Convolutional Neural Network Technique Research Articles

Botanic Shield: A Deep Learning Solution for Early Detection of Plant Disease

Abstract: Plant diseases threaten both food security and the botanical system of the environment which canlead to low- grade food quality and life style. Early detection of plants diseases is the key feature in plantproduction which increase the better growth of plant, results in best yield and better food quality andquantity. The plant condition can be determined byusing the photos of the plant which will be in the formof jpg using digital image processing and the current emerging technology Deep Learning. DeepLearning has made breakthroughs in the field of digital image processing, far superior to traditional methods. The Digital image processing process happens through convolution neural network (CNN) and computer vision techniques, the image recognition technology based on deep learning does not need to extractspecificfeatures, and only through iterative learning can find appropriate features, which can acquire global andcontextual features of images, and has strong robustness and higher recognition accuracy. By inputting a test image into the classification network, the network analyses the input image and returns a label that classifiesthe image. The added feature is thatit also suggeststhe required pesticides after the detection

Plant Leaf Disease Detection and Suggesting Medication using Image Processing and Convolutional Neural Network Techniques

In day-to-day life many factors that affect plants. Many problems are occurring at a rapid pace and new diseases are rapidly being identified. In today’s world of pollution and soil quality, disease detection is essential and its supplement is required to protect the plants. The main motivation of doing this project is to present a disease prediction model for the prediction of occurrence of plant crop disease. Further, this research work is aimed towards identifying the best classification algorithm for identifying the possibility of disease in plants. The identification of the possibility of disease in plants is complicated task for practitioners because it requires years of experience and intense tests to be conducted. The main objective of this significant research work is to identify the best classification algorithms suitable for providing maximum accuracy when classification of normal and abnormal plants is carried out. Convolutional neural network (CNN) architecture is used to map the relationship between the indoor PM and weather data to the found values. The proposed method is compared with the state-of-the-art deep neural network (DNN) based techniques in terms of the root mean square and mean absolute error accuracy measures. The applied CNN classification helps to predict the heart disease with more accuracy in the new data set. The coding language used is Python 3.10..

Automated Prediction of Malignant Melanoma using Two-Stage Convolutional Neural Network.

A skin lesion refers to an area of the skin that exhibits anomalous growth or distinctive visual characteristics compared to the surrounding skin. Benign skin lesions are noncancerous and generally pose no threat. These irregular skin growths can vary in appearance. On the other hand, malignant skin lesions correspond to skin cancer, which happens to be the most prevalent form of cancer in the United States. Skin cancer involves the unusual proliferation of skin cells anywhere on the body. The conventional method for detecting skin cancer is relatively more painful. This work involves the automated prediction of skin cancer and its types using two stage Convolutional Neural Network (CNN). The first stage of CNN extracts low level features and second stage extracts high level features. Feature selection is done using these two CNN and ABCD (Asymmetry, Border irregularity, Colour variation, and Diameter) technique. The features extracted from the two CNNs are fused with ABCD features and fed into classifiers for the final prediction. The classifiers employed in this work include ensemble learning methods such as gradient boosting and XG boost, as well as machine learning classifiers like decision trees and logistic regression. This methodology is evaluated using the International Skin Imaging Collaboration (ISIC) 2018 and 2019 dataset. As a result, the first stage CNN which is used for creation of new dataset achieved an accuracy of 97.92%. Second stage CNN which is used for feature selection achieved an accuracy of 98.86%. Classification results are obtained for both with and without fusion of features. Therefore, two stage prediction model achieved better results with feature fusion.

