Статьи журнала - International Journal of Engineering and Manufacturing
Все статьи: 520
Статья научная
In the modern era agriculture development is the highly contribute field of food security. Data Science is one of the top analysis experimental methods for forecasting and mapping synchronize. In our study, we experiment with three major parameters (Rainfall, Relative Humidity and Temperature) that can be affected crop production rate as well as area-based mapping. To complete the procedure, the cluster groping and prediction system has created a machine learning BOT combined analysis system. Bangladesh and its 13 areas with 46 years of data have visualized with proper analysis and build up a 2D map of each separate production area. Multi Linear Regression (MLR) and KMean Clustering is the main key point algorithm for the production analysis. Experiment analyzing, we can see that some elements of our environment are closely associated with the productivity of the crop. An untactful environmental change on parameters (Rainfall, Humidity, and Temperature) reduces agricultural productivity by 32-38%. Developed model accuracy 91.25% forecasting methodological analysis for production mapping and prediction. Extreme population food security has ensured ICT and Agriculture combine BOT & EVPM method is essential for the scientific world. This study will allow farmers to choose the proper crop in the right environmental condition, which will play a key role in strengthening the economy of the country.
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Cystic Region Detection Using Hybrid Fuzzy-based Multi-Region Normalization
Статья научная
One of the main purposes of this approach is to automatically extract the cystic border. Several of the semi-automatic segmentation strategies that have already been used may result in incomplete categorization, which is likely to fail as well as causes solitary pixel in noise dentistry x-rays images due to sampling artifact. As such, cyst boundaries are not removed appropriately. It focuses on the elimination of solitary pixels caused by artefacts. This suggested technique uses both the fuzzy memberships function of every pixel as well as localized spatially information of the neighbor pixels to accomplish the maximum feasible levels of automated processes for computers-aided diagnostics or identification of illnesses. That fuzzy-based multi-region normalization is implemented in five phases. To begin, FCM techniques are used to determine the numbers of centroids. This fuzzified function is constructed as well as provides memberships degree numbers to any and every pixel within every class based on the number of cluster centers as well as the shape of the histogram. It generates an intermediary segmentation output by fuzzy memberships degrees at about this step. This fuzzy localized aggregating of the neighborhood pixels will be the fourth phase, with the greatest responsiveness of the memberships degree generating pixels being kept in mind only for ultimate cystic area retrieved outputs.
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DataViz Model: A Novel Approach towards Big Data Analytics and Visualization
Статья научная
Big Data is the collection of large data sets which contains large amount of data. There are different areas which are generating huge data, this data may be present in the form of semi-structured or unstructured and to get useful information from such raw data there is need of data analysis. Due to Big Data’s excessive volume, variety, and velocity it is very difficult to store and process huge data. The process of extracting the information from such raw data is called Big Data Analytics. Big data Analytics processes data gives result in the form of structured data. Again this data is huge size and very difficult to understand since it is present in the form of CSV or excel or simple text files. So for effective decision making and to understand the information quickly the data need to be visualized as human mind understands images and graphs better and faster than text data. In this paper a model called Data Visualization (Viz) is designed which integrates big data analytics and the data visualization. This model first takes the data from various sources and then processes it and converts it into structured form, if want this data can be stored to RDBMS. Finally the text result can be visualized with the help of Visualization module of the DataViz. Here text result is represented in the form of charts and graphs.
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Database Design of a General Data Analysis System of Commodity Sales Information
Статья научная
A data analysis system for the general commodity sales information is researched on and further designed. The key problems of designing this system are that, it should enable the system to adjust to user-data with different structures, enable users to define or change data structures as well as retrieval methods. Through the research on the general structure and retrieval method of the commodity sales data, the system realizes users' customization of the database, thus is applicable to sales data with different structures. This paper focuses on the structure of the database, the creation method and process of the database by the user, the structure of the data dictionary and data exchange between database and software, with case examples in the final illustration.
