loss, including total blindness. Among these disorders, retinopathy of prematurity (ROP) stands out as a leading cause of blindness in preterm newborns due to abnormal retinal blood vessel development. While efforts have been made to diagnose retinal disorders early, most existing approaches have fallen short, particularly in addressing ROP. This study centers on the crucial task of retinal blood vessel segmentation in ROP fundus images, coupled with precise measurements of vessel length, breadth, and tortuosity. These metrics serve as valuable indicators for early ROP diagnosis and risk assessment, aiding ophthalmologists in preventing infant blindness. Our approach begins by enhancing ROP image quality, addressing issues of poor contrast, noise, and uneven lighting. We then employ morphological operations, lookup tables, and geodesic distance transformation techniques to delineate vessel characteristics accurately. Experimental results demonstrate the superiority of our method in both blood vessel segmentation and morphological measurements compared to existing techniques. This research contributes a robust and effective tool for ROP diagnosis, offering promise in mitigating the threat of infant blindness and advancing the field of retinal image analysis.

The human skeletal framework relies heavily on bones, and one such crucial component is the “Humerus.” Positioned in the upper arm, extending from the shoulder to the elbow junction, the Humerus provides essential structural support for muscles and facilitates upper-body movement, particularly in the arms and hands. Consequently, Humerus fractures significantly impact daily life, causing disruptions and limitations. This paper presents a thorough exploration of an Explainable AI-based Humerus Fracture Detection and Classification system, employing various deep learning models. Leveraging a dataset of 1266 X-ray images, encompassing fractured and non-fractured humerus bones from the publicly available “MURA” dataset, our research evaluates the effectiveness of Convolutional Neural Networks (CNN), VGG16, VGG19, DenseNet121, and DenseNet169 in detecting fractures. After 30 epochs of training, we assessed their performance using critical metrics: accuracy, precision, recall, and F1 score. Notably, DenseNet121 and DenseNet169 exhibited superior accuracy, precision, and recall, laying a robust foundation for automated humerus fracture diagnosis. We also introduced two ensemble models, "Ensemble-1 (VGG16 and VGG19)" and "Ensemble-2 (DenseNet121 and DenseNet169)," which delivered substantial improvements in accuracy, precision, recall, and F1 score, showcasing the potential of ensemble techniques in clinical settings. Furthermore, we enhanced model interpretability and transparency by incorporating Saliency Maps and GRAD-CAM (Gradient-weighted Class Activation Mapping) for Explainable AI (XAI). This visualization allowed us to identify regions of interest in X-ray images contributing to the model’s predictions, providing valuable insights for medical practitioners.

The classification and identification of arrhythmias using ECG signals hold substantial practical importance in the early prevention and detection of cardiac/cardiovascular disorders. Traditional ECG interpretation, relying on human clinical judgment, is susceptible to errors due to fatigue. Our method harnesses the power of two-dimensional convolutional neural networks (2D-CNNs) and transfer learning techniques, such as ResNet50, VGG16, and VGG19, to analyze both 2D image representations and one-dimensional heartbeat signal data. Initially, we train CNN and transfer learning models on 1D heartbeat signals and employ ensemble techniques to combine their predictions. Additionally, we construct models using transfer learning and CNN for analyzing 2D heartbeat images. By utilizing ensemble methods to consolidate the predictions of these models, we achieve a commendable accuracy of 0.94 for ECG signal classification and 0.93 for ECG image data. Our results underscore the efficiency and improved accuracy of our proposed approach in classifying ECG data, demonstrating its potential for early cardiovascular disease detection. This research showcases the significance of leveraging 2D-CNNs, transfer learning, and ensemble techniques in advancing cardiac disorder diagnosis based on ECG information.

