LAKIREDDY BALI REDDY COLLEGE OF ENGINEERING
LAKIREDDY BALI REDDY COLLEGE OF ENGINEERINGDEPARTMENT OF COMPUTER SCIENCE AND ENGINEERINGLESSON PLANSubject : SOFTWARE PROJECT MANAGEMENTCourse : IV/ IV B.Tech (VII Semester)Faculty : Dr. B.Thirumala Rao41Week 2Week 3Week 4Week 5Week 6Week 7Week 8Week 3031323334353637383940Week 1S.NOBranch & Section : CSE-ASemester-IAcademic year : 2013-2014DATETOPIC TO BE 2-Aug-13Conventional software managementConventional software managementThe waterfall modelConventional software management performanceConventional software management performanceEvolution of software economicsEvolution of software economicsSoftware economicsSoftware economicsPragmatic software cost estimationImproving software economicsImproving software economicsReducing software product sizeImproving software processesImproving team effectivenessImproving automationImproving automationAchieving required quality ,peer inspectionsThe old way and the new way: principles sof modernmanagementTransitioning to an iterative processReview of 1st unitLife cycle phases: engineering and production stagesLife cycle phases: engineering and production stagesInception , elaborationInception , elaborationConstruction and transition phasesConstruction and transition phasesArtifacts of the process: the artifact setsManagement artifactsEngineering artifactsEngineering artifactsPragmatic artifactsModel based software architectureModel based software architectureA management perspective and technical perspectiveA management perspective and technical perspectiveA management perspective and technical perspectivendReview of 2 unitReview of 2nd unitHOURS TEACH. METHOD/ AID1111111111111111111111111111111111111111Black BoardBlack BoardLCDLCDLCDBlack BoardBlack BoardBlack BoardBlack BoardLCDBlack BoardLCDBlack BoardLCDBlack BoardLCDBlack BoardLCDLCDLCDBlack BoardLCDBlack BoardLCDBlack BoardLCDBlack BoardLCDLCDLCDBlack BoardLCDBlack BoardLCDBlack BoardLCDBlack BoardLCDLCDBlack BoardII- Mid Examinations (10-Oct-13 to 12-Oct-13)8-Aug-13 Workflows of the process1LCD
Week 10Week 11Week 12Week 13Week 14Week 15Week 1324-Sep-1325-Sep-1326-Sep-1327-Sep-13Workflows of the processSoftware process workflowsSoftware process workflowsIteration workflowsCheckpoints of the process: major milestonesMinor mile stonesPeriodic status assessmentsIterative process planning: work breakdownstructuresguidelines, cost and schedule estimatingPanningIteration planning processIteration planning processPragmatic planningProject organizations and responsibilities:Line of business organizationsProject organizations, evolution of organizationsProcess automation:automation building blocksThe project environmentProject control and process instrumentation: thesevencorecoremetricsthesevenmetricsManagement indicatorsManagement indicatorsQuality indicators, life cycle expectationsPragmatic software metrics,metrics automationTailoring the process: Process DiscriminantsFuture software project management: modernprojectprofiles software economicsNextgenerationModern process transitionsModern process transitionsCCPDS-R Case StudyCCPDS-R Case StudyCCPDS-R Case Study1111111Black BoardLCDBlack BoardLCDBlack BoardLCDBlack Board11111111111111111111111LCDBlack BoardLCDLCDLCDLCDLCDBlack BoardLCDLCDBlack BoardLCDLCDLCDLCDBlack BoardLCDBlack BoardLCDLCDLCDLCDLCD111111LCDLCDLCDLCDLCDLCDWeek 18Week 17ADDITIONAL TOPICS BEYOND -138-Oct-139-Oct-13Excel/Outlook/ based Project Management SoftwareMicrosoft Project 2003SPM tools in open sourceOpen Workbench toolRECAPOLD QUESTION PAPAERS REIVEWII- Mid Examinations (10-Oct-13 to 12-Oct-13)TEXT BOOK :1. Software Project Management, Walker Royce: Pearson Education, 2009.REFERENCES :1. Software Project Management, Bob Hughes and Mike Cotterell: Tata McGraw-Hill Edition.2. Software Project Management, Joel Henry, Pearson Education.3. Software Project Management in practice, Pankaj Jalote, Pearson Education.2005.Prepared By :Dr. B.Thirumala Rao, Professor, CSEDate: 10-06-2013Head of the Department
