Convolutional Neural Network Architecture Research Articles

Building typology classification using convolutional neural networks utilizing multiple ground-level image process for city-scale rapid seismic vulnerability assessment

Several studies have focused on generating seismic vulnerability maps for earthquake-prone areas, particularly in Indonesia. Building typologies are a key factor in determining vulnerability to earthquakes. However, conducting large-scale field surveys to determine the spatial distribution of building typologies in a city is uneconomical. This paper explores the use of a convolutional neural network (CNN) to automatically detect building typologies from diverse regions in Indonesia, utilizing both conventional and automated building image acquisition processes. In this study, datasets from three distinct image acquisition methods are trained with four unique CNN architectures to identify the best-performing model to classify building typologies. The sample size effect on CNN performance is also investigated. The results showed that randomly sampled Google Street View (GSV) images are the most effective dataset for the CNN model, achieving an f1-score of 84.33%. Among the network architectures tested, MobileNet demonstrated superior performance on the majority of evaluated datasets. As the sample size increases by about 350% in the dataset, there is a positive correlation with up to 2.3% f1-score improvement. Using the best-performing CNN model, two building vulnerability models were employed to assess the spatial distribution of building damage in the urban area of Bandung, considering a hypothetical scenario of an M7 earthquake. Incorporating local construction data, one of the generated maps estimated that approximately 55% of buildings in Bandung would experience moderate to severe structural damage. This study showcases the potential of CNN models in automating regional seismic assessments and providing valuable insights for comprehensive seismic mitigation strategies.

Cloudy Weather Prediction Using CNN Models and Satellite Images

Abstract: Cloudy weather classification is a vital task in meteorology and remote sensing, facilitating various applications such as weather forecasting, climate monitoring, and environmental analysis. In this study, we explore the application of convolutional neural network (CNN) techniques for classifying cloudy weather conditions using the Cloudy Weather Dataset sourced from Kaggle. The primary CNN architectures investigated include AlexNet, LeNet, and ResNet. The dataset undergoes preprocessing steps, including resizing and normalization to floating-point representation. Additionally, for calculating cloud cover percentage, the images are processed through grayscaling followed by thresholding. The performance of each CNN model is evaluated based on metrics of accuracy, that is providing insights into their effectiveness for cloudy weather classification

A Time-Frequency Domain Mixed Attention-Based Approach for Classifying Wood-Boring Insect Feeding Vibration Signals Using a Deep Learning Model

Wood borers, such as the emerald ash borer and holcocerus insularis staudinger, pose a significant threat to forest ecosystems, causing damage to trees and impacting biodiversity. This paper proposes a neural network for detecting and classifying wood borers based on their feeding vibration signals. We utilize piezoelectric ceramic sensors to collect drilling vibration signals and introduce a novel convolutional neural network (CNN) architecture named Residual Mixed Domain Attention Module Network (RMAMNet).The RMAMNet employs both channel-domain attention and time-domain attention mechanisms to enhance the network’s capability to learn meaningful features. The proposed system outperforms established networks, such as ResNet and VGG, achieving a recognition accuracy of 95.34% and an F1 score of 0.95. Our findings demonstrate that RMAMNet significantly improves the accuracy of wood borer classification, indicating its potential for effective pest monitoring and classification tasks. This study provides a new perspective and technical support for the automatic detection, classification, and early warning of wood-boring pests in forestry.