Real-time Speech Emotion Recognition using Machine Learning

This project is centred around examining spoken language emotion, a critical aspect of human-computer interaction and artificial intelligence. Leveraging advanced techniques such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models, we aim to decipher and understand the nuanced expressions of emotions conveyed through speech. The investigation draws from diverse datasets, including RAVDESS, SAVEE, TESS, and CREAM-D, each carefully chosen to encapsulate a broad spectrum of emotions and real-world speech scenarios. The project unfolds by elucidating the methodologies employed in dataset selection, the prepossessing of raw audio data, and the intricacies of utilizing CNN and LSTM techniques for speech emotion analysis. The main objective is to create a robust and reliable outcome model capable of accurately classifying and interpreting emotions in speech across various contexts. Practical applications of this Research extend to fields such as sentiment analysis and potential contributions to mental health monitoring. Through this project, we contribute valuable insights to the evolving landscape of speech emotion analysis, addressing the inherent challenges and exploring opportunities for enhancing human-computer interaction and emotional intelligence in artificial systems.

OPTIMIZATION OF CNN + MOBILENETV3 FOR INSECT IDENTIFICATION: TOWARD HIGH ACCURACY

Developments in the field of artificial intelligence and deep learning, particularly Convolutional Neural Networks (CNN) techniques, have expanded the research potential and applications in ecology, including efficient and accurate insect classification. However, there are challenges in achieving high levels of accuracy with similar computational efficiency. In response, the efficient MobileNetV3 architecture was investigated to improve the insect pest classification process. Through an analytical descriptive quantitative approach and insect datasets from Kaggle, this study tested the effectiveness of CNN models optimized with MobileNetV3. The results indicated that the optimized model achieved classification accuracy of up to 90%, with consistent performance between training and validation data and significant loss reduction. With high precision and processing efficiency, this discovery makes a substantial contribution to deep learning applications in the field of intelligent agriculture, promising methodological improvements for other classification problems. Despite offering a promising solution, this study recognizes the limitation in dataset diversity and suggests further exploration with more varied datasets to strengthen the model's application in actual agricultural practices.

BRAIN NEOPLASM IDENTIFICATION USING DEEP LEARNING

This study offers a powerful brain tumor detection system that makes use of deep learning techniques, especially the VGG16 learning algorithm and Convolutional Neural Network techniques (CNN). The ultimate objective of the recommended strategy is to enhance the accuracy and efficiency of identifying tumors in magnetic resonance imaging (MRI) images.. Utilizing CNN allows the model to automatically extract intricate features from input images, while VGG16, renowned for its deep architecture, contributes to a more intricate understanding of complex patterns. The integration of these algorithms empowers the system to discern subtle nuances indicative of brain tumors, providing a reliable and swift diagnostic tool for medical practitioners. Experimental results demonstrate the system's effectiveness in achieving high accuracy rates, showcasing its potential as an invaluable asset in early tumor detection and subsequent medical intervention. KEYWORDS: CNN, Brain tumor, Machine learning, Medical imaging

Analyzing the Deep Learning-Based Mobile Environment in Educational Institutions

More efficient instructional models that encourage students to engage more actively in their education are essential in today’s world. How information can be found and exchanged, as well as how explanations are delivered, have been shaped by technology. The outcomes demonstrate that mobile learning (m-learning) has enhanced collaboration among instructors and learners, provided immediate feedback, increased student participation and engagement, allowed for authentic learning and evaluation, and supported learning communities in higher education institutions. There was also a change in the teachers’ instructional methods. Students will benefit from simple learning activities, convenient communication, and coaching provided through mobile devices. This study recommends using a learning-based convolutional neural network (CNN) technique to improve students’ English-speaking fluency. In addition, communication between teachers and children in remote areas becomes easier through an interactive system, making both sides more accessible. As a result, it is advisable that educational institutions continually develop innovative teaching techniques that bridge face-to-face and formal-informal learning in the educational setting. The article discusses the background of mobile learning and how it might enhance e-learning as a whole. The m-learning approach is described in this paper as a future version of e-learning. In comparison to the next generation of educational systems, it will be extensively offered and simple to operate for any individual interested in learning. The article also discusses the benefits and potential drawbacks of mobile learning in current educational institutions.