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DbDMAD: a Database of DNA Methylation in Human Age-related Disease
Статья научная
DNA methylation plays a variety of crucial roles in cell division, proliferation, development of aging life, development of genetic diseases related to uniparental disomy, and carcinogenesis. DNA methylation can be probed by HPLC and gene chip which is very helpful to the high-through methylation test. In recent years many published articles reported that DNA methylation may be linked with human aged disease. Mining and integration of DNA methylation in human aged disease can be beneficial to novel biological discoveries. There has not been DNA methylation database repository which is exclusive for human aged disease. Therefore, we developed dbDMAD: a database of DNA methylation in human aged diseases, there are two purposes of it, one is to store DNA methylation in human aged disease datasets which were obtained from laboratory experiments, another is to find the relationship of different aged diseases. This is the first release of dbDMAD in which users can find 12 kinds of human aged diseases and relevant DNA methylation information. It can be searched by disease name and gene ID. This database also includes a visualization tool named ChainMap, by which the map of methylation pathway can be shown.
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Deep Convolution Neural Networks for Cross-Dataset Facial Expression Recognition System
Статья научная
Facial Expressions are a true and obvious way to represent emotions in human beings. Understanding facial expression recognition (FER) is essential, and it is also useful in the area of Artificial Intelligence, Computing, Medical, Video games, e-Education, and many more. In the past, much research was conducted in the domain of FER using different approaches such as analysis through different sensor data, using machine learning and deep learning framework with static images and dynamic sequence. Researchers used machine learning-based techniques such as the Multi-layer Perceptron Model, k- Nearest Neighbors, and Support Vector Machines were used by researchers in solving the FER. These methods have extracted features such as Local Binary Patterns, Eigenfaces, Face-landmark features, and Texture features. Recently use of deep learning algorithms in FER has been considerable. State-of-the-art results show deep learning-based approaches are more potent than conventional FER approaches. This paper focuses on implementing three different Custom CNN Architecture training them on FER13 Dataset and testing them on CK+ and JAFFE Dataset including FER13 after fine-tuning. The three pre-trained models' on FER2013 after fine-tuning have significantly improved the accuracy of the resulting CNN on the target test sets between 65.12 % to 79.07% on the JAFFE dataset and 50.96% to 68.81% on the CK+ dataset.
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Deep Learning based Real Time Radio Signal Modulation Classification and Visualization
Статья научная
Radio Modulation Classification is implemented by using the Deep Learning Techniques. The raw radio signals where as inputs and can automatically learn radio features and classification accuracy. The LSTM (Long short-term memory) based classifiers and CNN (Convolutional Neural Network) based classifiers were proposed in this paper. In the proposed work, two CNN based classifiers are implemented such as the LeNet classifier and the ResNet classifier. For visualizing the radio modulation, a class activation vector (w) is used. Finally in the proposed work, it is performed the classification by using the Deep learning models like CNN and LSTM based modulation classifiers. These deep learning models extract the important radio features that are used for classification. Here, the bench mark dataset RadioML2016.10a is used. This is an open dataset which contains the modulated signal I and Q values fewer than ten modulation categories. After evolution of proposed model with bench mark dataset, it is applied with real time data collected through the SDR Dongle receiver. From the obtained real time signal, the modulation categories have been classified and visualized the radio features extracted from the radio modulation classifiers.
