Identifying Stress Level Among Gamers Using Electroencephalogram Signals

This research is conducted to identify stress level among gamer using Electroencephalogram Machine (EEG). Electroencephalogram machine or better known as EEG machine is a machine used by neuroscientists to read brain signals activity through various number of channels. The brain signals collected from subjects using 19 channels EEG machine which is DABO Machine. The problem in this research study is to find out if game can induce stress. The expected outcome of this research is that brain signal collected from subjects could give enough evidence about the relationship between playing game and stress level in their daily activities. Objective of the research is to design experimental procedure suitable for understanding the bio-signal of subjects inducing stress and to understand the relationship between four basic emotion (Happy, Calm, Fear, Sad) and the emotion while playing the games. In our research methodology, we focus on five difference stages to complete the research. The stages start with the data collection, pre-processing, features extraction classification and lastly analysis. Later, we able to come out with the result of our research about the stress level for the subject. The experiment was conducted by following a standard protocol experiment for EEG machine. This data will be analysed using Mel Frequency Cepstral Coefficients (MFCC) as feature extraction, and multilayer perceptron (MLP) as classifier. The result show that the subject has positive emotion which is calm and happy at the beginning and ending of playing the game. At the beginning, subject only start with demo, so the subject did not feel pressured and at the end we assumed that the subject feel relieved because of ending the game. After certain time playing the game, the subject starting to have negative emotion until the end of the game. This happen because of subject started to feel stress after plays the higher level of the game. Based on the result, we can conclude that game can induce stress among gamers

Mathefunic: A Mobile Based Game for Engaging Children with ADHD

Attention Deficit Hyperactivity Disorder (ADHD) is a common neurobehavioral disorder that is prevalent in children and adolescents. ADHD affects student’s learning due to its symptoms which are inability to stay focused, hyperactivity, and challenges in self-control. In this paper, we explore the use of mobile games to engage children diagnosed with ADHD. Mobile games are now widely used in learning, which is part of mobile learning. Previous studies have shown a positive relationship between digital games and learning for children with ADHD. Therefore, we designed and developed a mobile game based on existing literature on techniques used to retain the attention and engage children with ADHD. The study was evaluated based on the duration of time the children spent playing the game and their activities and interactions while playing the game. Our findings showed that mobile-based games can be used to engage children with ADHD.

P300 ERP Component on Eating Habits Profiling Using Dynamic Evolving Spiking Neural Network (deSNN)

— Unhealthy eating habits have become a big issue that often causes many chronic diseases in various countries in recent years. The current assessment to identify the status of eating habits is to use self-assessment. However, self-assessment is known to have an error or uncertainty value due to cognitive factors from respondents that affect the results of the assessment. A person's profile is potentially measured by reviewing Event-related potential (ERP) which is an ideal technique for understanding perception and attention. This study uses images of healthy and unhealthy foods as a stimulus when recording EEG data. The method used for classification is dynamic evolving spiking neural network (deSSN) based on the Neucube architecture. The results showed that the mean amplitude of the P300 component discovered in the Parietal and Occipital lobes was higher for healthy food in the healthy eating habits group. Whereas the unhealthy eating habits group was higher for unhealthy foods. The deSNN classification is proven to operate in learning ERP data but the accuracy rate is not too high due to inadequate sample training

Proposed De-noising Algorithm

minimizing noises from images to restore it and increase its quality is a crucial step. For this, an efficient algorithms were proposed to remove noises such as (salt pepper, Gaussian, and speckle) noises from grayscale images. The algorithm did that by selecting a window measuring 3x3 as the center of processing pixels, other algorithms did that by using median filter (MF), adopted median filter (AMF), adopted weighted filter (AWF), and the adopted weighted median filter (AWMF). The results showed that the proposed algorithm compares to previous algorithms by having a better signal-to-noise ratio (PSNR).

Factors Influencing the adoption of e-commerce by Small and Medium-Sized Enterprises (SMEs) in Algeria: a qualitative study

Small and Medium-sized Enterprises (SMEs) are regarded as the engine of the growth of the world economy. They had recently experienced rapid growth and improved their business activities in terms of customers’ number and revenue expansion when they began to embrace e-commerce and started using it in their business. However, although there is a growing interest in e-commerce, its use is still insufficient in Algeria. Therefore, this study aimed to identify the factors that influence the adoption of e-commerce by SMEs in Algeria. This study adopts a qualitative methodology which involves in-depth, structured interviews to identify the factors that affect SMEs for the adoption of e-commerce in Algeria. The result of research found the main factors hindering the adoption of e-commerce by SMEs in Algeria are the e-payment methods, non-readiness of banks, lack of legal protection and lack in awareness of the benefits of e-commerce, as well as fear of risks. However, some other elements are less significant to influence the adoption of e-commerce. These research findings will give an addition in terms of bringing and giving a chance to the SMEs' leaders and the Algerian economic officials and a clear view of e-commerce practices. This can help them to design a strategy to remove barriers tactfully to its advantage.