Deep Learning-Enhanced Inverse Modeling of Terahertz Metasurface Based on a Convolutional Neural Network Technique

The traditional design method for terahertz metasurface biosensors is cumbersome and time-consuming, requires expertise, and often leads to significant discrepancies between expected and actual values. This paper presents a novel approach for the fast, efficient, and convenient inverse design of THz metasurface sensors, leveraging convolutional neural network techniques based on deep learning. During the model training process, the magnitude data of the scattering parameters collected from the numerical simulation of the THz metasurface served as features, paired with corresponding surface structure matrices as labels to form the training dataset. During the validation process, the thoroughly trained model precisely predicted the expected surface structure matrix of a THz metasurface. The results demonstrate that the proposed algorithm realizes time-saving, high-efficiency, and high-precision inversion methods without complicated data preprocessing and additional optimization algorithms. Therefore, deep learning algorithms offer a novel approach for swiftly designing and optimizing THz metasurface sensors in biomedical detection, bypassing the complex and specialized design process of electromagnetic devices, and promising extensive prospects for their application in the biomedical field.

Development of A Palm-Vein Recognition System for Identification and Verification Systems using Enhanced Convolutional Neural Network

Biometric authentication systems have gained significant attention in access control applications due to their ability to provide enhanced security and convenience. Among various biometric modalities, palm-vein recognition has emerged as a promising approach, offering high accuracy, reliability, and resistance to forgery. However, existing palm-vein recognition systems often face challenges in implementation costs, computational efficiency, and performance limitations. This research aimed to develop an enhanced palm-vein recognition system for access control applications by optimizing a Convolutional Neural Network (CNN) architecture. A palm-vein dataset comprising 1000 images from 200 LAUTECH students was acquired, with 5 images per individual. The dataset was split into 700 training images and 300 testing images. The acquired images were pre-processed for quality enhancement and region of interest extraction. A Gravitational Search Algorithm (GSA) optimized CNN (GSA-CNN) was then employed to extract deep features from the pre-processed images, which were classified using a SoftMax layer. Experimental results revealed that the CNN technique achieved a specificity, sensitivity, False Positive Rate (FPR), accuracy of 74.60%, 79.89%, 25.40%, 77.67% at 117.52 seconds, respectively. In contrast, the proposed GSA-CNN technique demonstrated superior performance, achieving a specificity, sensitivity, FPR, accuracy of 92.06%, 92.53%, 7.94%, 92.33% at 97.14 seconds, respectively. The GSA-CNN system outperformed the conventional CNN approach in terms of accuracy, specificity, sensitivity, FPR, and processing time, demonstrating its potential for reliable and efficient palm-vein recognition in access control applications. The findings have significant implications for developing robust and secure access control systems, contributing to enhanced privacy and security across various domains.

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

Enabling temporal–spectral decoding in multi-class single-side upper limb classification

This manuscript presents a novel approach for decoding pre-movement patterns from brain signals using a two-stage-training temporal–spectral neural network (TTSNet). The TTSNet employs a combination of filter bank task-related component analysis (FBTRCA) and convolutional neural network (CNN) techniques to enhance the classification of single-upper limb movements in non-invasive brain–computer interfaces (BCIs).In our previous work, we introduced the FBTRCA method which utilized filter banks and spatial filters to handle spectral and spatial information, respectively. However, we observed limitations in the temporal decoding phase, where correlation features failed to effectively utilize temporal information because of misaligned onset and noisy spikes. To address this issue, our proposed method focuses on analyzing multi-channel signals in the temporal–spectral domain. The TTSNet first divides the signals into various filter banks, employing task-related component analysis to reduce dimensionality and eliminate noise, respectively. Subsequently, a CNN is employed to optimize the temporal characteristics of the signals and extract class-related features. Finally, the class-related features from all filter banks are concatenated and classified using the fully connected layer.To evaluate the effectiveness of our proposed method, we conducted experiments on two publicly available datasets. In binary classification tasks, the TTSNet achieved an improved accuracy of 0.7707 ± 0.1168, surpassing the performance of EEGNet (accuracy: 0.7340 ± 0.1246) and FBTRCA (accuracy: 0.7487 ± 0.1250). In multi-class tasks, TTSNet achieved an accuracy of 0.4588 ± 0.0724, exhibiting a 4.27% and 3.95% accuracy increase over EEGNet and FBTRCA, respectively.The findings of this study suggest that the proposed TTSNet method holds promise for detecting limb movements and assisting in the rehabilitation of stroke patients. The classification of single-side limb movements is expected to facilitate the interaction between patients and external environment by increasing the number of control commands in BCIs.