Improvement Model for Speaker Recognition using MFCC-CNN and Online Triplet Mining

Various biometric security systems, such as face recognition, fingerprint, voice, hand geometry, and iris, have been developed. Apart from being a communication medium, the human voice is also a form of biometrics that can be used for identification. Voice has unique characteristics that can be used as a differentiator between one person and another. A sound speaker recognition system must be able to pick up the features that characterize a person's voice. This study aims to develop a human speaker recognition system using the Convolutional Neural Network (CNN) method. This research proposes improvements in the fine-tuning layer in CNN architecture to improve the Accuracy. The recognition system combines the CNN method with Mel Frequency Cepstral Coefficients (MFCC) to perform feature extraction on raw audio and K Nearest Neighbor (KNN) to classify the embedding output. In general, this system extracts voice data features using MFCC. The process is continued with feature extraction using CNN with triplet loss to obtain the 128-dimensional embedding output. The classification of the CNN embedding output uses the KNN method. This research was conducted on 50 speakers from the TIMIT dataset, which contained eight utterances for each speaker and 60 speakers from live recording using a smartphone. The accuracy of this speaker recognition system achieves high-performance accuracy. Further research can be developed by combining different biometrics objects, commonly known as multimodal, to improve recognition accuracy further.

ГЛУБОКАЯ НЕЙРОННАЯ СЕТЬ ДЛЯ ОБНАРУЖЕНИЯ ТОЧЕК НА ИЗОБРАЖЕНИЯХ С ИСПОЛЬЗОВАНИЕМ KERAS

Convolutional neural networks are a powerful tool for image processing and analysis. They allow you to automatically extract features from the input data and apply them 
 to classification, object detection and other computer vision tasks. This article presents the convolutional neural networks architecture developed using the Keras library. The architecture of a convolutional neural network, which consists of several successive layers, is analyzed. The structure of the model has been built, which will be trained using the Adam optimizer and monitor the recall and precision metrics during the training process. The results of experiments are presented, which showed that the trained model successfully detects points in images, achieving high accuracy and completeness. The proposed model can be used in various areas where the detection of facial points in photographs is required.

Enhancing energy‐efficient sleep scheduling for Narrowband Internet of Things devices in coordinated 5G networks within smart environments

SummaryThis research project takes on a crucial role in the quickly changing field of integrated 5G networks inside smart environments by concentrating on the creation of an extremely effective sleep scheduling system designed especially for Narrowband Internet of Things (NB‐IoT) devices. This work introduces a unique method for precisely controlling the nodes' sleep schedules through the use of convolutional neural network (CNN) architecture, which optimizes both energy usage and operating patterns. The principal goal still stands to guarantee the extended lifetime of operation and dependability of NB‐IoT devices in the larger framework of intelligent ecosystems driven by synchronized 5G networks. To achieve its objectives, the research explores a number of complex domains and employs cutting‐edge technologies and techniques, such as CNN‐based pattern recognition. This method's real‐time component is essential since it allows for prompt modifications to sleep schedules in order to optimize energy savings. Further boosting the devices' effectiveness and flexibility is continuous contact with a central server, which guarantees that the devices are updated with the most recent data and instructions. Essentially, the main objective of this study is to greatly increase the energy economy and operational lifetime of NB‐IoT devices, which will allow for stable and long‐lasting IoT deployments in the context of 5G networks in intelligent settings. This advancement is not only a boon for businesses and industries leveraging IoT technology but also a substantial step toward building smarter, more energy‐efficient, and resilient smart ecosystems that benefit society as a whole.

High sensitivity methods for automated rib fracture detection in pediatric radiographs

Rib fractures are highly predictive of non-accidental trauma in children under 3 years old. Rib fracture detection in pediatric radiographs is challenging because fractures can be obliquely oriented to the imaging detector, obfuscated by other structures, incomplete, and non-displaced. Prior studies have shown up to two-thirds of rib fractures may be missed during initial interpretation. In this paper, we implemented methods for improving the sensitivity (i.e. recall) performance for detecting and localizing rib fractures in pediatric chest radiographs to help augment performance of radiology interpretation. These methods adapted two convolutional neural network (CNN) architectures, RetinaNet and YOLOv5, and our previously proposed decision scheme, “avalanche decision”, that dynamically reduces the acceptance threshold for proposed regions in each image. Additionally, we present contributions of using multiple image pre-processing and model ensembling techniques. Using a custom dataset of 1109 pediatric chest radiographs manually labeled by seven pediatric radiologists, we performed 10-fold cross-validation and reported detection performance using several metrics, including F2 score which summarizes precision and recall for high-sensitivity tasks. Our best performing model used three ensembled YOLOv5 models with varied input processing and an avalanche decision scheme, achieving an F2 score of 0.725 ± 0.012. Expert inter-reader performance yielded an F2 score of 0.732. Results demonstrate that our combination of sensitivity-driving methods provides object detector performance approaching the capabilities of expert human readers, suggesting that these methods may provide a viable approach to identify all rib fractures.

