A Dictionary of the Economic Products of India: DICTIONARY OF THE ECONOMIC PRODUCTS OF INDIA pages 1 to 174

With the advancement of blockchain technology, and the proliferation of Internet of things (IoT)‐driven devices, the blockchain‐IoT applications is changing the perception and working infr...

A Distributed framework for detecting DDoS attacks in smart contract‐based Blockchain‐IoT Systems by leveraging Fog computing

Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks. Currently, deep learning (DL) and machine learning (ML) are being applied in different domains, especially information security, for developing effective ID systems. These ID systems are capable of detecting malicious threats automatically and on time. However, malicious threats are occurring and changing continuously, so the network requires a very advanced security solution. Thus, creating an effective and smart ID system is a massive research problem. Various ID datasets are publicly available for ID research. Due to the complex nature of malicious attacks with a constantly changing attack detection mechanism, publicly existing ID datasets must be modified systematically on a regular basis. So, in this paper, a convolutional recurrent neural network (CRNN) is used to create a DL-based hybrid ID framework that predicts and classifies malicious cyberattacks in the network. In the HCRNNIDS, the convolutional neural network (CNN) performs convolution to capture local features, and the recurrent neural network (RNN) captures temporal features to improve the ID system’s performance and prediction. To assess the efficacy of the hybrid convolutional recurrent neural network intrusion detection system (HCRNNIDS), experiments were done on publicly available ID data, specifically the modern and realistic CSE-CIC-DS2018 data. The simulation outcomes prove that the proposed HCRNNIDS substantially outperforms current ID methodologies, attaining a high malicious attack detection rate accuracy of up to 97.75% for CSE-CIC-IDS2018 data with 10-fold cross-validation.

https://www.mdpi.com/2227-9717/9/5/834/pdf

HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System

Big data is the concept of enormous amounts of data being generated daily in different fields due to the increased use of technology and internet sources. Despite the various advancements and the hopes of better understanding, big data management and analysis remain a challenge, calling for more rigorous and detailed research, as well as the identifications of methods and ways in which big data could be tackled and put to good use. The existing research lacks in discussing and evaluating the pertinent tools and technologies to analyze big data in an efficient manner which calls for a comprehensive and holistic analysis of the published articles to summarize the concept of big data and see field-specific applications. To address this gap and keep a recent focus, research articles published in last decade, belonging to top-tier and high-impact journals, were retrieved using the search engines of Google Scholar, Scopus, and Web of Science that were narrowed down to a set of 139 relevant research articles. Different analyses were conducted on the retrieved papers including bibliometric analysis, keywords analysis, big data search trends, and authors’ names, countries, and affiliated institutes contributing the most to the field of big data. The comparative analyses show that, conceptually, big data lies at the intersection of the storage, statistics, technology, and research fields and emerged as an amalgam of these four fields with interlinked aspects such as data hosting and computing, data management, data refining, data patterns, and machine learning. The results further show that major characteristics of big data can be summarized using the seven Vs, which include variety, volume, variability, value, visualization, veracity, and velocity. Furthermore, the existing methods for big data analysis, their shortcomings, and the possible directions were also explored that could be taken for harnessing technology to ensure data analysis tools could be upgraded to be fast and efficient. The major challenges in handling big data include efficient storage, retrieval, analysis, and visualization of the large heterogeneous data, which can be tackled through authentication such as Kerberos and encrypted files, logging of attacks, secure communication through Secure Sockets Layer (SSL) and Transport Layer Security (TLS), data imputation, building learning models, dividing computations into sub-tasks, checkpoint applications for recursive tasks, and using Solid State Drives (SDD) and Phase Change Material (PCM) for storage. In terms of frameworks for big data management, two frameworks exist including Hadoop and Apache Spark, which must be used simultaneously to capture the holistic essence of the data and make the analyses meaningful, swift, and speedy. Further field-specific applications of big data in two promising and integrated fields, i.e., smart real estate and disaster management, were investigated, and a framework for field-specific applications, as well as a merger of the two areas through big data, was highlighted. The proposed frameworks show that big data can tackle the ever-present issues of customer regrets related to poor quality of information or lack of information in smart real estate to increase the customer satisfaction using an intermediate organization that can process and keep a check on the data being provided to the customers by the sellers and real estate managers. Similarly, for disaster and its risk management, data from social media, drones, multimedia, and search engines can be used to tackle natural disasters such as floods, bushfires, and earthquakes, as well as plan emergency responses. In addition, a merger framework for smart real estate and disaster risk management show that big data generated from the smart real estate in the form of occupant data, facilities management, and building integration and maintenance can be shared with the disaster risk management and emergency response teams to help prevent, prepare, respond to, or recover from the disasters.

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Part One. Multivariate distributions. Preliminary data analysis. Part Two: Finding new underlying variables. Principal component analysis. Factor analysis. Part Three: Procedures based on the multivariate normal distribution. The multivariate normal distribution. Procedures based on normal distribution theory. The multivariate analysis of variance. The multivariate analysis of covariance and related topics. Part Four: Multi-dimensional scaling and cluster analysis. Multi-dimensional scaling. Cluster analysis.

Introduction to multivariate analysis, by C. Chatfield and A. J. Collins. Pp 246. £13 hardcover, £7·50 paperback. 1980. ISBN 0-412-16030-7/4 (Chapman and Hall)

Serious concerns regarding vulnerability and security have been raised as a result of the constant growth of computer networks. Intrusion detection systems (IDS) have been adopted by netwo...

