Design and implementation of monitoring system for paralysis patient using IoT

Internet of things represents a catch line of smart applications. At the same time it plays a leading role in the health care systems, as it provides the connectivity of the distant patients, who are not able to express their concerns. In this modern era there are a lot of technologies that are being used in different healthcare fields for paralyzed people. This paper proposed a model for paralyzed patient by considering Arduino with the integration of gyro sensors for the patient hand and finger movement. Patient can easily be connected with this device by sending his message to caretaker only by moving their hands into respective directions, or they can send their message by moving their fingers and feet. Through this device paralyzed patient will be able to convey their messages to caretakers, so they can assist the patient. This system will enhance the medical care to those patients who are even not able to convey their message.

Crypto Currency Cognizance: A New Entrant in Financial Heaven

Cryptocurrency used an online ledger with highly strong cryptography to secure online transactions. There are multiple cryptocurrencies traded publically that imparts an important role on the behavioral intention of users. The disruption in the economy of the world has provided some opportunities for the development of virtual money or cryptocurrencies so that financial crises of individuals can be removed. The study adapted Theory of Acceptance Model (TAM) that explains the factor that effect behavioral intention of the users of cryptocurrency. The data was collected through survey questionnaire from 177 participants to measure the impact of cryptocurrency on the behavioral intention of users. The findings of this study revealed that multiple factors such as social influence, facilitating conditions, financial literacy, and perceived risk of cryptocurrency imparts an important role on the behavioral intention of individuals to use cryptocurrencies. Moreover the findings will propose numerous methods to function with a greater chance of success in the block chain-related services and cryptocurrencies markets.

An Autonomous Follow Me Platform for Carrying and Moving Objects

The technology of An Autonomous "follow me" platform for carrying and moving objects has gone through rapid technological advancements. Numerous follow me robots are accessible with various running advancements, yet the expense is high. These robots are not user-friendly and therefore not much successful. In this research, a fully automated, economical, fast, efficient and smart “Follow Me” robot is designed. This robot has the ability to carry luggage or move objects from one place to another place. It will help pregnant women and elder people to carry their things. An autonomous follow me robot has two working modes, the first one is the default mode and the second one is Bluetooth mode or remote mode. In default mode, the user will walk in the front of the ultrasonic sensor and it will follow the user until it goes beyond the range. In Bluetooth mode, the customer needs to interact with the robot with the help of a mobile application. The customer by then has the Graphical User Interface (GUI) to control the robot. This framework enables the client to vigorously communicate with the robot at various dimensions of the control (left, right, forward, backward, and stop). The application interface is built as simple as it can be used by a wide range of patients.

Perceived Usefulness of Big-Data for Store Layout: Evidence for Organized Retailers of Karachi

Big-Data is one of the most useful technologies available nowadays to understand behaviorsand patterns. However, in addition to its societal benefits technology might also be used bypractitioners in industrial settings. The Retail industry is also treated as the one which might receive major benefits from the use of Big-Data and therefore this study is purposively associated with implications of Big-Data for the retail sector. The Study uses store layout as the dependent variable as it has the most influence on purchase as the real purpose of Big-Data is to analyze behavior and patterns, therefore, the selection of variable is legitimate. However, the technology is not well-known in emerging markets like Pakistan therefore study is linked with quota sampling and uses SMART-PLS to analyze results. Results indicated that Big-Data was perceived as the potent tool for operations of the organized retail sector of Karachi.

Poet Attribution for Urdu: Finding Optimal Configuration for Short Text

This study presents a machine learning system to identify the poet of a given poetic piece consisting of 2 lines (i.e. a couplet) or more. The task is more difficult than the general task of author attribution, as the number of words in verses and poems are usually less than the number of articles present in author attribution datasets. We applied classification algorithms with different sets of feature configurations to run several experiments and found that the system performs best when support vector machine using a combination of unigram and bigram are used . The best system (for 5 Urdu poets) has the accuracy of 88.7%.

PSL Eye: Predicting the Winning Team in Pakistan Super League (PSL) Matches

Pakistan Super League (PSL) is a well-known T20 cricket league with millions of viewers. With this large viewer base, predicting the outcome of PSL matches opens a new research avenue for academic researchers. In this paper, we collect PSL data from relevant sources and generate a validated data set for machine learning experiments. We implement the “PSL Eye” solution which employs Neural Networks (NNs) to predict the match winning team. We preprocess the dataset to eliminate the extra variables then we tune the hyper parameters of NN. After acquiring the optimal values of hyper parameters, we run our NN based PSL Eye to obtain the final results. The overall accuracy of PSL-Eye with testing data set is 82% which is very promising and shows the importance of NN in predicting PSL match outcome.

A Comparative Study of Curvature-Based and Differential Versions of Dotter Raster-stereography Techniques

Conventional Line-based Raster-stereography has been a popular technique for 3-D surface topography. However, in its application for human face screening, the problem of line breaking was observed. In order to resolve this problem, there came up a new technique called dotted raster-stereography. The previously reported version of dotted raster-stereography extracted the curvature features of human face. This paper presents a modified version, viz. differential dotted raster-stereography in which instead of curvature, differences in straight line distances between adjacent points are calculated. A comparative picture of the two versions of dotted raster-stereography techniques is presented. Results suggest that this new differential version of dotted raster-stereography algorithm is faster in execution due to its simpler implementation in software, though lower in accuracy, as compared with the previously reported curvature-based version of dotted raster stereography.

Keystroke dynamics Based Technique to Enhance the Security in Smart Devices

Nowadays, smart devices have become a part of ourlives, hold our data, and are used for sensitive transactions likeinternet banking, mobile banking, etc. Therefore, it is crucial tosecure the data in these smart devices from theft or misplacement.The majority of the devices are secured with password/PINbaseduser authentication methods, which are already proveda less secure or easily guessable user authentication method.An alternative technique for securing smart devices is keystrokedynamics. Keystroke dynamics (KSD) is behavioral biometrics,which uses a natural typing pattern unique in every individualand difficult to fake or replicates that pattern. This paperproposes a user authentication model based on KSD as an additionalsecurity method for increasing the smart devices’ securitylevel. In order to analyze the proposed model, an android-basedapplication has been implemented for collecting data from fakeand genuine users. Six machine learning algorithms have beentested on the collected data set to study their suitability for usein the keystroke dynamics-based authentication model.

Influence of Online Banking on consumers and its perception and acceptance during Covid-19

The study is about Influence of Online Banking on consumers and its perception and acceptance during Covid-19. Research hypothesis and research questionnaire had been developed to undertake the study for the research hypothesis, primary data has been used through the sample size and Convenience sampling was carried out where the quota was determined according to the share of the total number. To answer the questions statistically five hypotheses were created and tested through the questionnaire. Results shows that majority of the respondents have agreed and established trust on online banking transactions during pandemic situation

CONCEPT DRIFT IN STREAMING DATA: A SYSTEMATIC LITERATURE REVIEW

World is generating immeasurable amount of data every minute, that needs to be analyzed for better decision making. In order to fulfil this demand of faster analytics, businesses are adopting efficient stream processing and machine learning techniques. However, data streams are particularly challenging to handle. One of the prominent problems faced while dealing with streaming data is concept drift. Concept drift is described as, an unexpected change in the underlying distribution of the streaming data that can be observed as time passes. In this work, we have conducted a systematic literature review to discover several methods that deal with the problem of concept drift. Most frequently used supervised and unsupervised techniques have been reviewed and we have also surveyed commonly used publicly available artificial and real-world datasets that are used to deal with concept drift issues.