LAKIREDDY BALI REDDY COLLEGE OF ENGINEERINGDEPARTMENT OF COMPUTER SCIENCE AND ENGINEERINGLESSON PLANSubject : SOFTWARE PROJECT MANAGEMENTCourse : IV/ IV B.Tech (VII Semester)Faculty : Dr. B.Thirumala Rao41Week 2Week 3Week 4Week 5Week 6Week 7Week 8Week 3031323334353637383940Week 1S.NOBranch & Section : CSE-BSemester-IAcademic year : 2013-2014DATETOPIC TO BE 2-Aug-13Conventional software managementConventional software managementThe waterfall modelConventional software management performanceConventional software management performanceEvolution of software economicsEvolution of software economicsSoftware economicsSoftware economicsPragmatic software cost estimationImproving software economicsImproving software economicsReducing software product sizeImproving software processesImproving team effectivenessImproving automationImproving automationAchieving required quality ,peer inspectionsThe old way and the new way: principles sof modernmanagementTransitioning to an iterative processReview of 1st unitLife cycle phases: engineering and production stagesLife cycle phases: engineering and production stagesInception , elaborationInception , elaborationConstruction and transition phasesConstruction and transition phasesArtifacts of the process: the artifact setsManagement artifactsEngineering artifactsEngineering artifactsPragmatic artifactsModel based software architectureModel based software architectureA management perspective and technical perspectiveA management perspective and technical perspectiveA management perspective and technical perspectivendReview of 2 unitReview of 2nd unitHOURS TEACH. METHOD/ AID1111111111111111111111111111111111111111Black BoardBlack BoardLCDLCDLCDBlack BoardBlack BoardBlack BoardBlack BoardLCDBlack BoardLCDBlack BoardLCDBlack BoardLCDBlack BoardLCDLCDLCDBlack BoardLCDBlack BoardLCDBlack BoardLCDBlack BoardLCDLCDLCDBlack BoardLCDBlack BoardLCDBlack BoardLCDBlack BoardLCDLCDBlack BoardII- Mid Examinations (10-Oct-13 to 12-Oct-13)8-Aug-13 Workflows of the process1LCD
Week 10Week 11Week 12Week 13Week 14Week 15Week 1324-Sep-1325-Sep-1326-Sep-1327-Sep-13Workflows of the processSoftware process workflowsSoftware process workflowsIteration workflowsCheckpoints of the process: major milestonesMinor mile stonesPeriodic status assessmentsIterative process planning: work breakdownstructuresguidelines, cost and schedule estimatingPanningIteration planning processIteration planning processPragmatic planningProject organizations and responsibilities:Line of business organizationsProject organizations, evolution of organizationsProcess automation:automation building blocksThe project environmentProject control and process instrumentation: thesevencorecoremetricsthesevenmetricsManagement indicatorsManagement indicatorsQuality indicators, life cycle expectationsPragmatic software metrics,metrics automationTailoring the process: Process DiscriminantsFuture software project management: modernprojectprofiles software economicsNextgenerationModern process transitionsModern process transitionsCCPDS-R Case StudyCCPDS-R Case StudyCCPDS-R Case Study1111111Black BoardLCDBlack BoardLCDBlack BoardLCDBlack Board11111111111111111111111LCDBlack BoardLCDLCDLCDLCDLCDBlack BoardLCDLCDBlack BoardLCDLCDLCDLCDBlack BoardLCDBlack BoardLCDLCDLCDLCDLCD111111LCDLCDLCDLCDLCDLCDWeek 18Week 17ADDITIONAL TOPICS BEYOND -138-Oct-139-Oct-13Excel/Outlook/ based Project Management SoftwareMicrosoft Project 2003SPM tools in open sourceOpen Workbench toolRECAPOLD QUESTION PAPAERS REIVEWII- Mid Examinations (10-Oct-13 to 12-Oct-13)TEXT BOOK :1. Software Project Management, Walker Royce: Pearson Education, 2009.REFERENCES :1. Software Project Management, Bob Hughes and Mike Cotterell: Tata McGraw-Hill Edition.2. Software Project Management, Joel Henry, Pearson Education.3. Software Project Management in practice, Pankaj Jalote, Pearson Education.2005.Prepared By :Dr. B.Thirumala Rao, Professor, CSEDate: 10-06-2013Head of the Department
