Enhancement of Toll Plaza System With Smart Features

This chapter aims to solve the problem of heavy traffic caused due to a long queue near the toll plaza. The authors design the website with the motive that it will save the maximum time of the public, reducing the problem of heavy traffic. Moreover, the website maintains the entire database containing the details of the staff, pass, receipts, vehicle details, etc., which will reduce any problem in the future. Since they are also aware of the fact that in many villages in India, there are not even proper toll booths to pay taxes, and people are doing it manually, which can result in data loss and even is time-consuming. So, keeping this mind, they aim to design the website that is simple to use such that every people working in toll booth can get habituated to it easily. They also aim to make this website fully secure such that data can be protected and citizens are comfortable providing their details to create their pass and generate receipts. The main feature is that users can also generate receipt for themselves from anywhere through website to avoid waste of time at toll.

IoT in the Education Sector

Smart education derived from information communication technologies (ICT) has attracted various academicians towards it. The growth of multiple sensor devices and wireless networks has brought drastic changes in IoT in the education sector. Applications of IoT in the education sector can improve academicians' and learners' considerable skills. Therefore, this chapter analyses various applications, advantages, and challenges of IoT in the education sector. The multiple applications of IoT in the education sector are identified in terms of smart classroom management, student tracking and monitoring, campus energy management, and intelligent learning. IoT in education's significant advantages are an innovative teaching and learning process, cost reduction, and smart infrastructure development. Various challenges in developing IoT-based applications identify as designing a secure learning environment, efficient resource tracking, efficient access to information, and intellectual plan development.

Review on Sparse Matrix Storage Formats With Space Complexity Analysis

Sparse matrix-vector multiplication (SpMV) is a challenging computational kernel in linear algebra applications, like data mining, image processing, and machine learning. The performance of this kernel is greatly dependent on the size of the input matrix and the underlying hardware features. Various sparse matrix storage formats referred to commonly as sparse formats have been proposed in the literature to reduce the size of the matrix. In modern multi-core and many-core architectures, the performance of the kernel is mainly dependent on memory wall and power wall problem. Normally review on sparse formats is done with specific architecture or with specific application. This chapter presents a comparative study on various sparse formats in cross platform architecture like CPU, graphics processor unit (GPU), and single instruction multiple data stream (SIMD) registers. Space complexity analysis of various formats with its representation is discussed. Finally, the merits and demerits of each format have been summarized into a table.

Hyperspectral Image Classification Through Machine Learning and Deep Learning Techniques

Dimensionality reduction for hyperspectral imagery plays a major role in different scientific and technical applications. It enables the identification of multiple urban-related features on the surface of the earth, such as building, highway (road), and other natural and man-made structures. Since manual road detection and satellite imagery extraction is time-consuming and costly, data time and cost-effective solution with limited user interaction will emerge with road and building extraction techniques. Therefore, the need to focus on a deep survey for improving ML techniques for dimensionality reduction (DR) and automated building and road extraction using hyperspectral imagery. The main purpose of this chapter is to identify the state-of-the-art and trends of hyperspectral imaging theories, methodologies, techniques, and applications for dimensional reduction. A different type of ML technique is included such as SVM, ANN, etc. These algorithms can handle high dimensionality and classification data.

Security and Privacy for Electronic Healthcare Records Using AI in Blockchain

In yesteryears, the healthcare data related to each patient was limited. It was stored and controlled by the hospital authorities and was seldom regulated. With the increase in awareness and technology, the amount of medical data per person has increased exponentially. All this data is essential for the correct diagnosis of the patient. The patients also want access to their data to seek medical advice from different doctors. This raises several challenges like security, privacy, data regulation, etc. As health-related data are privacy-sensitive, the increase in data stored increases the risk of data exposure. Data availability and privacy are essential in healthcare. The availability of correct information is critical for the treatment of the patient. Information not easily accessed by the patients also complicates seeking medical advice from different hospitals. However, if data is easily accessible to everyone, it makes privacy and security difficult. Blockchains to store and secure data will not only ensure data privacy but will also provide a common method of data regulation.

