Testing Big Data Applications

Today big data has become the basis of discussion for the organizations. The big task associated with big data stream is coping with its various challenges and performing the appropriate testing for the optimal analysis of the data which may benefit the processing of various activities, especially from a business perspective. Big data term follows the massive volume of data, (might be in units of petabytes or exabytes) exceeding the processing and analytical capacity of the conventional systems and thereby raising the need for analyzing and testing the big data before applications can be put into use. Testing such huge data coming from the various number of sources like the internet, smartphones, audios, videos, media, etc. is a challenge itself. The most favourable solution to test big data follows the automated/programmed approach. This paper outlines the big data characteristics, and various challenges associated with it followed by the approach, strategy, and proposed framework for testing big data applications

Classification of Physiological Signals for Emotion Recognition using IoT

Emotion recognition gains huge popularity now a days. Physiological signals provides an appropriate way to detect human emotion with the help of IoT. In this paper, a novel system is proposed which is capable of determining the emotional status using physiological parameters, including design specification and software implementation of the system. This system may have a vivid use in medicine (especially for emotionally challenged people), smart home etc. Various Physiological parameters to be measured includes, heart rate (HR), galvanic skin response (GSR), skin temperature etc. To construct the proposed system the measured physiological parameters were feed to the neural networks which further classify the data in various emotional states, mainly in anger, happy, sad, joy. This work recognized the correlation between human emotions and change in physiological parameters with respect to their emotion

Email classification via intention-based segmentation

Email is the most popular way of personal and official communication among people and organizations. Due to untrusted virtual environment, email systems may face frequent attacks like malware, spamming, social engineering, etc. Spamming is the most common malicious activity, where unsolicited emails are sent in bulk, and these spam emails can be the source of malware, waste resources, hence degrade the productivity. In spam filter development, the most important challenge is to find the correlation between the nature of spam and the interest of the users because the interests of users are dynamic. This paper proposes a novel dynamic spam filter model that considers the changes in the interests of users with time while handling the spam activities. It uses intention-based segmentation to compare different segments of text documents instead of comparing them as a whole. The proposed spam filter is a multi-tier approach where initially, the email content is divided into segments with the help of part of speech (POS) tagging based on voices and tenses. Further, the segments are clustered using hierarchical clustering and compared using the vector space model. In the third stage, concept drift is detected in the clusters to identify the change in the interest of the user. Later, the classification of ham emails into various categories is done in the last stage. For experiments Enron dataset is used and the obtained results are promising

Person tracking with non-overlapping multiple cameras

Monitoring and tracking of any target in a surveillance system is an important task. When these targets are human then this problem comes under person identification and tracking. At present, large scale smart video surveillance system is an essential component for any commercial or public campus. Since field of view (FOV) of a camera is limited; for large area monitoring, multiple cameras are needed at different locations. This paper proposes a novel model for tracking a person under multiple non-overlapping cameras. It builds the reference signature of the person at the beginning of the tracking system to match with the upcoming signatures captured by other cameras within the specified area of observation with the help of trained support vector machine (SVM) between two cameras. For experiments, wide area re-identification dataset (WARD) and a real-time scenario have been used with color, shape and texture features for person's re-identification

Aggressive driving behaviour classification using smartphone's accelerometer sensor

Aggressive driving is the most common factor of road accidents, and millions of lives are compromised every year. Early detection of aggressive driving behaviour can reduce the risks of accidents by taking preventive measures. The smartphone's accelerometer sensor data is mostly used for driving behavioural detection. In recent years, many research works have been published concerning to behavioural analysis, but the state of the art shows that still, there is a need for a more reliable prediction system because individually, each method has it's own limitations like accuracy, complexity etc. To overcome these problems, this paper proposes a heterogeneous ensemble technique that uses random forest, artificial neural network and dynamic time wrapping techniques along with weighted voting scheme to obtain the final result. The experimental results show that the weighted voting ensemble technique outperforms to all the individual classifiers with average marginal gain of 20%