LESSON PLANDate:10.06.2013To 09.10.2013Sub Name :ARTIFICIAL INTELLIGENCEBranch: B.TechCSESemester& Section:VII –A SectionUNIT - IIntroduction: Overview of Artificial intelligence- Problems of AI, AI technique, Tic - Tac Toe problem.Intelligent Agents: Agents & environment, nature of environment, structure of agents,goal based agents, utility based agents, learning agents. Problem Solving: Problems, ProblemSpace &search: Defining the problem as state space search, production system, problemcharacteristics, and issues in the design of search programs.UNIT - IISearch techniques: Solving problems by searching: problem solving agents, searching forsolutions; uniform search strategies: breadth first search, depth first search, depth limitedsearch, bi-directional search, comparing uniform search strategies. Heuristic search strategies:Greedy best-first search, A* search, memory bounded heuristic search, local search algorithms &optimization problems, Hill climbing search, simulated annealing search, local beam search, genetic algorithms, constraint satisfaction problems, local search for constraint satisfaction problems.UNIT - IIIKnowledge: Knowledge representation issues, representation & mapping, approaches toknowledge representation, issues in knowledge representation. Using predicate logic:Representingsimple fact in logic, representing instant & ISA relationship, computable functions&predicates, resolution, natural deduction.UNIT - IVRepresenting knowledge using rules:Procedural verses declarative knowledge, logic programming, forward verses backward reasoning,matching, control knowledge.UNIT - VReasoning:Probabilistic reasoning: Representing knowledge in an uncertain domain, the semantics of Bayesiannetworks, Dempster-Shafertheory, Fuzzy sets & fuzzy logics.
LESSON PLANDate:10.06.2013To 09.10.2013Sub Name :ARTIFICIAL INTELLIGENCEBranch: B.TechCSESemester& Section:VII –A SectionThe main objectives of this course are as follows:1. Different types of AI techniques and their implementation.2. Types of agents and activities of agents.3. Different problem solving techniques and problem characteristics.4. Various searching strategies and their implementations.5. Representation of knowledge using predicate logic.6. Representation of knowledge using prepositional logic.7. Knowledge rules using reasoning.8. Knowledge rules using matching.9. Reasoning using fuzzy sets and fuzzy logics.The main outcomes of this course are as follows:Upon the successful completion of this course students will be able to:1. What are AI techniques How to implement them and how they help in problem solving?2. What are various searching strategies, how to implement them?3. How to implement different algorithms in searching techniques?4. How to represent knowledge, what are the rules of it?5. How to represent knowledge using predicate knowledge?6. How to implement knowledge using propositional logic?7. How to represent knowledge using reasoning?8. How to represent knowledge using Fuzzy sets and Fuzzy logics?
LESSON PLANDate:10.06.2013To 09.10.2013Sub Name :ARTIFICIAL INTELLIGENCEBranch: B.TechCSESemester& Section:VII –A Section
Date:LESSON PLAN10.06.2013To 09.10.2013Sub Name :ARTIFICIAL INTELLIGENCEBranch: B.TechCSESemester& Section:SessionNo12345Topics to be covered891011DateOverview of Artificial intelligenceProblems of AI, AI techniqueTic - Tac - Toe problem.10.06.201311.06.201312.06.2013Agents & environment, nature ofenvironment, structure of agents13.06.2013goal based agents, utility basedagents, learning agents17.06.201367VII –A SectionProblemsProblem space and searchDefining the problem as statespace search, production 6.201318.06.201319.06.2013BBUNIT 32425262728293031Problem characteristics andissues in searching of programsProblem characteristics andissues in searching of programsSolving problem by searching24.06.201325.06.201326.06.2013Problem solving agents,searching 27.06.2013for solutions28.06.20131.07.2013Uninformed searching 5.07.2013Comparing searching techniques08.07.201309.07.2013Heuristic searching strategies10.07.201311.07.2013Greedy BFS12.07.201315.07.2013A*,memory bounded search16.07.2013Local search algorithms and17.07.2013optimization problemsLocal search algorithms and18.07.2013optimization problemsHill climbing BBBBBBUNIT 2
