Nagaiah Mohanan Balamurugan

Diabetic Eye Disease (DED) is a fundamental cause of blindness in human beings in the medical world. Different techniques are proposed to forecast and examine the stages in Prognostication of Diabetic Retinopathy (DR). The Machine Learning (ML) and the Deep Learning (DL) algorithms are the predominant techniques to project and explore the images of DR. Even though some solutions were adapted to challenge the cause of DR disease, still there should be an efficient and accurate DR prediction to be adapted to refine its performance. In this work, a hybrid technique was proposed for classification and prediction of DR. The proposed hybrid technique consists of Ensemble Learning (EL), 2 Dimensional-Conventional Neural Network (2D-CNN), Transfer Learning (TL) and Correlation method. Initially, the Stochastic Gradient Boosting (SGB) EL method was used to predict the DR. Secondly, the boosting based EL method was used to predict the DR of images. Thirdly 2D-CNN was applied to categorize the various stages of DR images. Finally, the TL was adopted to transfer the classification prediction to training datasets. When this TL was applied, a new prediction feature was increased. From the experiment, the proposed technique has achieved 97.8% of accuracy in prophecies of DR images and 98% accuracy in grading of images. The experiment was also extended to measure the sensitivity (99.6%) and specificity (97.3%) metrics. The predicted accuracy rate was compared with existing methods.

Suspicious pre- and post-activity detection in crowded places is essential as many suspicious activities may be carried out by culprits. Usually, there will be installations of surveillance cameras. These surveillance cameras capture videos or images later investigated by authorities and post-event such suspicious activity would be detected. This leads to high human intervention to detect suspicious activity. However, there are no systems available to protect valuable things from such suspicious incidents. Nowadays machine learning (ML)- and deep learning (DL)-based pre-incident warning alarm systems could be adapted to monitor suspicious activity. Suspicious activity prediction would be based on human gestures and unusual activity detection. Even though some methods based on ML or DL have been proposed, the need for a highly accurate, highly precise, low-false-positive and low-false-negative prediction system can be enhanced by hybrid or enhanced ML- or DL-based systems. This proposed research work has introduced an enhanced convolutional neural network (ECNN)-based suspicious activity detection system. The experiment was carried out and the results were claimed. The results are analyzed with the Statistical Package for the Social Sciences (SPSS) tool. The results showed that the mean accuracy, mean precision, mean false-positive rate, and mean false-negative rate of suspicious activity detections were 97.050%, 96.743%, 2.957%, and 2.927% respectively. This result was also compared with the convolutional neural network (CNN) algorithm. This research work can be applied to enhance the pre-suspicious activity alert security system to avoid risky situations.

Network data traffic is increasing with expanded networks for various applications, with text, image, audio, and video for inevitable needs. Network traffic pattern identification and analysis of traffic of data content are essential for different needs and different scenarios. Many approaches have been followed, both before and after the introduction of machine and deep learning algorithms as intelligence computation. The network traffic analysis is the process of incarcerating traffic of a network and observing it deeply to predict what the manifestation in traffic of the network is. To enhance the quality of service (QoS) of a network, it is important to estimate the network traffic and analyze its accuracy and precision, as well as the false positive and negative rates, with suitable algorithms. This proposed work is coining a new method using an enhanced deep reinforcement learning (EDRL) algorithm to improve network traffic analysis and prediction. The importance of this proposed work is to contribute towards intelligence-based network traffic prediction and solve network management issues. An experiment was carried out to check the accuracy and precision, as well as the false positive and negative parameters with EDRL. Also, convolutional neural network (CNN) machines and deep learning algorithms have been used to predict the different types of network traffic, which are labeled text-based, video-based, and unencrypted and encrypted data traffic. The EDRL algorithm has outperformed with mean Accuracy (97.20%), mean Precision (97.343%), mean false positive (2.657%) and mean false negative (2.527%) than the CNN algorithm.

In recent times, there has been a huge upsurge in malicious attacks despite sophisticated technologies in digital network data transmission. This research proposes an innovative method that utilizes the forward-propagation workflow of the convolutional neural network (CNN) algorithm to detect malicious information effectively. The performance comparison of this approach was accomplished using accuracy, precision, false-positive and false-negative rates with k-nearest neighbor (KNN) and support vector machine (SVM) algorithms. To detect malicious packets in the original dataset, an experiment was carried out using CNN’s forward-propagation workflow method (N = 11) as well as the KNN and the SVM machine learning algorithms with a significant value of 0.005. The accuracy, precision, false-positive and false-negative rates were evaluated to detect malicious packets present in normal data packets. The mean performance measures of the proposed forward-propagation method of the CNN algorithm were evaluated using the Statistical Package for the Social Sciences (SPSS) tool. The results showed that the mean accuracy (98.84%) and mean precision (99.08%) of the proposed forward propagation of the CNN algorithm appeared to be higher than the mean accuracy (95.55%) and mean precision (95.97%) of the KNN algorithm, as well as the mean accuracy (94.43%) and mean precision (94.58%) of the SVM algorithm. Moreover, the false-positive rate (1.93%) and false-negative rate (3.49%) of the proposed method appeared to be significantly higher than the KNN algorithm’s false-positive (4.04%) and false-negative (6.24%) as well as the SVM algorithm’s false-positive (5.03%) and false-negative rate (7.21%). Hence, it can be concluded that the forward-propagation method of the CNN algorithm is better than the KNN and SVM algorithms at detecting malicious information.

Disease prediction has become one of the most sophisticated problems in the Machine Learning field in Computer Science. Almost all of the existing machines learning models are fixated on single disease prediction. For Example:-Machine learning model for predicting Malaria, ML model for predicting Brain Cancer, ML Model for predicting fever et cetera. This research paper proposes a system using which multiple diseases can be predicted. At present, the proposed system can predict Malaria, Alzheimer's Disease, Tuberculosis, Pancreatic Cancer The proposed system used CNN, Random Forest and Logistic Regression for analysis and prediction of multiple diseases. The system is implemented using Flask API which is a framework for creating web applications in Python and using ML Libraries in Python such as TensorFlow, Scikit Learn, Pandas et cetera. The main purpose of this Research Paper is to provide a system which Medical Professionals can use to predict diseases present in the patient in a very quick manner.

Predictive analytics has a multiplicity of statistical schemes from predictive modelling, data mining, machine learning. It scrutinizes present and chronological data to make predictions about expectations or if not unexplained measures. Most predictive models are used for business analytics to overcome loses and profit gaining. Predictive analytics is used to exploit the pattern in old and historical data. People used to follow some strategies for predicting stock value to invest in the more profit-gaining stocks and those strategies to search the stock market prices which are incorporated in some intelligent methods and tools. Such strategies will increase the investor’s profits and also minimize their risks. So prediction plays a vital role in stock market gaining and is also a very intricate and challenging process. The proposed optimized strategies are the Deep Neural Network with Stochastic Gradient for stock prediction. The Neural Network is trained using Back-propagation neural networks algorithm and stochastic gradient descent algorithm as optimal strategies. The experiment is conducted for stock market price prediction using python language with the visual package. In this experiment RELIANCE.NS, TATAMOTORS.NS, and TATAGLOBAL. NS dataset are taken as input dataset and it is downloaded from National Stock Exchange site. The artificial neural network component including Deep Learning model is most effective for more than 100,000 data points to train this model. This proposed model is developed on daily prices of stock market price to understand how to build model with better performance than existing national exchange method.