A pilot study on AI-driven approaches for classification of mental health disorders

The increasing prevalence of mental disorders among youth worldwide is one of society's most pressing issues. The proposed methodology introduces an artificial intelligence-based approach for comprehending and analyzing the prevalence of neurological disorders. This work draws upon the analysis of the Cities Health Initiative dataset. It employs advanced machine learning and deep learning techniques, integrated with data science, statistics, optimization, and mathematical modeling, to correlate various lifestyle and environmental factors with the incidence of these mental disorders. In this work, a variety of machine learning and deep learning models with hyper-parameter tuning are utilized to forecast trends in the occurrence of mental disorders about lifestyle choices such as smoking and alcohol consumption, as well as environmental factors like air and noise pollution. Among these models, the convolutional neural network (CNN) architecture, termed as DNN1 in this paper, accurately predicts mental health occurrences relative to the population mean with a maximum accuracy of 99.79%. Among the machine learning models, the XGBoost technique yields an accuracy of 95.30%, with an area under the ROC curve of 0.9985, indicating robust training. The research also involves extracting feature importance scores for the XGBoost classifier, with Stroop test performance results attaining the highest importance score of 0.135. Attributes related to addiction, namely smoking and alcohol consumption, hold importance scores of 0.0273 and 0.0212, respectively. Statistical tests on the training models reveal that XGBoost performs best on the mean squared error and R-squared tests, achieving scores of 0.013356 and 0.946481, respectively. These statistical evaluations bolster the models' credibility and affirm the best-fit models' accuracy. The proposed research in the domains of mental health, addiction, and pollution stands to aid healthcare professionals in diagnosing and treating neurological disorders in both youth and adults promptly through the use of predictive models. Furthermore, it aims to provide valuable insights for policymakers in formulating new regulations on pollution and addiction.

Deep learning and radiomics-based approach to meningioma grading: exploring the potential value of peritumoral edema regions.


Radiomics and deep learning techniques have become integral in meningioma grading. The combination of these approaches holds the potential to enhance classification accuracy. Given the frequent occurrence of peritumoral edema (PTE) in meningiomas, investigating the potential value of PTE requires further research.
Objectives:
To address the challenge of meningioma grading, this study introduces a unique approach that integrates radiomics and deep learning techniques. The primary focus is on the development of a Transfer Learning-based Meningioma Feature Extraction Model (MFEM), leveraging both Vision Transformer (ViT) and Convolutional Neural Network (CNN) architectures. Furthermore, the study explores the potential significance of the peritumoral edema (PTE) region in enhancing the grading process.
Materials and Methods:
A retrospective study was conducted involving 98 meningioma patients, with 60 classified as low-grade meningiomas and 38 as high-grade meningiomas. PTE was observed in 51.02% of low-grade meningiomas patients and 89.47% of high-grade meningiomas patients. Magnetic resonance images were acquired using a GE Signa HDxt 1.5T MRI scanner, incorporating T2-weighted Fluid-Attenuated Inversion Recovery (T2 Flair) sequences. A Transfer Learning-based Meningioma Feature Extraction Model (MFEM) was constructed by combining ViT and CNN models to extract deep features from the Transformer layer, alongside radiomics features. These were then utilized as input for a machine learning classifier to accurately grade meningiomas.
Results:
The proposed method demonstrated excellent grading accuracy and robustness on the meningioma dataset, offering valuable guidance for treatment decisions. The approach achieved 92.86% accuracy, 93.44% precision, 95% sensitivity, and 89.47% specificity.
Conclusion:
The radiomics and deep learning-based approach presented in this study offers a reliable method for preoperative meningioma grading. This innovative method not only enhances accuracy but also reduces observer subjectivity, thereby contributing to improved clinical decision-making.&#xD.

