Review of Artificial Intelligence with Retailing Sector

This research service provides an original perspective on how artificial intelligence(AI)is making its way into the retail sector.Retail has entered a new era where ECommerce and technology bellwethers like Alibaba,Amazon,Apple,Baidu,Facebook,Google,Microsoft,and Tencent have raised consumers’expectations.AI is enabling automated decision-making with accuracy and speed,based on data analytics,coupled with selflearning abilities.The retail sector has witnessed the dramatic evolution with the rapid digitalization of communication(i.e.Internet)and;smart phones and devices.Customer is no longer the same as they became more empowered by smart devices which has entirely prevailed their expectation,habits,style of shopping and investigating the shops.This article outlines the Significant innovation done in retails which helped them to evolve such as Artificial Intelligence(AI),Big data and Internet of Things(IoT),Chatbots,Robots.This article further also discusses the ideology of various author on how AI become more profitable and a close asset to customers and retailers.

Author Guidelines

This document provides some guidelines to authors for submission in order to work towards a seamless submission process.While complete adherence to the following guidelines is not enforced,authors should note that following through with the guidelines will be helpful in expediting the copyediting and proofreading processes,and allow for improved readability during the review process.

Machine Learning Approach to Mobile Forensics Framework for Cyber Crime Detection in Nigeria

The mobile Cyber Crime detection is challenged by number of mobiledevices (internet of things), large and complex data, the size, the velocity,the nature and the complexity of the data and devices has become sohigh that data mining techniques are no more efficient since they cannothandle Big Data and internet of things. The aim of this research work wasto develop a mobile forensics framework for cybercrime detection usingmachine learning approach. It started when call was detected and thisdetection is made by machine learning algorithm furthermore intelligentmass media towers and satellite that was proposed in this work has theability to classified calls whether is a threat or not and send signal directlyto Nigerian communication commission (NCC) forensic lab for necessaryaction.

Author Guidelines

This document provides some guidelines to authors for submission in order to work towards a seamless submission process.While complete adherence to the following guidelines is not enforced,authors should note that following through with the guidelines will be helpful in expediting the copyediting and proofreading processes,and allow for improved readability during the review process.

Alpha Power-Kumaraswamy Distribution with An Application on Survival Times of Cancer Patients

The aim of the study is to obtain the alpha power Kumaraswamy(APK)distribution.Some main statistical properties of the APK distribution are investigated including survival,hazard rate and quantile functions,skewness,kurtosis,order statistics.The hazard rate function of the proposed distribution could be useful to model data sets with bathtub hazard rates.We provide a real data application and show that the APK distribution is better than the other compared distributions from the right-skewed data sets.

Churn Prediction Task in MOOC

Churn prediction is a common task for machine learning applications in business.In this paper,this task is adapted for solving problem of low efficiency of massive open online courses(only 5%of all the students finish their course).The approach is presented on course“Methods and algorithms of the graph theory”held on national platform of online education in Russia.This paper includes all the steps to build an intelligent system to predict students who are active during the course,but not likely to finish it.The first part consists of constructing the right sample for prediction,EDA and choosing the most appropriate week of the course to make predictions on.The second part is about choosing the right metric and building models.Also,approach with using ensembles like stacking is proposed to increase the accuracy of predictions.As a result,a general approach to build a churn prediction model for online course is reviewed.This approach can be used for making the process of online education adaptive and intelligent for a separate student.

CO-BOT:An Intelligent Technique for Designing a Chatbot for Initial COVID-19 Test

The coronavirus(nCOV-19),which was discovered,has now spread around the world.However,managing the flow of a large number of cases has proven to be a significant issue for hospitals or healthcare professionals.It is becoming increasingly challenging to speak with a medical expert after the epidemic’s initial wave has passed,particularly in rural areas.Thus,it becomes clear that a Chatbot that is well-designed and implemented can assist patients who are located far away by advocating preventive actions,and viral updates in various cities,and minimising the psychological harm brought on by dread.In this study,a sophisticated Chabot’s design for diagnosing individuals who have been exposed to COVID-19 is presented,along with recommendations for immediate safety measures.Additionally,when symptoms grow serious,this virtual assistant makes contact with specialised medical professionals.

On Enforcing Dyadic-type Homogeneous Binary Function Product Constraints in MatBase

Homogeneous binary function products are frequently encountered in the sub-universes modeled by databases,spanning from genealogical trees and sports to education and healthcare,etc.Their properties must be discovered and enforced by the software applications managing such data to guarantee plausibility.The(Elementary)Mathematical Data Model provides 17 types of dyadic-based homogeneous binary function product constraint categories.MatBase,an intelligent data and knowledge base management system prototype,allows database designers to simply declare them by only clicking corresponding checkboxes and automatically generates code for enforcing them.This paper describes the algorithms that MatBase uses for enforcing all 17 types of homogeneous binary function product constraint,which may also be employed by developers without access to MatBase.