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Deep Learning-Based Potato Leaf Disease Detection Using CNN in the Agricultural System
Статья научная
Potatoes play a vital role as a staple crop worldwide, making a significant contribution to global food security. However, the susceptibility of potato plants to various leaf diseases poses a threat to crop yield and quality. Detecting these diseases accurately and at an early stage is crucial for the effective management and protection of crops. Recent advancements in Convolutional Neural Networks (CNNs) have demonstrated potential in image categorization applications. Therefore, the goal of this work is to investigate the potential of CNNs in detecting potato leaf diseases. As neural networks have become part of agriculture, numerous researchers have worked on improving the early detection of potato blight using different machine and deep learning methods. However, there are persistent problems related to accuracy and the time it takes for these methods to work. In response to these challenges, we tailored a convolutional neural network (CNN) to enhance accuracy while reducing the trainable parameters, computational time and information loss. To conduct this research, we compiled a diverse dataset consisting of images of potato leaves. The dataset encompassed both healthy leaves and leaves infected with common diseases such as late blight and early blight. We took great care in curating and preprocessing the dataset to ensure its quality and consistency. Our focus was to develop a specialized CNN architecture tailored specifically for disease detection. To improve the performance of the network, we employed techniques like data augmentation and transfer learning during the training phase. The experimental outcomes demonstrate the efficacy of our proposed customized CNN model in accurately identifying and classifying potato leaf diseases. Our model's overall accuracy was an astounding 99.22%, surpassing the performance of existing methods by a significant margin. Furthermore, we evaluated precision, recall, and F1-score to evaluate the model's effectiveness on individual disease classes. To give an additional understanding of the model's behavior and its capacity to distinguish between various disease types, we utilized visualization techniques such as confusion matrices and sample output images. The results of this study have implications for managing potato diseases by offering an automated and reliable solution for early detection and diagnosis. Future research directions may include expanding the dataset, exploring different CNN architectures, and investigating the generalizability of the model across different potato varieties and growing conditions.
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Deep neural network for human face recognition
Статья научная
Face recognition (FR), the process of identifying people through facial images, has numerous practical applications in the area of biometrics, information security, access control, law enforcement, smart cards and surveillance system. Convolutional Neural Networks (CovNets), a type of deep networks has been proved to be successful for FR. For real-time systems, some preprocessing steps like sampling needs to be done before using to CovNets. But then also complete images (all the pixel values) are passed as input to CovNets and all the steps (feature selection, feature extraction, training) are performed by the network. This is the reason that implementing CovNets are sometimes complex and time consuming. CovNets are at the nascent stage and the accuracies obtained are very high, so they have a long way to go. The paper proposes a new way of using a deep neural network (another type of deep network) for face recognition. In this approach, instead of providing raw pixel values as input, only the extracted facial features are provided. This lowers the complexity of while providing the accuracy of 97.05% on Yale faces dataset.
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Статья научная
Refer to this research, an intelligent fuzzy parallel switching Proportional-Derivative (PD) plus gravity controller is proposed for highly nonlinear continuum robot manipulator. Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of the most important challenging works. In order to provide high performance in nonlinear systems, switching partly sliding mode plus gravity controller is selected. Pure switching partly sliding mode plus gravity controller can be used to control of partly known nonlinear dynamic parameters of continuum robot manipulator. Conversely, this method is used in many applications; it must to solve chattering phenomenon which it can cause some problems such as saturation and heat the mechanical parts of continuum robot manipulators or drivers. In order to solve the chattering phenomenon, implement easily and avoid mathematical model base controller, Mamdani’s performance/error-based fuzzy logic methodology with two inputs and one output and 49 rules is parallel applied to pure switching partly sliding mode plus gravity controller. The results demonstrate that this method is a model-free controllers which works well in certain and partly uncertain system.
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Статья научная
Refer to this research, an intelligent robust fuzzy parallel feedback linearization estimator for Proportional-Integral-Derivative (PID) controller is proposed for highly nonlinear continuum robot manipulator. In the absence of robot knowledge, PID may be the best controller, because it is model-free, and its parameters can be adjusted easily and separately. And it is the most used in robot manipulators. In order to remove steady-state error caused by uncertainties and noise, the integrator gain has to be increased. This leads to worse transient performance, even destroys the stability. The integrator in a PID controller also reduces the bandwidth of the closed-loop system. Model-based compensation for PD control is an alternative method to substitute PID control. Feedback linearization compensation is one of the nonlinear compensator. The first problem of the pure feedback linearization compensator (FLC) was equivalent problem in certain and uncertain systems. The nonlinear equivalent dynamic problem in uncertain system is solved by using parallel fuzzy logic theory. To eliminate the continuum robot manipulator system’s dynamic; Mamdani fuzzy inference system is design and applied to FLC. This methodology is based on design parallel fuzzy inference system and applied to equivalent nonlinear dynamic part of FLC. The results demonstrate that the model free fuzzy FLC estimator works well to compensate linear PID controller in presence of partly uncertainty system (e.g., continuum robot).
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