Improving Speech Recognition with Convolutional Neural Networks

This project explores advanced techniques in speech recognition, focusing on emotion identification using Convolutional Neural Networks for improved accuracy and real-time processing efficiency. Emotion recognition from speech signals plays a crucial role in various applications, including human-computer interaction, customer service, mental health monitoring, and entertainment. This project proposes an innovative approach to emotion recognition using Convolutional Neural Networks (CNNs) applied to speech data. By leveraging advanced deep learning techniques, the proposed system aims to accurately identify and classify emotions conveyed through vocal expressions. The project begins with a comprehensive review of existing literature on emotion recognition and speech processing, identifying key challenges and opportunities in the field. Building upon prior research, the project introduces a novel CNN architecture optimized for emotion recognition tasks. This architecture is designed to extract relevant features from speech signals and capture subtle nuances indicative of different emotional states. One of the distinguishing features of the proposed approach is its multi-modal integration, which combines information from both audio and visual modalities to enhance emotion recognition accuracy. In addition to analysing speech signals, the system incorporates visual cues such as facial expressions and gestures, providing a more comprehensive understanding of the speaker's emotional state. Real-time processing efficiency is prioritized in the design of the system, ensuring prompt and responsive emotion recognition in interactive applications. Optimization techniques such as model quantization and lightweight architecture design are employed to minimize computational overhead while maintaining high accuracy. To address the variability and subjectivity of emotional expression, the system incorporates user-specific adaptation mechanisms. Through continuous learning and feedback integration, the system dynamically adapts to individual speakers' speech patterns and emotional characteristics, enhancing its ability to accurately recognize emotions in diverse contexts. The project also explores ensemble learning strategies to improve robustness and generalization performance. By combining predictions from multiple CNN models trained on diverse datasets, the system achieves greater resilience to variations in emotional expression and environmental factors. Ethical considerations, including privacy protection and responsible data handling, are integral aspects of the project's design and implementation. Measures are implemented to ensure the ethical collection, storage, and usage of speech data, safeguarding user privacy and maintaining trust in the system. Overall, the proposed system represents a significant advancement in emotion recognition technology, offering a sophisticated and versatile solution for accurately identifying emotions from speech signals. By leveraging deep learning techniques, multi-modal integration, real-time processing optimization, user-specific adaptation, and ensemble learning, the system demonstrates promising potential for various practical applications requiring robust and context-aware emotion recognition capabilities. Keywords: Speech Recognition, Emotion Identification, Convolutional Neural Networks (CNNs), Real-time Processing, Multi-modal Integration, User-specific Adaptation, Ensemble Learning, Deep Learning, Emotional Expression, Ethical Data Handling

Denoising Seismic Waveforms Using a Wavelet-Transform-Based Machine-Learning Method

ABSTRACT Seismic waveform data recorded at stations can be thought of as a superposition of the signal from a source of interest and noise from other sources. Frequency-based filtering methods for waveform denoising do not result in desired outcomes when the targeted signal and noise occupy similar frequency bands. Recently, denoising techniques based on deep-learning convolutional neural networks (CNNs), in which a recorded waveform is decomposed into signal and noise components, have led to improved results. These CNN methods, which use short-time Fourier transform representations of the time series, provide signal and noise masks for the input waveform. These masks are used to create denoised signal and designaled noise waveforms, respectively. However, advancements in the field of image denoising have shown the benefits of incorporating discrete wavelet transforms (DWTs) into CNN architectures to create multilevel wavelet CNN (MWCNN) models. The MWCNN model preserves the details of the input due to the good time–frequency localization of the DWT. Here, we use a data set of over 382,000 constructed seismograms recorded by the University of Utah Seismograph Stations network to compare the performance of CNN and MWCNN-based denoising models. Evaluation of both models on constructed test data shows that the MWCNN model outperforms the CNN model in the ability to recover the ground-truth signal component in terms of both waveform similarity and preservation of amplitude information. Model evaluation of real-world data shows that both the CNN and MWCNN models outperform standard band-pass filtering (BPF; average improvement in signal-to-noise ratio of 9.6 and 19.7 dB, respectively, with respect to BPF). Evaluation of continuous data suggests the MWCNN denoiser can improve both signal detection capabilities and phase arrival time estimates.