A Trustworthy Convolutional Neural Network-Based Malware Variant Detector in Python

Malware attacks are broadly disguised as useful applications. Many android apps, downloaded to perform crucial tasks or play games (take one's pick), seem to do completely different tasks, which are potentially harmful and invasive in nature. This could include sending text messages to random users, exporting the phone's contacts, etc. There exist some algorithms in place that can detect these malwares, but so far, it has been observed that many of these algorithms suffer from false negatives, which grossly reduced the effectiveness of said algorithms. The aim of this chapter is to introduce a flexible method to detect if a certain application is malware or not. The working can be loosely defined as the source of a set of applications is detected and the list of permissions is studied. The set of relevant and highly close applications is selected, and from the most relevant category, the permissions are checked for overlap to see if it can be stated as a possible anomalous application.

Detecting DDoS Attack

The major network security problems faced by many internet users is the DDoS (distributed denial of service) attack. This attack makes the service inaccessible by exhausting the network and resources with high repudiation and economic loss. It denies the network services to the potential users. To detect this DDoS attack accurately in the network, random forest classifier which is a machine learning based classifier is used. The experimental results are compared with naïve Bayes classifier and KNN classifier showing that random forest produces high accuracy results in classification. Application of machine learning, detecting DDoS attacks is modeled based on the supervised learning algorithm to produce best outcome with high accuracy of training algorithm on network dataset.

Wearable Technology as a Source of Data-Generation Tool for Artificial Intelligence

The world is fascinated to see how technology evolves each passing day. All too soon, there's an emerging technology that is trending around us, and it is no other technology than smart wearable technology. Less attention is paid to the data that our bodies are radiating and communicating to us, but with the timely arrival of wearable sensors, we now have numerous devices that can be tracking and collecting the data that our bodies are radiating. Apart from numerous benefits that we derive from the functions provided by wearable technology such as monitoring of our fitness levels, etc., one other critical importance of wearable technology is helping the advancement of artificial intelligence (AI) and machine learning (ML). Machine learning thrives on the availability of massive data and wearable technology which forms part of the internet of things (IoT) generates megabytes of data every single day. The data generated by these wearable devices are used as a dataset for the training and learning of machine learning models. Through the analysis of the outcome of these machine learning models, scientific conclusions are made.

A Study and Analysis of Trust Management System in Cloud Technologies

Cloud computing is a technology that has enabled individual users and organizations alike to implement such functionality. Currently, a large percentage of the data being generated is stored on clouds, and the number of organizations opting for cloud-based technologies is continuously on the rise. With such growing numbers accessing and utilizing cloud resources, data security has become a significant cause of concern. Traditional methods of cloud computing are becoming obsolete and ineffective with each technological breakthrough, and data is thus highly subjected to getting corrupted or hacked. This chapter provides a survey on various trust management techniques used in cloud technology to protect the data with multiple security features.

A Proficient Hybrid Framework for Image Retrieval

In this work, an image retrieval system based on three main factors is constructed. The proposed system at first chooses relevant pictures from an enormous information base utilizing colour moment data. Accordingly, canny edge recognition and local binary pattern and strategies are utilized to remove the texture plus edge separately, as of the uncertainty and resultant pictures of the underlying phase of the system. Afterward, the chi-square distance between the red-green and the blue colour channels of the query and the main image are calculated. Then these two (the LBP pattern and the edge feature extracted from the canny edge detection and by chi-square method) data about these two highlights compared to the uncertainty and chosen pictures are determined and consolidated, are then arranged and the nearest ‘n' images are presented. Two datasets, Wang and the Corel databases, are used in this work. The results shown herein are obtained using the Wang dataset. The Wang dataset contains 1,000 images and Corel contains 10,000 images.