Pairwise classification using combination of statistical descriptors with spectral analysis features for recognizing walking activities

The advancement of sensor technology has provided valuable information for evaluating functional abilities in various application domains. Human activity recognition (HAR) has gained high demand from the researchers to undergo their exploration in activity recognition system by utilizing Micro-machine Electromechanical (MEMs) sensor technology. Tri-axial accelerometer sensor is utilized to record various kinds of activities signal placed at selected areas of the human bodies. The presence of high inter-class similarities between two or more different activities is considered as a recent challenge in HAR. The nt of incorrectly classified instances involving various types of walking activities could degrade the average accuracy performance. Hence, pairwise classification learning methods are proposed to tackle the problem of differentiating between very similar activities. Several machine learning classifier models are applied using hold out validation approach to evaluate the proposed method

EEG pattern of cognitive activities for non dyslexia (engineering student) due to different gender

The purpose of this study is to identify the electroencephalogram (EEG) pattern of male and female engineering student during the cognitive activity. EEG is a method to monitoring electrical activity in the brain and has four main brainwave signal Delta Wave, Theta Wave, Alpha Wave and Beta Wave. Delta wave is a slow wave generated in deepest meditation, Theta Wave usually occurs in sleep, Alpha Wave dominant in calming, relaxing condition and Beta Wave dominant in wakeful condition. The raw data collected analysis using SPSS and Microsoft Excel to analysis the accuracy and the brainwave pattern between male and female. The average, standard derivation, correlation and Q-Q Plot are used to identify the EEG pattern between male and female during cognitive activity. Cognitive is one of the bloom taxonomy formulate for education activities. The process involves in decision making, understanding of information, attitudes and solving. Subjects are given a set of question to answer. A total of 24 students, 12 males and 12 female involve recording their EEG signal while answering the cognitive question by wearing the Emotive Insight device. All subjects are from UTHM engineering students. Data collected are focused in Alpha Wave and Beta wave which exist in when someone is in awaken condition. The difference between male and female brainwave during the cognitive activity can be observed from the analysis and discussion of the result. For future recommendation for this research is the number of subject can be increased to get more accurate data

1,1-Dibenzyl-3-(3-chloro­benzo­yl)thio­urea

In the title compound, C22H19ClN2OS, the thiono and carbonyl groups are trans positioned with respect to a partially double C—N bond. The amide group is twisted relative to the thio­urea fragment, forming a dihedral angle of 46.75 (11)°. In the crystal, inter­molecular N—H⋯S and C—H⋯O hydrogen bonds link the mol­ecules into a one-dimensional polymeric structure parallel to the c axis

1,1-Dibenzyl-3-(4-fluoro­benzo­yl)thio­urea

In the title compound, C22H19FN2OS, the 2-fluoro­benzoyl group adopts a trans conformation with respect to the thiono S atom across the N—C bond. In the crystal, inter­molecular N—H⋯S, C—H⋯S and C—H⋯O hydrogen bonds link the mol­ecules, forming a two-dimensional network parallel to (101)

3-(3-Meth­oxy­benzo­yl)-1,1-diphenyl­thio­urea

The thiono and carbonyl groups in the title compound, C21H18N2O2S, adopt an anti disposition with respect to the central C—N bond. The diphenyl­amine rings are twisted relative to each other by a dihedral angle of 82.55 (10)°. The 3-meth­oxy­benzoyl fragment is twisted relative to one of the diphenyl­amine rings, forming a dihedral angle of 74.04 (9)°. In the crystal, pairs of inter­molecular N—H⋯S hydrogen bonds link the mol­ecules into centrosymmetric dimers, forming columns parallel to the a axis

Pairwise Classification using Combination of Statistical Descriptors with Spectral Analysis Features for Recognizing Walking Activities

The advancement of sensor technology has provided valuable information for evaluating functional abilities in various application domains. Human activity recognition (HAR) has gained high demand from the researchers to undergo their exploration in activity recognition system by utilizing Micro-machine Electromechanical (MEMs) sensor technology. Tri-axial accelerometer sensor is utilized to record various kinds of activities signal placed at selected areas of the human bodies. The presence of high inter-class similarities between two or more different activities is considered as a recent challenge in HAR. The nt of incorrectly classified instances involving various types of walking activities could degrade the average accuracy performance. Hence, pairwise classification learning methods are proposed to tackle the problem of differentiating between very similar activities. Several machine learning classifier models are applied using hold out validation approach to evaluate the proposed method

ONE-AGAINST-ALL BINARIZATION CLASSIFICATION STRATEGY TO RECOGNIZE INTERCLASS SIMILARITIES ACTIVITIES FROM SEVERAL SENSOR POSITIONS

Prior knowledge in pervasive computing recently has garnered a great deal of attention due to the high demand in most applications in order to fulfil the human needs. Human Activity Recognition (HAR) has considered every bit unitary of the applications that are widely explored to provide the valuable information to the human. Small in size within the various smartphones, accelerometer sensor has utilized to undergo the HAR research. Current HAR is not only covered the simple daily activities but also, broadly covered the complex activities. Nevertheless, the existence of high interclass similarities activities tends to increase the level of incorrectly classified instances. Hence, this study demonstrates the binarization classification strategy to tackle the abovementioned issue for the activities with a high degree of similarities. Acceleration signal in the time domain is transformed into frequency terms for separating the signals between gravitational and body acceleration. Two different groups of features; statistical, and frequency analysis are extracted in order to increase the diversity in differentiating between stationary and locomotion activities. The problem complexity is simplified using the binarization strategy before the extracted subset is evaluated. One-Against-All (OAA) classification strategy is introduced to tackle the challenge in improving the accuracy for very similar activity. The proposed work significantly resulted with high accuracy performance, particularly in differentiating between the various high interclass similarities activities using two physical activity datasets; WISDM and PSRG

Assessing safety level of UTM campus based on safe city concepts

Safety is an important aspect in today's living, in urban city, residential area, and also in campus area. Several initiatives were introduced to increase the safety level, and to prevent crime from happening in the campus area, known as Safe City Concept. These initiatives included the Safe City Index, Crime Prevention Through Environmental Design (CPTED), behavioural model, safe city urban area, safe city of smart city, and resident safety assessment. Some of this initiative focus on urban city area, or residential, besides only focus on crime prevention and not focus on the assessment of safety level for campus area. This study aims to assess the safety level for campus area, with case study of UTM Campus. To assess the safety level, a set 4 indicators, which is crime, environment, public health and emergency response, with 9 sub-indicators was identified in this study. These indicators and sub-indicators used to determine the safety level of campus area based on the Safe City Concept. The analysis used is spatial analysis on the indicator, and using weighted criteria matrix to evaluate safety level for each building in UTM campus. The results show that most the buildings in UTM are in good and high safety level, with 65% of buildings score more than 70%. For buildings was detected with highest score of 95% of safety level, while 3 buildings score lowest percentage of 53.7%.These results indicated that UTM campus area is a safe area, based on the Safe City Concept. These results can help authorities to use these indicators of Safe City Concept to assess the education campus area safety level