Deep learning-based image annotation for leukocyte segmentation and classification of blood cell morphology

The research focuses on the segmentation and classification of leukocytes, a crucial task in medical image analysis for diagnosing various diseases. The leukocyte dataset comprises four classes of images such as monocytes, lymphocytes, eosinophils, and neutrophils. Leukocyte segmentation is achieved through image processing techniques, including background subtraction, noise removal, and contouring. To get isolated leukocytes, background mask creation, Erythrocytes mask creation, and Leukocytes mask creation are performed on the blood cell images. Isolated leukocytes are then subjected to data augmentation including brightness and contrast adjustment, flipping, and random shearing, to improve the generalizability of the CNN model. A deep Convolutional Neural Network (CNN) model is employed on augmented dataset for effective feature extraction and classification. The deep CNN model consists of four convolutional blocks having eleven convolutional layers, eight batch normalization layers, eight Rectified Linear Unit (ReLU) layers, and four dropout layers to capture increasingly complex patterns. For this research, a publicly available dataset from Kaggle consisting of a total of 12,444 images of four types of leukocytes was used to conduct the experiments. Results showcase the robustness of the proposed framework, achieving impressive performance metrics with an accuracy of 97.98% and precision of 97.97%. These outcomes affirm the efficacy of the devised segmentation and classification approach in accurately identifying and categorizing leukocytes. The combination of advanced CNN architecture and meticulous pre-processing steps establishes a foundation for future developments in the field of medical image analysis.

Exploring Advanced Machine Learning Techniques for Swift Legume Disease Detection

Background: In the realm of agriculture, the insidious menace of legume crop diseases looms large, posing a significant threat to food security. This study embarks on a transformative journey, harnessing the prowess of Convolutional Neural Networks (CNNs), to fortify early disease detection in legume crops. By utilizing the inherent capabilities of deep learning, try to develop a sentinel that can identify even the most minor signs of crop diseases. Thorough data curation and preprocessing provide the system the ability to examine photos of legume leaves with previously unheard-of clarity. Methods: Meticulously crafted, the CNN architecture plays the role of a virtuoso, skilfully traversing the convolutional layers. It gains proficiency in the complex language of illness-induced aberrations via intense training, enabling it to discern between health and illness. Result: Provide remarkable results from the experimental experience using a wide range of assessment metrics. By undertaking this project, the commitment to preserving agricultural yields and, consequently, global food security is reaffirmed. It portends a more optimistic future for legume farming by indicating a ground-breaking effort at the nexus of artificial intelligence and agriculture.

DeepFake Image Detection

The growth of deepfakes in today’s digital environ- ment raises significant doubts regarding the genuineness and dependability of the content found. To overcome this new challenge, Developing an effective method in the context of detection of deep images. In this study, we conduct a comparative analysis of three varied convolutional neural networks (CNNs) for deepfake image detection. Our experimental results highlight the strengths and weaknesses of each CNN architecture. We deliberate on the consequences of our results in the context of deep image perception and show which models may be better for certain situations. We also address the challenges and limitations associated with deep learning, such as the arms race of deep learning technologies and tools. In conclusion, our work adds to the expanding body of knowledge regarding deep image detection by comparing three major CNN architectures. Our findings provide important guidance for researchers, prac- titioners, and policymakers working to improve the security and authenticity of content in an increasingly digital age. As deepfake technology continues to evolve, the information presented in this study sets the groundwork for development of more powerful and updated deepfake detection mechanisms. Keywords—Deepfake, image detection, convolutional neural networks (CNNs), ResNet, InceptionV3, DenseNet, face forgery detection, GAN, forensics, deep learning, artificial intelligence, convolutional layers, pooling layers, fully connected layers, augmented data, accuracy, precision, recall, F1 measure, Area Under Receiver Operating Characteristic Curve.

