Asian Journal of Research in Computer Science

Though all brain tumors are not cancerous but they caused a critical disease produced by irrepressible and unusual dividing of cells. For the case of Medical diagnostics of many diseases, the health industry needs help, the current development in the arena of deep learning has assisted to detect diseases. In recent years medical image classification has gained remarkable attention. The most well-known neural network model for image classification problems is the Convolutional Neural Network (CNN). CNN is the frequently employed machine-learning algorithm that is used in Visual learning and Image Recognition research. It is considered to derive features adaptively through convolution, activation, pooling, and fully connected layers. In our paper, we present the convolutional neural network method to determine cancerous and non-cancerous brain tumors. We also used Data Augmentation and Image Processing to classify brain (Magnetic Resonance Imaging (MRI). We used two significant steps in our proposed system. First, different image processing techniques are used to preprocess the images and secondly we classify the preprocessed image using CNN. Brain tumor classification is a process of identifying and separating the cancerous and non-cancerous brain tissues and labeling them automatically. We use the famous machine learning algorithms Convolutional Neural Network which is broadly employed for image classifications. This experiment is conducted on a dataset of 2065 images. In our dataset number of training, examples are 1445, the number of validation examples is 310, and the number of testing example is 310. We also used data augmentation to raise the number of the dataset. We achieved a high testing accuracy of 94.39%. The proposed system displayed sufficient accuracy on the dataset and beat many of the noticeable present methods.

Aims: The data which is continuously being produced by hundreds of thousands of data sources is recognized as streamed data. The data which is processed via this kind of source is relatively smaller in size and is being sent at the same time it is generated.

Study Design:  In streaming data, the data range is so wide like the telemetry from interconnected devices or other such forms of data with the inclusion of certain web applications. This information should be handled consecutively and steadily on a record-by-record premise or throughout sliding time windows and utilized for a wide assortment of examinations including relationships, totals, separating, and inspecting.

Place and Duration of Study: Service usage (for metering and billing), server activity, website clicks, and the geo-location of devices, people, and physical goods are just a few of the many aspects of a company's business and customer activity that can be seen through this type of analysis. It also enables companies to respond quickly to new arising situations.

Methodology: The research methodology is used for the current research work is the qualitative method through which the research studies of a similar domain are studied thoroughly. It has been analyzed that the specified changes in the large volumes of data can better be managed through stream data processing.

Results: The flaws of batch data processing are better dealt with through the usage of streaming data processing agenda. Real-time monitoring as well as response functionality are the keys to success in the given method of data processing.

Conclusion: Stream data processing connects analytics and applications. Because multiple systems can be constructed using the same architecture, this makes the construction of the infrastructure a similar architecture. It additionally allows designers to fabricate applications that utilize scientific outcomes to straightforwardly answer information experiences and make a move.

In the present scenario, the wired network or wireless networks is an application across the world. So the wire networks used in various software industries, educational institutions, and various enterprises used such as distributed data centers. The data transmission works like a flow of electricity in a linear way. So in this process during the data transmission exhibits from one stage to another the energy consumption in carbon footprint (i.e. CO₂). The data transmission is two types of methods (1) Communication-Based (2) Component-Based. Here this paper concludes the compared study of component-based energy consumption using the Bellman-Ford Algorithm and Dijkstra's Algorithm. The results and finding measurement as discussed in this paper.

The main issues with machine learning-based feature extraction techniques are the requirement of extensive domain-level knowledge, experience, and the need to be supported by large amounts of data that are sometimes not available. Moreover, it is often difficult to apply domain-level knowledge to extract the necessary features for building a machine-learning classifier. Therefore, it is significantly important to find and develop feature extraction techniques that depend mainly on the training data and don’t require or depend on domain-level knowledge and experience. To address these issues for binary classification problems, a novel feature extraction approach, AE-FT(Fixed Target) for extracting common features using a Deep Belief Network (DBN)-based Autoencoder (AE) is proposed in this paper. In this approach, common features are extracted by a DBN trained on a dataset sample’s binary using the Fixed Target training approach.

The proposed common features extraction approach is tested and evaluated on two different data sets. For each dataset, the extracted features are used to train seven of the common machine learning binary classification algorithms and compared their performances. Moreover, the number of extracted features is very small compared to other existing feature extraction methods. Therefore, the proposed common features extraction method improves the performance of the binary classification algorithms by reducing the number of features reducing laborious processes, and increasing the recognition accuracy effectively.

The results show that the proposed common features extraction approach, without any domain-level knowledge or human expertise, provides a very good performance compared to other feature extraction techniques.

Capturing commonly occurring behaviors is a tough issue in computer vision. A few of them are recreation, touring, leisure pursuits, and religious practice. A comprehensive effort has already been dedicated to this aspect to deal with this issue. In this work, we recreated a dataset with five categories, including household activities, farming, exercise, sports, and occupation, to identify human daily actions. This collection has 4328 colored images in total, among them 630 are set aside for testing, and 3698 for training. Deep learning and standard image-based strategies are being explored to address the issues. In this paper, we have designed a deep learning paradigm to classify the regular activities of human beings. To characterize people's daily chores, we use the CNN model, one of the greatest tools for visual identification. We also have chosen two already-trained VGG16 and ResNet50 models. When we compare our model with the existing techniques, the investigation's findings demonstrate that the suggested network has a better recognition accuracy of 91%. Additionally, we have observed that accuracy varies throughout different epochs, and after 25 epochs we got better stable results from our model. The reader may find this article instructive in grasping CNN models for various recognizing applications.