A Genetic Algorithm with Design of Experiments Approach to Predict the Optimal Process Parameters for FDM

This paper describes a Genetic Algorithm (GA) with Design of Experiments (DoE) approach to predict the optimized surface roughness and porosity characteristics of the parts produced using ABS material on stratasys FDM 2000 machine. The Mathematical Model (MM) was developed by using Response Surface Methodology (RSM). It is to predict and investigate the influence of selected process parameters namely slice thickness, road width, liquefier temperature and air gap and their interactions on the surface roughness and porosity. The developed MM is the fitness function in GA in order to find out the optimal sets of process parameters and to predict the corresponding surface quality characteristics. These results have been validated and the experimental results after GA are found to be in conformance with the predicted process parameters.Mechanical Engineerin

A BIG DATA ENABLED CHANNEL MODEL FOR 5G WIRELESS COMMUNICATION SYSTEMS

The normalization movement of the fifth era (5G) remote interchanges has of late been quickened and the main business 5G administrations would be given. The developing of huge PDAs, inventive multipart situation, cumbersome recurrence groups, enormous reception apparatus components, and thick little cells will manufacture huge datasets and pass on 5G interchanges to the age of huge information. This contradiction examines a scope of solicitation of huge information investigation, particularly AI calculations in remote correspondences and channel displaying. We recommend a major information and AI empowered remote channel model structure. The proposed channel model depends on fake neural organizations (ANNs), along with feed-forward neural organization (FNN) and spiral premise work neural organization (RBF-NN). The commitment imperative are transmitter (Tx) and beneficiary (Rx) organizes, Tx-Rx separation, and transporter recurrence, while the yield boundaries are channel measurable properties, tallying they got power, root mean square defer spread, and RMS point spreads. Datasets used to prepare and examination the ANNs are gathered from together genuine channel estimations and calculation based stochastic model (GBSM). Reproduction grades show excellent execution and pick that AI calculations can be compelling insightful instruments for future estimation based remote channel displaying

ADAPTIVE SALES PREDICTION IN TOURISM INDUSTRY BASED ON FEATURE SELECTION IN DATA MINING

Information mining procedure is used routinely to separate gigantic proportion of data and concentrate unforeseen results from that data. Data mining methodologies are used in a wide collection of requests and fields, for instance, customer relationship the board in advancing, clinical illness desire and affirmation of feasible treatment strategies, budgetary and banking danger the heads, getting ready masterminding, customer direct assessment in electronic business, predicting and preventing possible wrongdoings in criminal sciences, etc There are different assessing models for choosing bargains in the movement business; data mining methodology have been assessed the basically astonishing practice for foreseeing bargains in the movement business. The counting is then completed to uncover out the tendencies of the customers from the solicitations table. Second is the idea assessments where the words enter in the comments by the customers are being verified whether the word is positive or negative. Moreover the scores are given out. After the scores are apportioned they are connoted find the rating. Appropriately the two enlightening assortments used here are orders seat and the comments board in the improvement of envisioning bargains in the movement business

ONLINE LEARNING PERFORMANCE ON E-LEARNING SYSTEM

Training is normally seen as the strategy whereby we have understudies in a homeroom conveyance exercises from a Teacher however with encourage of data innovation through the web, learning would now be able to be achieve without basically having an instructor directly before an understudy. E-learning is one of the instruments that show up from data innovation and has been coordinated in bunches of colleges schooling programs, variable from the conventional methodology of training to electronic climate in which an understudy can induction and make use of data all over the place and at any all around arranged time. The expectation of E-learning Management System is to make schedule the current manual plan by the help of automated gear's and undeniable program, fulfilling their necessities, so their valuable information/data can be store for a more extended age with easy getting to and control of the equivalent. The fundamental programming and equipment are easily reachable and easy to work with. E-learning Management System, as depicted higher than, can manual for blunder free, secure, steady and quick administration grouping. It can uphold the client to consider on their different exercises modestly to think on the check keeping. In this manner it settles help association in upgraded usage of assets. The association can protect automated records without excess passages. That implies that one need not be occupied by data that isn't relevant, while having the option to arrive at the data. It might help gathering ideal administration in points of interest. In an amazingly brief timeframe, the arrangement will be recognizable, basic and sensible. It will help an individual to know the administration of spent year totally and brilliantly. It likewise helps in current each work comparative with E-learning Management System. It will be additionally consolidated the expense of gather the administration and assortment practice will go on easily

WIRELESS SENSOR BASED MAXIMIZING SENSOR LIFETIME USING ADAPTIVE ALGORITHM

Remote energy move innovation dependent on dazzling reverberating coupling has arisen as a cheerful innovation for remote sensor organizations, by on condition that controllable yet successive energy to sensors. The utilization of a portable charger to remotely charge sensors in a battery-powered sensor organization so the amount of sensor lifetimes is boost even as the go on an outing distance of the versatile charger is limit. Differentiating existing investigations that implicit a versatile charger should charge a sensor to its full energy ability prior to moving to charge the following sensor, we here accept that every sensor can be halfway charged so more sensors can be charged before their energy exhaustions. Under this new energy charging model, we initially plan two novel enhancement issues of booking a portable charger to charge a bunch of sensors, with the goals to augment the amount of sensor lifetimes and to limit the movement distance of the versatile charger while accomplishing the greatest amount of sensor lifetimes, individually. We at that point propose effective calculations for the issues. We at long last gauge the introduction of the proposed calculations through investigational reenactments. Proliferation results make clear that the proposed calculations are very guarantee. Particularly, the normal energy termination length per sensor by the proposed calculation for amplifying the amount of sensor lifetimes is just 9% of that by the best in class calculation while the movement distance of the portable charger constantly proposed calculation is just about from 1% to 15% longer than that by the cutting edge benchmark