Expert Review on Usefulness of an Integrated Checklist-based Mobile Usability Evaluation Framework

Previous mobile usability studies are only pertinent in the context of ergonomics,physical user interface,and mo­bility aspects.In addition,much of the previous mobile usability conception was built on desktop computing measure­ments,such as desktop and web application checklists,or scarcely addressed the mobile user interface.Moreover,the studies focus mainly on interface features for desktop applications and do not reflect comprehensive mobile interface features such as navigation drawers and spinners.Therefore,conducting usability evaluation using conventional us­ability measurement would result in irrelevant results.In addition,the resulting works are tailored for usability testing,which requires highly skilled evaluators and usability specialists(e.g.,usability testers and user experience designers),who are rarely integrated into a development team.The lack of expertise could lead to unreliable usability evaluations.This paper presents a review from industrial experts on a comprehensive and feasible usability evaluation framework developed in our previous work.The framework is dedicated to smartphone apps,which integrate evaluator skills and design concerns.However,there is no evidence of its usefulness in practice.Therefore,the usefulness of the frame­work measurement for evaluating apps’usability in the eyes of non-usability specialists is empirically assessed in this paper through an expert review.The expert review involved eleven industrial developers and was complemented by a semi-structured interview.The method is replicated in comparison with a framework from another study.The findings show that the formulated framework significantly outperformed the framework(p=0.0286)from other studies with large effect sizes(r=1.81)in terms of usefulness.

AgamottoEye:Recovering Request Flow for Cloud Systems via Log Analysis

Cloud applications are implemented on top of different distributed systems to provide online service.A service request is decomposed into multiple sub-tasks,which are dispatched to different distributed systems components.For cloud providers,monitoring the execution of a service request is crucial to promptly find problems that may compromise cloud availability.In this paper,we present AgamottoEye,to automatically construct request flow from existing logs.AgamottoEye addresses the challenges of analyzing interleaved log instances,and can successfully extract request flow spread across multiple distributed systems.Our experiments with Hadoop2/YARN show that AgamottoEye can analyze 25,050 log instances in 57.4s,and the extracted request flow information is helpful with error detection and diagnosis.

A Review on Ranking of Z-numbers

There are numerous studies about Z-numbers since its inception in 2011.Because Z-number concept reflects human ability to make rational decisions,Z-number based multi-criteria decision making problems are one of these studies.When the problem is translated from linguistic information into Z-number domain,the important question occurs that which Z-number should be selected.To answer this question,several ranking methods have been proposed.To compare the performances of these methods,benchmark set of fuzzy Z-numbers has been created in time.There are relatively new methods that their performances are not examined yet on this benchmark problem.In this paper,we worked on these studies which are relative entropy based Z-number ranking method and a method for ranking discrete Z-numbers.The authors tried to examine their performances on the benchmark problem and compared the results with the other ranking algorithms.The results are consistent with the literature,mostly.The advantages and the drawbacks of the methods are presented which can be useful for the researchers who are interested in this area.

Establishing the Forecasting Model with Time Series Data Based on Graph and Particle Swarm Optimization

In recent years,a wide variety of fuzzy time series(FTS)forecasting models have been created and recommended to handle the complicated and ambiguous challenges relating to time series data from real-world sources.However,the accuracy of a model is problem-specific and varies across data sets.But a model’s precision varies between different data sets and depends on the situation at hand.Even though many models assert that they are better than statistics and a single machine learning-based model,increasing forecasting accuracy is still a challenging task.In the fuzzy time series models,the size of the intervals and the fuzzy relationship groups are thought to be crucial variables that affect the model’s forecasting abilities.This study offers a hybrid FTS forecasting model that makes use of both the graph-based clustering technique(GBC)and particle swarm optimization(PSO)for adjusting interval lengths in the universe of discourse(UoD).The suggested model’s forecasting results have been compared to those provided by other current models on a dataset of enrollments at the University of Alabama.For all orders of fuzzy relationships,the suggested model outperforms its counterparts in terms of forecasting accuracy.

Author Guidelines

This document provides some guidelines to authors for submission in order to work towards a seamless submission process.While complete adherence to the following guidelines is not enforced,authors should note that following through with the guidelines will be helpful in expediting the copyediting and proofreading processes,and allow for improved readability during the review process.

High-Resolution Traffic Flow Prediction Model Based on Deep Learning

The time resolution of the existing traffic flow prediction model is too big to be applied to adaptive signal timing optimization.Based on the view of the platoon dispersion model,the relationship between vehicle arrival at the downstream intersection and vehicle departure from the upstream intersection was analyzed.Then,a high-resolution traffic flow prediction model based on deep learning was developed.The departure flow rate from the upstream and the arrival flow rate at the downstream intersection was taking as the input and output in the proposed model,respectively.Finally,the parameters of the proposed model were trained by the field data,and the proposed model was implemented to forecast the arrival flow rate of the downstream intersection.Results show that the proposed model can better capture the fluctuant traffic flow and reduced MAE,MRE,and RMSE by 9.53%,39.92%,and 3.56%,respectively,compared with traditional models and algorithms,such as Robert­son's model and artificial neural network.Therefore,the proposed model can be applied for realtime adaptive signal timing optimization.