Detection of Military Targets on Ground and Sea by UAVs with Low-Altitude Oblique Perspective

Small-scale low-altitude unmanned aerial vehicles (UAVs) equipped with perception capability for military targets will become increasingly essential for strategic reconnaissance and stationary patrols in the future. To respond to challenges such as complex terrain and weather variations, as well as the deception and camouflage of military targets, this paper proposes a hybrid detection model that combines Convolutional Neural Network (CNN) and Transformer architecture in a decoupled manner. The proposed detector consists of the C-branch and the T-branch. In the C-branch, Multi-gradient Path Network (MgpNet) is introduced, inspired by the multi-gradient flow strategy, excelling in capturing the local feature information of an image. In the T-branch, RPFormer, a Region–Pixel two-stage attention mechanism, is proposed to aggregate the global feature information of the whole image. A feature fusion strategy is proposed to merge the feature layers of the two branches, further improving the detection accuracy. Furthermore, to better simulate real UAVs’ reconnaissance environments, we construct a dataset of military targets in complex environments captured from an oblique perspective to evaluate the proposed detector. In ablation experiments, different fusion methods are validated, and the results demonstrate the effectiveness of the proposed fusion strategy. In comparative experiments, the proposed detector outperforms most advanced general detectors.

Enhanced Diagnostics Based on Deep Learning for Varicose Vein Detection

Varicose veins, a prevalent vascular condition affecting a significant portion of the population, necessitate early detection for optimal intervention and improved patient outcomes. This study introduces a novel diagnostic approach for varicose vein detection, leveraging the capabilities of deep learning. Varicose veins represent a prevalent vascular condition that can lead to discomfort and complications if not identified and treated promptly. This research introduces an advanced diagnostic methodology for varicose vein detection, utilizing deep learning techniques. Our approach harnesses the capabilities of deep neural networks, specifically employing a convolution neural network (CNN) architecture. Trained on a diverse dataset of vascular images, the model demonstrates a high proficiency in identifying varicose veins by capturing nuanced patterns and subtle features. Fine-tuning through transfer learning ensures adaptability to variations in patient demographics and imaging conditions. To enhance diagnostic precision, our system incorporates advanced image processing techniques and feature extraction methods. The integration of image segmentation algorithms and anatomical feature analysis provides detailed insights into the extent and severity of varicose veins. Furthermore, the model is designed to accommodate multi-modal imaging data, incorporating information from various imaging modalities to enhance diagnostic accuracy. Validation of our approach is conducted on a comprehensive dataset encompassing a spectrum of varicose vein cases, including varying degrees of severity. The results underscore the superior performance of our deep learning-based diagnostic system, achieving notable sensitivity and specificity in varicose vein detection. This proposed enhanced diagnostic tool holds promise for healthcare professionals, streamlining the diagnostic process and facilitating early and accurate identification of varicose veins. The potential implications include improved patient outcomes through timely intervention and the development of personalized treatment strategies. As we continue refining and expanding our model, its integration into clinical practice could contribute to a more efficient and effective management of varicose vein disorders.