DROPOUT STUDENT PREDICTION USING NAÏVE BAYS CLASSIFIER

The objectives of this research work is to identify relevant attribute from socio-demographic, academic and institutional data of first year students from undergraduate at the University and design a prototype machine learning tool which can routinely distinguish whether the student persist their revise or drop their learning using classification technique based on decision tree. For powerful decision making tool different parameter are need to be considered such as socio-demographic data, parental attitude and institutional factors. The generated knowledge will be quite useful for tutor and management of university to develop policies and strategies related to increase the enrolment rate in University and to take precautionary and consultative procedures and thereby diminish student dropout. It can also use to find the reasons and relevant factors that affect the dropout students

CONTEXT-BASED SPECTRUM SHARING IN 5G WIRELESS NETWORKS

Dynamic range sharing can give numerous advantages to remote organizations administrators. Nonetheless, its effectiveness requires complex control components. The more setting data is utilized by it, the better of organizations is normal. An office for gathering this data, handling it, and controlling base stations oversaw by different organization administrators is a supposed Radio Environment Map (REM) subsystem. REM-based plans for the designation of base stations power levels in 4G/5G organizations, while considering impedance produced to an authorized organization. It is expected that the two organizations have various profiles of served clients, e.g., territory of their positions and development, which opens open doors for range sharing. The proposed plans have been assessed by methods for broad framework level reproductions and contrasted and two generally embraced strategy based range sharing reference plans. Reenactment results show that dynamic plans using rich setting data beats static, arrangement based range sharing plans

A MISBEHAVIOUR NODE DETECTION SCHEME FOR WIRELESS SENSOR NETWORKS

Security is one of the primary issues that have pulled in a huge load of creative work effort as of late. In multi-ricochet far off improvised association interface botch and pernicious group dropping are two hotspots for package mishaps. Whether or not the adversities are achieved by associate bungles only, or by the merged effect of association botches and toxic drop are to be perceived, can be known by seeing a progression of bundle mishaps in the association. Regardless, in the insider-attack case, whereby poisonous center points that are significant for the course abuse their knowledge into the correspondence setting to explicitly drop a restricted amount of packages essential to the association execution. Ordinary figuring’s that rely upon recognizing the pack mishap rate can't achieve adequate area accuracy considering the way that the package dropping rate for the present circumstance is equivalent to the channel botch rate. In this way to assemble the distinguishing proof precision in the group setback information declared by centers. This system gives assurance defending, scheme affirmation, and achieves low correspondence and limit overheads. A group block based framework is moreover proposed, to diminish the computation overhead of the example contrive, which grants one to trade acknowledgment accuracy for lower estimation multifaceted natur

MULTI-PLATFORM CHABOT MODELING AND DEPLOYMENT WITH FRAMEWORK

Chatbots transforms into a troublesome endeavor that requires capacity in a combination of specific spaces, going from trademark language getting ready to a significant understanding of the APIs of the zeroed in on messaging stages and pariah organizations to be consolidated. Chatbot (and voice bot) applications are continuously gotten in various spaces, for instance, web business or customer organizations as a quick correspondence channel among associations and end-customers. Different frameworks have been made to encourage their definition and plan. While these structures are gainful to design clear chatbot applications, they regardless of everything require pushed particular data to portray complex affiliations and are difficult to progress close by the association needs (for instance it is consistently hard to change the NL engine provider). Likewise, the plan of a chatbot application generally requires a significant perception of the zeroed in on stages, especially back-end affiliations, growing the unforeseen development and backing costs. In this paper, we present the Xatkit framework. Xatkit handles these issues by giving a ton of Domain Specific Languages to portray chatbots (and voice bots and bots overall) in a phase independent way. Xatkit furthermore goes with a runtime engine that thus sends the visit bot application and manages the described conversation reasoning over the establishment of choice. Xatkit's deliberate designing energizes the distinctive improvement of any of its fragments. Xatkit is open source and totally available on the web

SECURE AND EFFICIENT FAULT NODE DETECTION IN WIRELESS SENSOR NETWORKS

Propose an included, energy efficient, resource allocation framework for overcommitted clouds. The concord makes massive energy investments by 1) minimizing Physical Machine overload occurrences via virtual machine resource usage monitoring and prophecy, and 2) reducing the number of active PMs via efficient VM relocation and residency. Using real Google data consisting of a 29 day traces collected from a crowd together contain more than 12K PMs, we show that our proposed framework outperforms existing overload avoidance techniques and prior VM migration strategies by plummeting the number of unexpected overloads, minimizing migration overhead, increasing resource utilization, and reducing cloud energy consumption.&nbsp