On Software Application Database Constraint-driven Design and Development

This paper presents a methodology driven by database constraints for designing and developing(database)software applications.Much needed and with excellent results,this paradigm guarantees the highest possible quality of the managed data.The proposed methodology is illustrated with an easy to understand,yet complex medium-sized genealogy software application driven by more than 200 database constraints,which fully meets such expectations.

Implementation of Distributed Control System for Rice Mill Using C#

The paper presents the distributed control system for rice mill using C#language.The real-time manufacturing system can be implemented by utilizing the signal from the real time control units that is more operative than other old-fashioned control systems in the extent of modern industrial days.The software-based Distributed Control System(DCS)is novel fashionable than any other conventional control systems in the state-ofthe-art manufacturing developments.This research study emphasizes on the implementation of the DCS-based rice mill using visual C#.net.The Industrial Ethernet(IE)is realized between the top level controller for the operator and the controlled station for the remote devices.The model of client-server approach is more appropriate for the automation and manufacturing research purposes.In this study,the computer graphical simulation of the complete control development is depicted in real-time status quo by visual C#language under Visual Studio 2008 software.The parallel ports in the computers of remote terminal level and the master terminal level controllers have been interconnected with port interface coding by visual C#program.

Radio Network Planning and Optimization for 5G Telecommunication System Based on Physical Constraints

The paper mainly focuses on the network planning and optimization problem in the 5G telecommunication system based on the numerical investigation.There have been two portions of this work,such as network planning for efficient network models and optimization of power allocation in the 5G network.The radio network planning process has been completed based on a specific area.The data rate requirement can be solved by allowing the densification of the system by deploying small cells.The radio network planning scheme is the indispensable platform in arranging a wireless network that encounters convinced coverage method,capacity,and Quality of Service necessities.In this study,the eighty micro base stations and two-hundred mobile stations are deployed in the-15km×15km wide selected area in the Yangon downtown area.The optimization processes were also analyzed based on the source and destination nodes in the 5G network.The base stations’location is minimized and optimized in a selected geographical area with the linear programming technique and analyzed in this study.

Study,Analysis and Comparison between Amazon A10 and A11 Search Algorithm

The entirety of Amazon’s sales being powered by Amazon Search,one of the leading e-commerce platforms around the globe.As a result,even slight boosts in appropriateness can have a major impact on profits as well as the shopping experience of millions of users.Throughout the beginning,Amazon’s product search engine was made up of a number of manually adjusted ranking processes that made use of a limited number of input features.Since that time,a significant amount has transpired.Many people overlook the fact that Amazon is a search engine,and even the biggest one for e-commerce.It is indeed time to begin treating Amazon truly as the top e-commerce search engine across the globe because it currently serves 54%of all product queries.In this paper,the authors have considered two most important Amazon search engine algorithms viz.A10 and A11 and comparative study has been discussed.

Intrusion Detection through DCSYS Propagation Compared to Auto-encoders

In network settings,one of the major disadvantages that threaten the network protocols is the insecurity.In most cases,unscrupulous people or bad actors can access information through unsecured connections by planting software or what we call malicious software otherwise anomalies.The presence of anomalies is also one of the disadvantages,internet users are constantly plagued by virus on their system and get activated when a harmless link is clicked on,this a case of true benign detected as false.Deep learning is very adept at dealing with such cases,but sometimes it has its own faults when dealing benign cases.Here we tend to adopt a dynamic control system(DCSYS)that addresses data packets based on benign scenario to truly report on false benign and exclude anomalies.Its performance is compared with artificial neural network auto-encoders to define its predictive power.Results show that though physical systems can adapt securely,it can be used for network data packets to identify true benign cases.

Development of New Machine Learning Based Algorithm for the Diagnosis of Obstructive Sleep Apnea from ECG Data

In this study,a machine learning algorithm is proposed to be used in the detection of Obstructive Sleep Apnea(OSA)from the analysis of single-channel ECG recordings.Eighteen ECG recordings from the PhysioNet Apnea-ECG dataset were used in the study.In the feature extraction stage,dynamic time warping and median frequency features were obtained from the coefficients obtained from different frequency bands of the ECG data by using the wavelet transform-based algorithm.In the classification phase,OSA patients and normal ECG recordings were classified using Random Forest(RF)and Long Short-Term Memory(LSTM)classifier algorithms.The performance of the classifiers was evaluated as 90% training and 10%testing.According to this evaluation,the accuracy of the RF classifier was 82.43% and the accuracy of the LSTM classifier was 77.60%.Considering the results obtained,it is thought that it may be possible to use the proposed features and classifier algorithms in OSA classification and maybe a different alternative to existing machine learning methods.The proposed method and the feature set used are promising because they can be implemented effectively thanks to low computing overhead.