An integrated intrusion detection system using correlation‐based attribute selection and artificial neural network

Traditional technique for determining the soil texture and other soil properties is performed in laboratory which is a time consuming task. In this paper, machine learning algorithms are deployed to classify the soil texture and its properties without any intervention of laboratory equipment using the satellite images recorded by Landsat 8. These images are used to extract the terrain properties of the region which is integrated with weather data for the specific region and the vegetation index which are the major factors affecting the soil condition. A major aim of this paper is to design a robust technique for extracting, transforming Landsat images to numerical data and pre-processing the data for classifying the soil property. Minimum Noise Fraction (MNF) is utilized to segregate and remove noise from the Landsat images for subsequent processing. A significant amount of noise is present in the raw data which affects the accuracy of the analysis. Terrain features are extracted after noise removal from the MNF transformed images and merged with the weather data, and vegetation index for a period of time and then classified using voting classifier of the ensemble modeling or analysis of the soil texture of the region. The voting is performed by integrating the results of logistic regression, support vector machine and decision tree. With this study, the consolidated dependence of the soil texture on the environmental factors is analyzed and a cross validation accuracy of 94.44% is obtained.

https://ijnaa.semnan.ac.ir/article_4750_7fff24f568b8a9041ad50822ef55fd22.pdf

Terrain Mapping of LandSat8 Images using MNF and Classifying Soil Properties using Ensemble Modelling

Understanding response of plants to drought stress is critical for developing drought tolerant genotypes with stable performance under water limiting conditions. A backcross inbred population derived in the background of a popular variety Swarna, was evaluated for yield and yield attributing traits under reproductive stage and shoot morphological traits at seedling stage under drought stress over a period of two years during 2011–2013. Selection intensity varied across seasons and years with varied level of moisture stress. Traits such as flowering (DTF), plant height (PH), spikelet fertility (SF), biomass (BM), harvest index (HI) and grain yield (GY) are drastically affected by drought across seasons. Yield reduction varied from 48.5% to 92.4% over control trials. Chlorophyll content (CC) and canopy temperature (CT) recorded higher under drought rather than control. Yield was highly associated with DTF (−0.29* to −0.76**), SF (0.32** to 0.77**), HI (0.82** to 0.97**) and CT (−0.38**). Heritability is found to be similar for grain yield under both conditions. Genotypes displaying higher SF and HI, lower CT, reduction in flowering delay and low DSI are able to produce better grain yield under drought. High yielding genotypes at reproductive stage stress also showed superior performance under stress at seedling stage. Hence, selection intensity for high yield potential under favorable conditions, drought tolerance to varied level of stress (across seasons) and screening under target population environment had the advantage of identifying stable high yielding genotypes with wider adaption suitable for drought-prone regions, which can significantly benefits the poor farmers relying on rainfed lowland rice.

Grain yield responses to varied level of moisture stress at reproductive stage in an interspecific population derived from Swarna/O. glaberrima introgression line

The field of image computing incorporates the crucial subject of character recognition which is gaining significant importance and extensive research in the current wake of digital revolution The goal is to find an effective software tool capable of accurately identifying the characters The variations in a single alphabet itself and the plethora of handwriting varieties make this a challenging task The proposed solution comprises of a series of steps for the purpose of classification System training and testing incorporates hundred instances of handwritten digit images from MNIST Dataset Preprocessing of the image enhances data images prior to computational processing Input image are converted into gray scale and finally into binary This is followed by morphological operations and the images are then converted into Comma Separated Files to be able to be used as Training and Testing Dataset in WEKA Pattern recognition is done by Hoeffding Tree, Decision tree and Random forest methodologies to ultimately compare them on a set of benchmarks to find the most effective tool marked on a set of measures such as efficiency, effectiveness, time to perform the complete process of classification, etc On the basis of the key parameters which include classified instances of the digits, error rate and time taken for the classification, Hoeffding tree is found to be most effective in terms of time taken to build model, precision, recall and confusion matrix The future work requires inclusion of an extensive data set to declare the best among these approaches

Handwritten digit recognition using hoeffding tree, decision tree and random forests — A comparative approach

Home Automation Systems (HAS) have gained world-wide popularity these days. It guarantees security, surveillance and solace to the user and make their life more comfortable. In developed countries, many households have smart home automation systems which detect their surroundings and act on the data to trigger home appliances such as fan, air conditioners etc. But these systems are not very popular in developing nations as compared to developed nations because of their pricing. Also, almost all of these systems are hard coded so they just make some decisions based on iterative conditions. The system has neither user defined nor conditional control. There comes the need of Voice Controlled Home Automation Systems (VCHAS). The proposed ZigBee based home automation system and Wi-Fi network are unified through a mutual home gateway. The combinatory offers network interoperability, a simple and supple user interface, and distant access to the system.

K. Lavanya

Papers

An integrated intrusion detection system using correlation‐based attribute selection and artificial neural network

Terrain Mapping of LandSat8 Images using MNF and Classifying Soil Properties using Ensemble Modelling

Grain yield responses to varied level of moisture stress at reproductive stage in an interspecific population derived from Swarna/O. glaberrima introgression line

Handwritten digit recognition using hoeffding tree, decision tree and random forests — A comparative approach

Intelligent Home Automation System using BitVoicer