Date:LESSON PLAN10.06.2013To 09.10.2013Sub Name :ARTIFICIAL INTELLIGENCEBranch: B.TechCSESemester& Section:3233343536373839404142Simulated annealingLocal beam searchLocal beam searchGenetic algorithmsGenetic algorithmsCSPCSPLocal search for CSPLocal search for CSPRevisionRevisionVII –A 1.08.20132.08.2013BBBBBBBBBBBBBBI MID EXAMINATIONS (05-08-2013 TO 07-08-2013)43444546Knowledge representationissuesRepresentation and mapping47484950Approaches to representation5152Issues in representation5354Representing simple fact in logic5556Representation and mappingRepresenting simple Is a relationshipRepresenting simple Is a .08.201330.08.2013BBBBBBBBBBBB57Computable functions andpredicates5859Computable functions andpredicatesComputable functions NIT 3
Date:LESSON PLAN10.06.2013To 09.10.2013Sub Name :ARTIFICIAL INTELLIGENCEBranch: B.TechCSESemester& Section:60616263646566ResolutionNatural deductionNatural deductionProcedural vs declarativeknowledgeProcedural vs declarativeknowledgeLogic programming6768Logic programming69VII –A 8.09.2013BB19-09-2013BBBBBB70Forward and backward reasoning20-09-2013BB71727374757677Forward and backward reasoningMatchingMatchingControl KnowledgeProbablistic reasoningProbablistic reasoningRepresenting knowledge inuncertain domainSemantics of Bayesian networksSemantics of Bayesian networksDempster-shafer theoryDempster-shafer theoryFuzzy setsFuzzy setsFuzzy logicsFuzzy T BOOKArtificial Intelligence, Ritch& Knight, TMHUNIT 4UNIT4Unit 5
Date:LESSON PLAN10.06.2013To 09.10.2013Sub Name :ARTIFICIAL INTELLIGENCEBranch: B.TechCSESemester& Section:VII –A SectionREFERENCES1. Artificial Intelligence A Modern Approach, Stuart Russell & Peter Norvig Pearson2. Introduction to Artificial Intelligence & Expert Systems, Patterson, PHI3. Artificial Intelligence A new Synthesis, Neil J. Nilsson, Morgan Kaufman4. Artificial Intelligence, John. F. Lugar, Pearson Ed.5. Artificial Intelligence, Winston, Pearson Ed.Course Delivery:UNIT123WEEK 1 2 3 4 5 6 7 8 9 10 11Prepared bySignatureNameM.SreeBalaDesignation Asst.Professor/CSE12.06.2013Date41213514615 16Approved byHOD/CSE
LESSON PLANDate:10.06.2013To 09.10.2013Sub Name :ARTIFICIAL INTELLIGENCEBranch: B.TechCSESemester& Section:VII –B SectionUNIT - IIntroduction: Overview of Artificial intelligence- Problems of AI, AI technique, Tic - Tac Toe problem.Intelligent Agents: Agents & environment, nature of environment, structure of agents,goal based agents, utility based agents, learning agents. Problem Solving: Problems, ProblemSpace &search: Defining the problem as state space search, production system, problemcharacteristics, and issues in the design of search programs.UNIT - IISearch techniques: Solving problems by searching: problem solving agents, searching forsolutions; uniform search strategies: breadth first search, depth first search, depth limitedsearch, bi-directional search, comparing uniform search strategies. Heuristic search strategies:Greedy best-first search, A* search, memory bounded heuristic search, local search algorithms &optimization problems, Hill climbing search, simulated annealing search, local beam search, genetic algorithms, constraint satisfaction problems, local search for constraint satisfaction problems.UNIT - IIIKnowledge: Knowledge representation issues, representation & mapping, approaches toknowledge representation, issues in knowledge representation. Using predicate logic:Representingsimple fact in logic, representing instant & ISA relationship, computable functions&predicates, resolution, natural deduction.UNIT - IVRepresenting knowledge using rules:Procedural verses declarative knowledge, logic programming, forward verses backward reasoning,matching, control knowledge.UNIT - VReasoning:Probabilistic reasoning: Representing knowledge in an uncertain domain, the semantics of Bayesiannetworks, Dempster-Shafertheory, Fuzzy sets & fuzzy logics.
