Note 10 Class Computer Applications 165 Pdf
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Touchpad Computer Applications Class 10
Author: Dr Sanjay Jain
language: en
Publisher: Orange Education Pvt Ltd
Release Date: 2021-01-11
The chapters of this book have been selected and designed as per the CBSE curriculum of Computer Applications (Code 165). KEY FEATURES ● National Education Policy 2020 ● Do you Know?: This section contains a fact about the topic. ● Lab Assignment 'N Activity: This section contains an activity to apply the concepts learnt. ● PART A & PART B: This section contains questions to assess the intellectual and comprehensive writing skills. ● CBSE Sample Question Paper: This section contains sample question paper. ● Digital Solutions DESCRIPTION The main features of this book are as follows: ● The language of the book is simple and easy to understand. ● The book focuses on Free and Open-Source Software (Foss) with highlights of MS Office. ● Notes are given for add-on knowledge. ● Students are provided with fun facts about the topic. ● Lab Activities are added in between the chapters to develop practical skills. ● The applications of IT Tools are discussed with real life scenarios. ● The contents will help to create opportunity for better job prospects with respect to IT fields. WHAT WILL YOU LEARN You will learn about: ● Networking ● HTML ● CSS ● Cyberethics ● Scratch ● Python WHO THIS BOOK IS FOR Grade - 10 TABLE OF CONTENTS 1. Unit-1: Networking (a) Chapter-1 Networking 2. Unit-2: HTML (a) Chapter-2 Introduction to HTML (b) Chapter-3 More About HTML (c) Chapter-4 Cascading Style Sheets 3. Unit-3: Cyber Ethics (a) Chapter-5 Cyber Ethics 4. Unit-4: Scratch or Python (a) Chapter-6 Scratch (b) Chapter-7 Programming in Python (c) Chapter-8 Decision Making in Python (d) Chapter-9 Looping in Python 5. Practical Work 6. Viva Voce Questions 7. Projects 8. Glossary 9. CBSE Sample Question Paper
The State of the Art in Intrusion Prevention and Detection
The State of the Art in Intrusion Prevention and Detection analyzes the latest trends and issues surrounding intrusion detection systems in computer networks, especially in communications networks. Its broad scope of coverage includes wired, wireless, and mobile networks; next-generation converged networks; and intrusion in social networks. Presenting cutting-edge research, the book presents novel schemes for intrusion detection and prevention. It discusses tracing back mobile attackers, secure routing with intrusion prevention, anomaly detection, and AI-based techniques. It also includes information on physical intrusion in wired and wireless networks and agent-based intrusion surveillance, detection, and prevention. The book contains 19 chapters written by experts from 12 different countries that provide a truly global perspective. The text begins by examining traffic analysis and management for intrusion detection systems. It explores honeypots, honeynets, network traffic analysis, and the basics of outlier detection. It talks about different kinds of IDSs for different infrastructures and considers new and emerging technologies such as smart grids, cyber physical systems, cloud computing, and hardware techniques for high performance intrusion detection. The book covers artificial intelligence-related intrusion detection techniques and explores intrusion tackling mechanisms for various wireless systems and networks, including wireless sensor networks, WiFi, and wireless automation systems. Containing some chapters written in a tutorial style, this book is an ideal reference for graduate students, professionals, and researchers working in the field of computer and network security.
Frontiers in Massive Data Analysis
Author: National Research Council
language: en
Publisher: National Academies Press
Release Date: 2013-09-03
Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.