Plant diseases pose a significant threat to global food security by limiting access to safe and abundant food sources while impacting agricultural productivity and food safety. To address this challenge, innovative disease detection techniques are crucial. This paper presents a novel approach to tomato leaf disease detection grounded in deep transfer learning. We employ convolutional neural networks (CNNs) to recognize and categorize diseases, leveraging pre-trained models such as EfficientNetB3, Xception, and MobileNetV2. Our experimental results highlight the efficacy of these models, with the CNN achieving an accuracy of 0.92, MobileNetV2 at 0.93, Xception at 0.94 and EfficientNetB3 at 0.993. By combining these machine learning techniques with image processing methods, we create a system capable of diagnosing tomato leaf diseases promptly. The system employs image processing techniques to facilitate disease recognition, enabling automated disease identification as soon as symptoms manifest on leaves or plants. This empowers farmers with timely information and appropriate treatment options, ultimately leading to precise disease management. Furthermore, our research delves into model interpretability by employing Saliency maps and Grad-CAM techniques, providing valuable insights into the decision-making process of our deep learning models. This transparency enhances the trustworthiness of the AI-driven disease detection system, fostering its practical application in agriculture.

This study focuses on the development of an obstacle avoidance robot controlled by an ultrasonic sensor. The robot utilizes robotics and artificial intelligence techniques to improve obstacle avoidance and enhance safety in various scenarios, particularly in the context of road accidents. The system incorporates an Arduino microcontroller and ultrasonic sensors that detect obstacles within the robot's path. By processing the sensor data, the microcontroller employs artificial intelligence algorithms to predict the presence of obstacles in real-time. The ultrasonic sensor is strategically positioned at the front of the robotic vehicle, continuously gathering data from the surrounding area. When an obstacle is detected, the robot autonomously adjusts its direction to avoid collision. The sensor relays the collected data to the microcontroller, which then determines the appropriate movement and direction for the robot's wheels. Consequently, the robot built with our approach is satisfactorily able to detect obstacles and avoid them, with a remarkable accuracy rate of 84%. This proactive approach to obstacle detection and avoidance aims to prevent potential accidents and promote safer navigation. The obstacle avoidance robot serves as a practical solution for detecting and avoiding obstacles, with potential applications in various domains. By combining robotics, artificial intelligence, and sensor technologies, this study contributes to the development of intelligent systems capable of autonomously navigating their environment while ensuring safety.

Alzheimer’s disease (AD) is a progressive and irreversible neurological disorder that affects millions of people worldwide. Early detection and accurate diagnosis of AD are crucial for effective treatment and management of the disease. In this paper, we propose a transfer learning-based approach for the diagnosis of AD using magnetic resonance imaging (MRI) data. Our approach involves extracting relevant features from the MRI data using transfer learning by alter the weights and then using these features to train pre-trained models and combined ensemble classifier. We evaluated our approach on a dataset of MRI scans from patients with AD and healthy controls, achieving an accuracy of 95% for combined ensemble models. Our results demonstrate the potential of transfer learning-based approaches for the early and accurate diagnosis of AD, which could lead to improved patient outcomes and more effective management of the disease.

House hunting, or the act of seeking for a place to live, is one of the most significant responsibilities for many families around the world. There are numerous criteria/factors that must be evaluated and investigated. These traits can be both statistically and qualitatively quantified and expressed. There is also a hierarchical link between the elements. Furthermore, objectively/quantitatively assessing qualitative characteristics is difficult, resulting in data inconsistency and, as a result, uncertainty. As a result, ambiguity must be dealt with using the necessary processes; otherwise, the decision to live in a particular property would be incorrect. To compare criteria, the Analytic Hierarchy Process (AHP) is employed, evidential reasoning is used to evaluate houses based on each criterion, and TOPSIS is used to rank house sites for selection. It was necessary to analyze qualitative and quantitative elements, as well as economic and social features of these residences, in order to arrive at the final order of houses, which was not an easy process. As a result, the authors developed a decision support model to aid decision makers in the management of activities related to finding a suitable dwelling. This study describes the development of a decision support system (DSS) capable of providing an overall judgment on the location of a house to live in while taking into account both qualitative and quantitative factors.