Efficient Bayesian CNN Model Compression using Bayes by Backprop and L1-Norm Regularization

The swift advancement of convolutional neural networks (CNNs) in numerous real-world utilizations urges an elevation in computational cost along with the size of the model. In this context, many researchers steered their focus to eradicate these specific issues by compressing the original CNN models by pruning weights and filters, respectively. As filter pruning has an upper hand over the weight pruning method because filter pruning methods don’t impact sparse connectivity patterns. In this work, we suggested a Bayesian Convolutional Neural Network (BayesCNN) with Variational Inference, which prefaces probability distribution over weights. For the pruning task of Bayesian CNN, we utilized a combined version of L1-norm with capped L1-norm to help epitomize the amount of information that can be extracted through filter and control regularization. In this formation, we pruned unimportant filters directly without any test accuracy loss and achieved a slimmer model with comparative accuracy. The whole process of pruning is iterative and to validate the performance of our proposed work, we utilized several different CNN architectures on the standard classification dataset available. We have compared our results with non-Bayesian CNN models particularly, datasets such as CIFAR-10 on VGG-16, and pruned 75.8% parameters with float-point-operations (FLOPs) reduction of 51.3% without loss of accuracy and has achieved advancement in state-of-art.

Signature Verification System Using Deep Learning

The proposed system employs a convolutional neural network (CNN) architecture for signature feature extraction and classification. Furthermore, the system integrates preprocessing modules for signature image normalization, noise reduction, and feature extraction to enhance the robustness and accuracy of the verification process. Extensive experimentation and evaluation are conducted on benchmark datasets, including the widely used Tobacco 800 dataset and Kaggle dataset, to assess the performance of the proposed system in terms of accuracy, precision, recall, and score metrics. The results demonstrate the effectiveness and robustness of the deep learning-based signature verification system in accurately distinguishing between genuine and forged signatures.

An Enhance CNN Model for Brain Tumor Detection from MRI Images

A Convolutional Neural Network (CNN) model for the precise classification and detection of brain tumors from Magnetic Resonance (MR) images is presented in this research. Leveraging the power of deep learning, the proposed CNN model demonstrates remarkable accuracy in distinguishing between different tumor types, aiding in effective diagnosis. The network is trained on a diverse dataset, enabling robust generalization to unseen cases. The proposed method not only enhances the classification performance but also facilitates early tumor detection. Operating on 256 x 256 image inputs, the proposed model boasts a complex architecture featuring six Conv2D layers, batch normalization, five MaxPooling2D layers, six dense layers, and an input layer. Throughout these layers, the ReLU activation function dominates with the final dense layer utilizing the sigmoid function. The potential of sophisticated CNN architectures in medical imaging is highlighted by this study. The outcomes validate the effectiveness of the CNN-based method, advancing computer-aided neuroimaging diagnostic tools and offering a useful tool for medical practitioners. The increased accuracy values demonstrate how well the sophisticated CNN model recognizes brain tumors and point to its potential as a more complex and dependable technique. It also presents a viable avenue for accurate and timely brain tumor identification.

Fruit Quality Analysis and Disease Detection using Deep Learning Techniques

Defective fruits are the main reason for worldwide financial catastrophes in agricultural production. It affects both the dependability and quality of the fruits. Post-harvest, quality checking requires a significant amount of time and labor-intensive skill. Automatically identifying fruit quality enables saving time and labor during harvest. Various algorithms have been created using machine learning and image processing methods to detect and categorize fruit quality. A system using Convolutional Neural Networks (CNN) and transfer learning techniques has been developed to enhance the fruit classification process. Two methods are suggested for predicting fruit freshness. One tailored CNN architecture is proposed by modifying the network's parameters to suit the dataset. The second technique utilizes the pre-trained VGG model with transfer learning to assess the freshness of the fruit. The proposed models are capable of differentiating between fresh and spoiled fruit by analyzing the input photos. This research used 70 diverse types of fruit, such as apples, bananas, oranges, and others. The first CNN obtains 98.55% accuracy for heterogeneous fruit dataset while second VGG16 achieves 99.05% accuracy on similar dataset. The plant village global dataset is utilized with various fruit categories. As a result, The VGG16 provides higher accuracy than conventional CNN and other deep learning algorithms.