Learning-Based Approach for Automated Surface Inspection with Industrial Tomography Imaging

Abstract In recent years, advanced deep learning techniques have emerged as pivotal tools in enabling the development of robust vision-based solutions for steel surface inspection. This resulted in enhanced inspection accuracy, all while significantly reducing costs in the manufacturing industry. However, the lack of actual steel surface defects datasets currently places a certain constraint on further research into classifying those anomalies. As a consequence, the Convolutional Neural Network (CNN) technique, known for its prowess in image-related tasks, faces certain challenges especially in classifying less common defects. This work proposes a novel hybrid CNN model with an SVM classifier at the output layer for surface defects classification. The features extracted from the pre-trained ResNet152 and EfficientB0 CNN algorithms are concatenated and fed to the SVM layer for classification. Extensive experiments on a merged dataset consisting of the publicly available Northeastern University (NEU) dataset and Xsteel surface defect dataset (X-SDD) are carried out and the accuracy and F1 scores are calculated for performance evaluation. The merged dataset contains eleven typical defect types with a total of 2660 defect images. Then, the adopted algorithm is compared with ten fine-tuned deep learning models to evaluate the performance of transfer learning for steel defect detection and identification. The evaluation results show that the deep feature extraction and SVM classification produced better results than the transfer learning. Finally, the proposed classifier model is validated on a new collected dataset from a Computed Tomography scanner with an accuracy reaching over 96%.

Proactive Analysis and Detection of Cyber-attacks using Deep Learning Techniques

Objectives: This study objective is to create a proactive forensic framework with a classification model to identify the malicious content to avoid cyber-attacks. Methods: In this proposed work, a novel framework is introduced to analyze and detect network attacks before it happens. It monitors the network packet flow, captures the packets, analyzes the packet flow proactively, and detects cyber-attacks using different machine learning algorithms and Deep Convolution Neural network (CNN) technique. The KDD dataset is used in this experiment with 30% for testing and 80% for training. Findings: The simulation results show that the detection percentage of the proposed framework reaches a maximum of 95.92% in different scenarios. It is approximately 10% higher than the existing proactive frameworks for example Gawand’s model, Ahmetoglu’s model and many more. Novelty and applications: The proposed framework is a proactive model which detects the cyber-attack in prior to avoid cyber-attacks. The deep CNN model highly efficient for detecting cyber-attack. Keywords: Proactive Forensic Framework, Deep CNN, Classification Algorithms, Cyber attack detection, Intrusion Detection System

A hybrid deep learning approach to integrate predictive maintenance and production planning for multi-state systems

This paper develops a data-driven approach to dynamically integrate tactical production and predictive maintenance planning for a multi-state system composed of several series-parallel machines. The objective is to determine an integrated lot-sizing and preventive maintenance strategy that will minimize the sum of maintenance and production costs, while satisfying the demand for all products over the entire horizon. A rolling horizon planning strategy is adopted to continuously update the production and maintenance plans based on new data obtained through sensors. Unlike the existing integrated models, we develop a hybrid deep learning (DL) approach to coordinate maintenance and production decisions for a multi-state system composed of multiple machines. To accurately predict the health condition of each machine, the developed hybrid DL method combines the powers of convolutional neural network (CNN), long-short-term memory (LSTM), and attention technique. We use multi-state reliability theory to estimate the production capacity. Furthermore, a genetic algorithm is developed to solve large-scale problems. Benchmarking data are used to compare the results of our data-driven approach with a model-based approach, a pure LSTM, and a CNN-LSTM approach. This comparison is based on prediction accuracy, solution quality, and computational time. The obtained results show the superiority of the suggested CNN-LSTM-attention data-driven framework integrating maintenance and production.

