Case Study 1: Introduction, Human Object Detection, Object Tracking, Lightweight Human Detection.
Case Study 2: Introduction, Data-Driven Intelligent Transportation Systems, Mission-Critical Computing Requirements of Smart Transportation Applications, Fog Computing for Smart Transportation Applications, Case Study 3: Intelligent Traffic Lights Management (ITLM) System, Testing Perspectives.
TEXTBOOKS
1. Fog and Edge Computing, Rajkumar Buyya, Satish Narayana Srirama, Wiley Publications, 2019.
2. Fog computing in the Internet of Things: Springer publications, 2018 REFERENCES
1. Research papers from IEEE, ACM, Springer and Elsevier)
108
SUBJECT CODE SUBJECT TITLE C/OE/TE/SE CREDITS L T P C
CSE 427 Parallel Algorithms TE 3 0 0 3
UNIT I
Sequential model need of alternative model, parallel computational 8 models such as PRAM, LMCC, Hypercube, Cube Connected Cycle, Butterfly, Perfect Shuffle Computers, Tree model, Pyramid model, Fully Connected model, PRAM-CREW, EREW models, simulation of one model from another one.
UNIT II
Performance Measures of Parallel Algorithms, speed-up and 8 efficiency of PA, Cost- optimality, an example of illustrate Cost- optimal algorithms- such as summation, Min/Max on various models.
UNIT III
Parallel Sorting Networks, Parallel Merging Algorithms on on 8 CREW/EREW/MCC, Parallel Sorting Networks CREW/EREW/MCC/, linear array.
UNIT IV
Parallel Searching Algorithm, Kth element, Kth element in X+Y on 8 PRAM, Parallel Matrix Transportation and Multiplication Algorithm on PRAM, MCC, Vector-Matrix Multiplication, Solution of Linear Equation, Root finding.
UNIT V
Graph Algorithms - Connected Graphs, search and traversal, 8 Combinatorial Algorithms-Permutation, Combinations, Derangements.
TEXTBOOKS
1. M.J. Quinn, “Designing Efficient Algorithms for Parallel Computer”, Mc Graw Hill.
2. S.G. Akl, “Design and Analysis of Parallel Algorithms” 3. S.G. Akl,” Parallel Sorting Algorithm” by Academic Press
109
SUBJECT CODE SUBJECT TITLE C/OE/TE/SE CREDITS L T P C
CSE 428 Web Services TE 3 0 0 3
UNIT I
Introduction to Web Services-Web services and Service oriented architecture-History of webservices-HTTP requests and responses-HTTP as an API-Introduction to Representational State Transfer (REST) Web service- Servlets for RESTful web services-RESTful Service as an Http Servlet- A services-RESTful Web Service as a JAX-RS Resource-A RESTful Web Service as Restlet Resources
UNIT II
RESTful Web Services: The client side- A Perl Client Against a Java RESTful Web Service-A Client Service-Against the Service-Amazon E-Commerce Service-Service-A Standalone JService-AX-B Example-RESTful Clients and WADL Documents-The JAX-RS Client API-JSON for JavaScript Clients
UNIT III
A SOAP-Based Web Service-The Rand Service in Two Files-Clients Against the Rand Service-The WSDL Service Contract in Detail-SOAP-Based Clients Against Amazon’s E-Commerce Service
UNIT IV
Introduction to web services security-Wire-level security-Symmetric and Asymmetric encryption-HTTPS handshake-A Very Lightweight HTTPS Server and Client-HTTPS in a Production-Grade Web Server-Container-Managed Security-WS-Security
UNIT V
Web services and Java application servers-Web container The message oriented middleware-The enterprise Java bean container- The naming and lookup services-The security provider-The client container The database system-Glass fish basics-Servelet-based web services under glass fish.
TEXTBOOKS
1. Martin Kalin, Java Web Services: Up and Running, O’Reilly publishers, Second edition, 2013.
REFERENCES
1. Java Web Service Architecture, James McGovern, Sameer Tyagi etal., Elsevier 2. Building Web Services with Java, 2 Edition, S. Graham and others, Pearson Edn.
3. Java Web Services, D.A. Chappell & T. Jewell, O’Reilly, SPD.
4. Web Services, G. Alonso, F. Casati and others, Springer. Outcomes.
110
SUBJECT CODE SUBJECT TITLE C/OE/TE/SE CREDITS L T P C
CSE 429 Advances in Data Mining TE 3 0 0 3
UNIT I
What is Data Mining, Compiling need of Data Mining, Business Data Mining, Data Mining Tools. Data Mining Process, CRISP-DM, Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation, Deployment. SEMMA, Steps in SEMMA Process, Comparison of CRISP & SEMMA, Handling Data.
UNIT II
Association Rules in Knowledge Discovery, Market-Basket Analysis, Mining Frequent Patterns, Associations, and Correlations, Apriori Algorithm, Pattern-Growth Approach for Mining Frequent Itemsets, Mining Frequent Itemsets using Vertical Data Format, Mining Closed and Max Patterns. Pattern Mining in Multilevel, Multidimensional Space, Constraint-Based Frequent Pattern Mining, Mining High-Dimensional Data and Colossal Patterns, Mining Compressed or Approximate Patterns.
UNIT III
Classification: Basic Concepts, Decision Tree Induction, Bayes Classification Methods:
Bayes’ Theorem, Na¨ıve Bayesian Classification, Rule-Based Classification. Model Evaluation and Selection, Techniques to Improve Classification Accuracy: Bagging, Boosting and AdaBoost, Random Forests, Improving Classification Accuracy of Class-Imbalanced Data. Other Classification Methods: Genetic Algorithms, Rough Set Approach, Fuzzy Set Approaches.
UNIT IV
Cluster Analysis, Partitioning Methods: k-Means: A Centroid-Based Technique, k-Medoids:
A Representative Object-Based Technique. Hierarchical Methods: Agglomerative versus Divisive Hierarchical Clustering, Distance Measures in Algorithmic Methods, BIRCH:
Multiphase Hierarchical Clustering Using Clustering, Feature Trees, Chameleon:
Multiphase Hierarchical Clustering Using Dynamic Modelling, Probabilistic Hierarchical Clustering. Density-Based Methods, Grid-Based Methods.
UNIT V
Outliers and Outlier Analysis, Outlier Detection Methods: Supervised, Semi-Supervised, and Unsupervised Methods, Statistical Methods, Proximity-Based Methods, and Clustering-Based Methods, Mining Contextual and Collective Outliers, Outlier Detection in High-Dimensional Data. Mining Complex Data Types, Data Mining Applications, Social Impacts of Data Mining.
TEXTBOOKS
111
1. Data Mining Concepts and Techniques, Third Edition, by Jiawei Han, Micheline Kamber, and Jian Pei.
2. Olson DL, Delen D. Advanced data mining techniques. Springer Science & Business Media.
REFERENCES
1. Aggarwal CC. Data mining: the textbook. Springer. William 2. Machine Learning, 2nd edition, by Ethem Alpaydin.