LESSON PLANDate:10.06.2013To 09.10.2013Sub Name :ARTIFICIAL INTELLIGENCEBranch: B.TechCSESemester& Section:VII –B SectionThe main objectives of this course are as follows:1. Different types of AI techniques and their implementation.2. Types of agents and activities of agents.3. Different problem solving techniques and problem characteristics.4. Various searching strategies and their implementations.5. Representation of knowledge using predicate logic.6. Representation of knowledge using prepositional logic.7. Knowledge rules using reasoning.8. Knowledge rules using matching.9. Reasoning using fuzzy sets and fuzzy logics.The main outcomes of this course are as follows:Upon the successful completion of this course students will be able to:1. What are AI techniques How to implement them and how they help in problem solving?2. What are various searching strategies, how to implement them?3. How to implement different algorithms in searching techniques?4. How to represent knowledge, what are the rules of it?5. How to represent knowledge using predicate knowledge?6. How to implement knowledge using propositional logic?7. How to represent knowledge using reasoning?8. How to represent knowledge using Fuzzy sets and Fuzzy logics?
LESSON PLANDate:10.06.2013To 09.10.2013Sub Name :ARTIFICIAL INTELLIGENCEBranch: B.TechCSESemester& Sec
S.NO DATE TOPIC TO BE COVERED HOURS TEACH. METHOD/ AID 1 10-Jun-13 Conventional software management 1 Black Board 2 11-Jun-13 Conventional software management 1 Black Board 3 12-Jun-13 The waterfall model 1 LCD 4 13-Jun-13 Conventional software management performance 1 LCD 5 14-Jun-13 Conventional software management performance 1 LCD 6 17-Jun-13 Evolution of software economics 1 Black Board
1 Dr. Prathap C Reddy - AAGPP9786N 2 Ms.Sucharitha Reddy AASPS4292G 3 Ms. Preetha Reddy AAEPR5656F 4 Ms. Suneeta Reddy AAEPR46O2H 5 Ms. Shobana Kamineni AAIPK7589G 6 Ms. Sangita Reddy AASPS6786K 7 Mr. P. Vijayakumar Reddy ACKPV5876B 8 Mr. P. Dwarkanath Reddy AFDPD98O6R 9 Mr. Anil Kamineni ADOPK87O1D
VIGNANA BHARATHI INSTITUTE OF TECHNOLOGY HYDERABAD Telangana South-Central/SCRO 35 IC201811272 LAKIREDDY BALI REDDY COLLEGE OF ENGINEERING MYLAVARAM Andhra Pradesh South-Central/SCRO 36 IC201811329 K.G.REDDY COLLEGE OF ENGINEERING & T
dimensions of calf from bali cows are 77.27 cm; 92.49 cm; 85.90cm; 25.65 cm; 12.813 cm; and 50.76 cm. The average body weight gain of bali calves and cows was 173.68 g and 172.10 g/head/day. It was concluded that body color of bali cattle in Kupang has no significant effect on body dimensions and body weight gain of bali calves and cows. The body
Research Article The Future Development Direction of Cloud-Associated Edge-Computing Security in the Era of 5G as Edge Intelligence Odugu Rama Devi ,1 Julian Webber ,2 Abolfazl Mehbodniya ,2 Morsa Chaitanya ,3 Parag S. Jawarkar ,4 Mukesh Soni ,5 and Shahajan Miah 6 1Department of CSE, Lakireddy Bali Reddy College of Engineering (Autonomous), Mylavaram 521230, NTR District,
LAKIP Dinas Pendidikan Pemuda dan Olahraga Provinsi Bali Tahun 2016 5 C. Struktur Organisasi Dinas Pendidikan Pemuda dan Olah Raga Provinsi Bali dibentuk berdasarkan Peraturan Gubernur Bali Nomor 67 Tahun 2011, tanggal 4 Nopember 2011 tentang Rincian Tugas Pokok Dinas Pendidikan, Pemuda dan Olahraga
24. Dinas Pendidikan, Kepemudaan dan Olah Raga adalah Dinas Pendidikan, Kepemudaan dan Olah Raga Provinsi Bali . 25. Dinas Kesehatan adalah Dinas Kesehatan Provinsi Bali. 26. Dinas Sosial, Pemberdayaan Perempuan dan Perlindungan Anak adalah Dinas Sosial, Pemberdayaan Perempuan
Bali et ses îles de paradis. C’est parti ! On embarque à bord du Panorama II au port de Benoa . l’après-midi pour se préparer pour le diner du capitaine. Tard dans la nuit, nous naviguerons vers le port de Benoa à Bali. JOUR 8 : BENOA, BALI
The hallmark of the ISO 14001 standard that differentiates it from other environmental standards is the integration of managerial decision-making with environmental protection efforts (Raines, 2002). This is a more effective approach that divorces environmental protection efforts from other management activities. Despite its merits, several criticisms have been leveled against the ISO 14001 .