Automatic Testing Technology of BTB Liquid Crystal Display Advanced Fault Detection in Smart Meter for Smart Machine

In smart meter technology, the reliability of Backplane to Bezel (BTB) Liquid Crystal Displays (LCDs) is crucial for efficient functioning within intelligent grid systems. Defects in these LCD screens can significantly impact overall smart meter performance, necessitating effective detection methods for proper management and utilization. Current detection approaches, which combine manual and automatic methods based on machine vision, have shown unsatisfactory performance. This research addresses this challenge by proposing a novel method for advanced fault detection in BTB LCDs of smart meters, Automatic Testing Technology of BTB Liquid Crystal Display Advanced Fault Detection in Smart Meter for smart machine (BTB-LCD-FD- MCDSGAN) is proposed. The study begins by collecting datasets specifically tailored for LCD screen localization and defect detection. To enhance data quality, a Window Adaptive Extended Kalman Filter (WAEKF) is applied during preprocessing for noise removal. Feature extraction follows, utilizing Parameterized Multi Synchrosqueezing Transforms (PMST) with a primary focus on Gray Level Co-occurrence Matrix features. These extracted features serve as input for classification by a Multimodal Contrastive Domain Sharing Generative Adversarial Network (MCDSGAN), categorizing defects into five types likes normal display, no display, abnormal display, liquid crystal rupture, and incomplete display. Furthermore, the proposed method optimizes the MCDSGAN weight parameter using the Red Fox Optimization Algorithm (RFOA) to achieve accurate LCD fault defect prediction. The entire approach is implemented in Python, performance metrics likes accuracy, precision, recall, F-score, computational time are thoroughly analyzed. Performance of proposed BTB-LCD-FD-MCDSGAN approach attains 20.89%, 33.67% and 25.98% high accuracy, and 17.98%, 23.78% and 33.45% higher recall compared with existing methods such as automatic detection of display defects for smart meters depend on deep learning (AD-AM-DL), deep learning-enabled image content-adaptive field sequential color LCDs by mini-LED backlight (DL-AFSC-LCD) and new multi category defect detection method depend on convolutional neural network technique for TFT-LCD panels (MDF-CNN-LCD), methods respectively.

An optimal model for detection of lung cancer using convolutional neural network

In terms of frequency and mortality, lung cancer ranks second among all cancers worldwide for both men and women. It is suggested that pattern classification and machine learning be applied to the identification and categorization of lung cancer. Convolution neural network (CNN) techniques divide the input data into groups according to the distinctive characteristics of the input. Using a standard approach to analyze a large number of computed tomography images, early detection of lung cancer can save lives. The suggested effort is centered on identifying the precise type of cancer and making predictions about whether it is benign or aggressive. The deployment of proposed model is an attempt to improve the accuracy of the system. The proposed work showed an overall accuracy of 98.4% during the detection of lung cancer and 98.8% accuracy towards the prediction of specific type in the lung cancer. Mean average precision score of 97.17% and 98.75% test and validation respectively. 0.96, 0.93, and 0.95 for malignant test data.

