The Prediction of Cryptocurrency Prices Using Neural Architectures and Sentiment Analysis

Cryptocurrency is the most secure, traceable, and reliable intangible currency because it uses blockchain technology. It eliminates the middle layer of financial institutes in the traditional economic system. Because of high returns in cryptocurrencies, investors and other firms invest a lot of money. But the prices of the cryptocurrencies are uncertain. Prices of cryptocurrencies are influenced by many factors like sentiments, trading volume, and similar. Researchers are doing plenty of work to predict the accurate prices of various cryptocurrencies. However, many of these methods cannot be used in real-time. Several deep learning models such as Neural networks (NN), Long short-term memory (LSTM), and Gated recurrent unit (GRU) have been utilized by researchers for predicting the price of cryptocurrencies and yet, are unable to achieve significant results. This work combines LSTM and GRU with sentiment analysis to precisely estimate bitcoin values. We have used Root means square error (RMSE) to evaluate the model performance with and without sentiments. Empirically, we have compared the results with the other state-of-the-art models and found better results using the proposed hybrid model incorporated with sentiments

Prediction of Product Rating based on Polarized Reviews using Supervised Machine Learning

E-commerce websites facilitate customers to leave their experiences in the form of textual reviews for a variety of products. Recently, online reviews have played significant influencing role in customers’ decision for purchasing. The reviews have information and first hand experience about products’ quality for customers. Free-text sections are frequently found on online review web pages in addition to star-level reviewing options. But on many web pages, we find only the former option. Therefore, there is a need to convert the text-written reviews to star-level on the basis of the information they contain. Automatic conversion of online text-based reviews has recently been emerged as an active field of research in machine learning and deep learning. This paper presents a supervised machine and deep learning based solution to transform text-based reviews to star-level numerical representation by exploiting polarization detected on the basis of lexical analysis. Experiments were conducted on famous Amazon dataset under different choices of regression and classification techniques. Experimental results have indicated that the use of polarized reviews can significantly improve the rating prediction

Extracting the most important Discrete Cosine Transform (DCT) Coefficients for Image Compression using Deep Learning

Image compression is all about reducing storage costs and making the transmission of huge image files feasible. This paper targets lossy image compression by estimating the most important Discrete Cosine Transform (DCT) coefficients through employing deep learning. DCT basically results in the transformation of an image to the frequency domain from the spatial domain. The first few coefficients, in the frequency domain of a transformed image, have great importance. They are the most informative, while the others are of the least importance. The abilities of Multi-Layer Perceptron (MLP) and Convolutional neural network (CNN) were exploited in order to find a reasonable estimate of important DCT coefficients. The target was to get a deep neural network (DNN) for the compression of digital images that has a reduced number of DCT coefficients that is; higher compression rate and better image quality upon reconstruction and improved generalization ability. To shorten the encoding-decoding time and to fasten the training of our deep neural networks, RELUs and Tangent Sigmoid were used. Experiments performed on a large set of grayscale images shows that only 15 out of 64 total available DCT coefficients result in more than 70% image quality and have a good compression ratio of 4:1. Moreover, the quality of images upon a subjective and objective evaluation of unseen data proves that our proposed MLP achieved better generalization as compared to CNN.

RDED: Recommendation of Diet and Exercise for Diabetes Patients using Restricted Boltzmann Machine

As per World Health Organization, noncommunicable diseases such as untimely birth, heart attacks, diabetes, and cancers are on the upswing. Diet intake that is insufficient or improper is known to cause a wide range of well-being illnesses. Due to the complexity of food components and a large number of dietary sources, it is difficult to select diets that must match one’s nutrition demands in real-time. Because of irrelevant material on proper food, patients are dependent on medicine rather than having precautionary steps in food consumption. Appropriate diet is especially crucial for persons living with chronic conditions and nutritionist food is essential for optimal health. An effective way to prevent disease is to eat a healthy nutritious diet. This study introduces the food and physical activity recommender system, which is capable of providing users with individualized and healthy nutrition recommendations based on their tastes as well as pathological medical data. Prescriptions characterize the ideal patient’s nutrition likes. In this paper, we show how Restricted-Boltzmann Machines, a type of two-layer undirected graphical model, can be utilized to describe ratings of food products. For this simple model, we provide effective learning and inference strategies that would be successfully applied to a food data set with over 100 million user-food ratings. When the predictions of the RBM model are created using different learning rates and several iterations, we attain an error rate of considerably below 0.30 percent in 50 epochs using 100 hidden nodes which fulfills our requirements. Hence, we want patients to use nutritious food rather than taking medicine to avoid an expensive trip to a physician

Industrial Control and Building Automation System Penetrating Testing using Modbus TCP Testbed

Industrial Control System (ICS) plays a vital role in industries as it controls industrial processes such as power plants, food production, transportation, water and gas distribution etc. Similarly Building Automation System (BAS) is utilized for control, energy efficiency and conservation of modern buildings. As both BAS and ICS systems are becoming increasingly interconnected with networking technologies and becoming a lucrative target for attacks thus pose a serious threat to the infrastructure they control. ICS and BAS networks have been using legacy protocols with implementation of ICT protocols and technologies to be connected with modern networks. Thus, they have lack of security implementation. This paper presented a test-bed for testing vulnerabilities in Modbus protocol on HVAC control system. Two MITM attack scenarios were discussed and performed to demonstrate the weakness in the Modbus TCP protocol. The proposed system was tested using EasyIO-FS-32 server class controller having Modbus RTU, TCP and BACnet MSTP, TCP

Multiclass Brain Tumor Classification from MRI Images using Pre-Trained CNN Model