Advanced Voting System Using Fingerprint

A Fingerprint is an important identifier for the humans. This paper proposes finger print voting system with Arduino. The majority of the worldwide election were using a paper-based voting rather than using biometric system. The current voting process has safety problems such as authenticity of voters. In proposed system, a voter identity can be proved instantly. All voters’ information was stored securely to register in the system. The main objective is to enhance the security in order to prevent duplication and provide a system which reduce the burden for people on conducting a voting. Thus, by implementing this system, user can put their vote with fingerprint instead of paper without doubting about their security. Voting Using Fingerprint reduce the polling time, it provides easy and accurate counting without human labor.

Sea Level Anomaly and Earthquake Predictions: Endangered Countries Prognostications

Natural calamities are often unforeseen and cause massive destruction. It is extremely difficult to predict natural disasters. Existing machine learning techniques are not reliable enough to find the affected countries due to earthquakes and rising sea levels. The aim of this paper is to use predictive analysis to find the countries that will be affected by earthquakes and rising sea levels. Also, the purpose is to see how machine learning techniques perform in terms of sudden calamities like earthquakes or slow calamities like rising sea level. The results was deduced by data analysis, and deep learning techniques like Long-Short Term Memory (LSTM). It was found out that using the approached method in this paper can accurately identify the countries that are going to be affected and predict both earthquake and sea level anomalies accurately. For earthquake, the model was able to capture the happening of earthquake events into a certain quarter of the year with the Root Mean Square Error (RMSE) of 0.504. And for sea level rise, the RMSE was 0.064. It was concluded that Deep learning techniques (e.g.-LSTM) work well with slow changes like sea level anomaly rather than earthquakes. The techniques used in this paper can be upgraded further in the future to find and help more endangered countries to be prepared better against these sudden calamities.

Solving Routing Problem Using Improved Camel Herds Algorithm

This paper introduces a new adaptive, distributed routing algorithm based on the Improved Camel Herds Algorithm (CHA). It is an intelligent, multi-agent optimization algorithm that is inspired by the behavior of camels and how they search for food in their desert environment. We examine its ability to solve the routing problem in switched networks: finding the shortest path in the process of transferring data packets between networks. Many meta-heuristic algorithms have been previously proposed to address the routing problem, and this proposed approach is compared with three well-known algorithms (ACO, GA, PSO) on ten graphs (weighted, integer, and not negative) and datasets with various size of nodes (from 10 nodes to 297 nodes). Three performance criteria were used to evaluate the performance of the algorithms (mean relative error, standard deviation, and number of function evaluations). The results proved that the performance of the proposed algorithm is both promising and competitive with other algorithms.

Health Profiling Using Event-Related Potential (ERP) Brain Signals and Spiking Neural Network (SNN)

Unhealthy lifestyles, especially on nutritional factors have become a major problem causing many diseases in Malaysians in recent years. Identification of lifestyle profiles such as preventive for individuals who adopt healthy and curative for individuals who do not maintain their lifestyle is needed to increase their awareness regarding their lifestyle. Because self-assessment is known to be vulnerable to produce response biases that lead to misclassification, identification of profiles based on brain responses needs to be done. An Event-related potential (ERP) is the main tools of cognitive neurologists and make ideal techniques for studying perception and attention. This research captured brain activity using electroencephalography (EEG) during receiving images of healthy and unhealthy foods that act as health-related stimuli. These EEG signals converted mathematically into the ERP signals and entered into the classification interface as input. In terms of classification, the methodology used is a dynamic developing Spiking Neural Network (deSSN) based on the Neucube architecture. ERP analysis results shown the mean amplitude of the LPP component in the Parietal and Occipital lobes is higher for healthy food in the preventive group. Whereas in the curative group it has been shown to be higher for unhealthy foods. This result is thought to reflect their preference in choosing food in their daily lifestyle. However, the results of the classification have shown that unhealthy food stimulation in the LPP wave showed superior results compared to data analysis in other conditions. Classification with ERP data is believed to support the results of self-assessment and build methods of making profiles that are more accurate and reliable.

Mobile Interface Design to suit the Algerian Culture: First initial design

The growing number on the use of mobile apps has extraordinary potential for the public transport industry. Through the use of mobile apps, may provide fast, effective and safe services to the passengers to commute to different destinations. However, the particularities of mobile applications need particular attention concerning the usability aspects, such as culture. Taxi-Inter-Wilaya is a shared-taxi service between cities in Algeria. This taxi service is estimated as one of the most beneficial ways for the Algerians to travel. However, the booking for passengers travelling to long distances of 600 KM and more, can only be made via phone calls. Consequently, passengers face various challenges in the booking process. Drivers receive calls for bookings while driving, which puts them in an illegal situation and unsafe. To this purpose, the researcher uses the design thinking approach to propose a transport mobile application that can assist and support both taxi drivers and passengers to travel between cities suitable to the Algerian culture. The data collection method comprises a series of Interviews with both users (passengers and taxi drivers) as well as designing the several iterations of low and high-fidelity prototypes of the mobile application that is named as DJAMAAI. The prototypes were then evaluated via a series of usability testing for both groups of users. Results shows that users favour visual representations that are associated to their culture more than textual; they prefer, buttons such as icons and symbols, as well as more straightforward and minimalist user interface design. Also, symbols and hints help learnability of using the tool.