DEF: Deep Ensemble Neural Network Classifier for Android Malware Detection

Malware is one of the threats to security of computer networks and information systems. Since malware instances are available sufficiently, there is increased interest among researchers on usage of Artificial Intelligence (AI). Of late AI-enabled methods such as machine learning (ML) and deep learning paved way for solving many real-world problems. As it is a learning-based approach, accumulated training samples help in improving thequality of training and thus leveraging malware detection accuracy. Existing deep learning methods are focusing on learning-based malware detection systems. However, there is need for improving the state of the art through ensemble approach. Towards this end, in this paper we proposed a framework known as Deep Ensemble Framework (DEF) for automatic malware detection. The framework obtains features from training samples. From given malware instance a grayscale image is generated. There is another process to extract the opcode sequences. Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) techniques are used to obtain grayscale image and opcode sequence respectively. Afterwards, a stacking ensemble is employed in order to achieve efficient malware detection and classification. Malware samples collected from the Internet sources and Microsoft are used for the empirical study. An algorithm known as Ensemble Learning for Automatic Malware Detection (EL-AML) is proposed to realize our framework. Another algorithm named Pre-Process is proposed to assist the ELAML algorithm for obtaining intermediate features required by CNN and LSTM. Empirical study reveals that our framework outperforms many existing methods in terms of speed-up and accuracy.

Constraining primordial non-Gaussianity using neural networks

ABSTRACT We present a novel approach to estimate the value of primordial non-Gaussianity (fNL) parameter directly from the cosmic microwave background (CMB) maps using a convolutional neural network (CNN). While traditional methods rely on complex statistical techniques, this study proposes a simpler approach that employs a neural network to estimate fNL. The neural network model is trained on simulated CMB maps with known fNL in range of [−50, 50], and its performance is evaluated using various metrics. The results indicate that the proposed approach can accurately estimate fNL values from CMB maps with a significant reduction in complexity compared to traditional methods. With 500 validation data, the $f^{\rm output}_{\rm NL}$ against $f^{\rm input}_{\rm NL}$ graph can be fitted as y = ax + b, where $a=0.980^{+0.098}_{-0.102}$ and $b=0.277^{+0.098}_{-0.101}$, indicating the unbiasedness of the primordial non-Gaussianity estimation. The results suggest that the CNN technique can be widely applied to other cosmological parameter estimation directly from CMB images.

Road Accident Severity Detection In Smart Cities

Ensuring safety, in cities is a focus in the development of urban areas requiring new and creative methods for categorizing and managing accidents. Traditional approaches often face challenges in evaluating accident seriousness within changing city environments. This research utilizes Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) techniques to create a system that categorizes accidents into three severity levels; minor, moderate and severe. By leveraging learning capabilities, our method boosts the precision and efficiency of safety protocols in cities. The outcomes exhibit promising results in categorizing accident severity offering a tool for enhancing urban safety infrastructure. Through empowering cities to handle accidents, our model establishes a foundation for safety initiatives. In essence, this study contributes to enhancing safety standards in cities promoting resilience and sustainability, within settings.

A novel Bagged-CNN architecture for short-term wind power forecasting

ABSTRACT Wind power forecasting plays a pivotal role in ensuring the dependable integration of wind energy into power grids. This research introduces a novel Bagged-CNN architecture tailored for short-term wind power forecasting. This architecture harnesses the combined strengths of the Bagging ensemble technique and Convolutional Neural Networks (CNN) to enhance prediction accuracy and robustness. Our approach demonstrates superior forecasting capabilities by exploiting the spatial and temporal features captured by CNNs. To validate our proposed architecture, we have conducted a comprehensive evaluation across three distinct wind farms, each characterized by unique geographical and climatic conditions. We systematically compared the performance of our Bagged-CNN model against several standalone machine-learning algorithms commonly employed in wind power forecasting. The hyperparameters of our Bagged-CNN model are meticulously optimized using the GRID search algorithm, which systematically explores a predefined parameter space. Notably, our predictions shed light on the substantial impact of training data period length on wind power prediction accuracy. Our research underscores the potential of coupling Bagging and CNN techniques for short-term wind power forecasting. The Bagged-CNN architecture, as proposed, serves as a robust framework for capturing intricate spatial and temporal patterns in wind power data, thereby facilitating more precise and dependable predictions.