A brain tumor is an accumulation of malignant cells that results from unrestrained cell division. Tumors can result in crucial effects if they are not promptly and accurately recognized. Misdiagnosis can result in ineffective therapy, which decreases the patient's survival rate. The standard procedure for determining the presence of brain tumors and the type of tumors is magnetic resonance imaging (MRI). But as technology advances, it gets harder to comprehend huge amounts of data generated in an acceptable time. However, building a deep learning model from the start requires collecting enormous amounts of labeled data, which is a costly, time-consuming operation. A method to solve these issues is transfer learning of a deep learning model that has already been trained on the ImageNet dataset. In this research, the classification of brain tumors using several pre-trained deep learning models, i.e., different variations of ResNet, VGG, and DenseNet models, are being trained on a brain tumor dataset and compared. According to experiments, the ResNet50 model with a fine-tuned and transfer learning approach has achieved the highest training accuracy of 99%, validation accuracy of 96%, and test accuracy of 80%.

A Survey of DDoS Attack Detection Strategies in Cloud

Cloud is known as highly-available platform that has become most popular among businesses for all IT needs. Being widely used platform, it’s also a hot target to cyber-attacks. Distributed Denial of Services (DDoS) is a great threat to cloud in which cloud bandwidth, resources and applications are attacked to cause service unavailability. In a DDoS attack, multiple botnets attack victims using spoofed IPs with a huge number of requests to a server. Since its discovery in 1980, numerous methods have been proposed for the detection and prevention of network anomalies. This study provides a background of DDoS attack detection methods in the past decade and a survey of some of the latest proposed strategies to detect DDoS attacks in the cloud, the methods are further compared for their detection accuracy

Diagnosis of Alzheimer’s Disease using Comparative Study on Machine Learning Models

The method of diagnosing and treating diseases can be improved by identifying the genes that cause diseases. Alzheimer’s disease (AD) is one of the neurodegenerative disease that slowly destroys memory as well as thinking abilities. It’s important to diagnose Alzheimer’s disease (AD) early on so that adequate treatment can be given to patient. That article compares various machine learning models for identify Alzheimer’s Disease and proves that which algorithm gives the most reliable results in detecting AD in advance. Machine learning is a backbone of technology and everything in our life related to machine learning technologies. In this study various biomarkers are developed based on different machine learning classifiers like Random Forest, K-NN, Support Vector Machine, AdaBoost and XgBoost for AD gene detection. Genome data is extracted from NCBI related to Alzheimer disease. After that features are extracted from this genome data. Then above machine learning classifiers are train on these features. Different results are obtained by using Self-Consistency test and 10 Cross Validation test. Random Forest in both test gives 100% results. KNN gives 73.17% and 86.33%, SVM gives 100% and 97% AdaBoost gives 74.02% and 87.42%, XgBoost gives 86.04%and 92.56%accuracy for self-consistency and 10 Cross Validation test respectively

Requirement-Based Automated Test Case Generation: Systematic Literature Review

There exist multiple techniques of software testing like requirement-based testing (RBT) an approach of software testing from which the tester can generate test cases on the base of requirements without considering the internal system’s structure. In the current area, automation testing is used to minimize time, cost, and human effort. As compared to automated testing, manual testing processes consume more human effort and time. Requirements are documented in natural language so there is no extra training required to understand requirements, RBT is the most used testing technique. Test cases generated with customer requirements are mainly focused on functional test cases. Most approaches focus on real-time embedded systems rather than UML diagrams because non-functional needs are not captured in test cases derived from UML diagrams. Metamodels can be used to extract information from requirements in some cases. Active testing approaches, bounded model checking, activity diagrams, Petri nets round strip strategy, and extended use cases are just a few of the typical ways used to generate test cases. In this article, multiple techniques of automated test case generation have been discussed which are not being addressed in state-of-art literature reviews. Studies included in this systematic literature review (SLR) are built on a set of three research objectives and a variety of high-quality evaluation criteria. Taxonomy has been presented based on test case generation with requirement-based techniques and tools. In the end, gaps and challenges have been discussed to assist researchers to pursue future work

A Treatise to Metrics and Frameworks for Semantic Social Networks: A Systematic Literature Review

In the semantic web, each piece of data, referred to as metadata, is accompanied by meaningful data. This information is then used by machines to make calculations-based decisions. This implementation of semantic web methods in social networks shapes the premise of Semantic Social Network (SSN). SSN creates a virtual group of friends and family where people can share resources, thoughts, by staying associated with their loved ones. This semantic social data can be used to gauge different conduct ascribes of SSN individuals utilizing measurements. The extricated results from these measurements can help experts of SSN to settle on better choices in giving offers, rewards, badges to individuals. There are frameworks available for semantic social network analysis as well. A systematic literature review (SLR) is presented in this paper by leading an overview of available metrics to calculate semantic social network member traits and frameworks used for social network analysis. The basic SLR has been aggregated by looking into research articles distributed in all around presumed scenes somewhere in the range of 2000 and 2020. A sum of 22 papers was painstakingly filtered through an orderly interaction and grouped appropriately. The essential target of this precise investigation is the assortment of all-important examinations on currently available metrics to calculate members ascribes of the semantic social network and the frameworks used for semantic network analysis. As a result, six metrics and nine frameworks are extracted and presented. Moreover, this paper also discusses possible ways to improve metrics for a better semantic social network (SSN) analysis. In recent years, countless efforts have been made in the Semantic Social Network Analysis area to measure ascribes and define frameworks. Consequently, it is imperative, to sum up, gather, break down, and group the research about this area. The reason for this research is to introduce an extensive deliberate literature review for the aggregation of metrics and frameworks used in semantic social network analysis SSNA