Client Side Channel State Information Estimation for MIMO Communication

Multiple-input multiple-output (MIMO) system relies on a feedback signal which holds channel state information (CSI) from receiver to the transmitter to do pre-coding for achieving better performance. However, sending CSI feedback at each time stamp for long duration is an overhead in the communication system. We introduce a deep reinforcement learning based channel estimation at receiver end for single user MIMO communication without CSI feedback. In this paper we propose to train the receiver with known pilot signals to analyse the stochastic behaviour of the wireless channel. The simulation on MIMO channel with additive white Gaussian noise (AWGN) shows that our proposed method can learn the different characteristics affecting the channel with limited number of pilot signals. Extensive experiments show that the proposed method was able to outperform the existing state-of-the-art end to end reinforcement learning method. The results demonstrate that the proposed method learns and predicts the stochastic time varying channel characteristic accurately at receiver’s end

Gesture recognition by learning local motion signatures using smartphones

In recent years, gesture or activity recognition is an important area of research for the modern health care system. An activity is recognized by learning from human body postures and signatures. Presently all smartphones are equipped with accelerometer and gyroscopes sensors, and the reading of these sensors can be utilized as an input to a classifier to predict the human activity. Although the human activity recognition gained a notable scientific interest in recent years, still accuracy, scalability and robustness need significant improvement to cater as a solution of most of the real world problems. This paper aims to fill the identified research gap and proposes Grid Search based Logistic Regression and Gradient Boosting Decision Tree multistage prediction model. UCI-HAR dataset has been used to perform Gesture recognition by learning local motion signatures. The proposed approach exhibits improved accuracy over preexisting techniques concerning to human activity recognition

Penggunaan Metode AHP Dan TOPSIS Untuk Pemilihan Dokter Terbaik

The hospital is the only one who handles the patients. Professional and qualified doctors can improve the quality of health services in a health institution. The problem is the existence of subjective assessments between doctors assessed with the appraiser, so that doctors who really deserve the predicate as the best doctor is often not chosen as the best doctor And the absence of information systems that can be used to determine the performance of each physician. The purpose of this research is to create a decision support system for the best performance determination doctor at the Berkah Jaya Medika Indramayu Clinic. The methods used in this acceptance decision support system use the method of Analytical Hierarcy Process (AHP) and Technique for others reference by similarity to ideal solution (TOPSIS). The results of this research is a Web application support system that is based on the decision to provide results in the form of each physicia

Implementation of Image Segmentation Techniques to Detect MRI Glioma Tumour

Image identification to detect a tumour needs several stages of image processing along with identifying analysis. To get an accurate segmentation of the tumour contour and to identify brain tumour based on brain magnetic resonance imaging (MRI), a suitable techniques and stages of image processing are required to be applied. One technique of mid-level image processing became an objective this work. The objective of the study is to segment the boundary of tumour by applying the Modification of Region Fitting (MRF) method in term of data fitting. The performance of the Region Scalable Fitting (RSF) method and Modified Region Scalable Fitting (MRSF) is evaluated by comparing the number of iterations. As the result, the MRF method has successfully segmented the initial region of braintumour images

IMPLEMENTASI DATA MINING PADA DATA PRODUKSI ROTI MANIS CAL DONAT HUSADA BERBASIS JAVA WEB MENGGUNAKAN ALGORITMA APRIORI

Cal Donat membuat list kebutuhan gerai dengan menginput data produksi di file.xls yang berbeda setiap harinya. Hal tersebut mengakibatkan banyaknya kendala yang terjadi seperti: file yang tidak dapat dibuka, selalu membuat file baru untuk menginput data produksi, terdapat banyak file yang tersimpan, sulitnya menganalisa dan memprediksi data, harus membuka banyak file untuk melihat perbedaan kebutuhan dan kendala-kendala lainnya. Dengan demikian penulis melakukan analisis data yang berfokus pada salah satu gerai Cal Donat yaitu Cal Donat Husada dengan data roti manis dan minuman. Analisis ini akan menggunakan teknik pengolahan data atau data mining dengan metode association rule dan apriori sebagai algoritma untuk mencari keterkaitan antar item dan mendapatkan rules atau aturan yang dapat menyelesaikan permasalahan yang ada di Caldonat Husada. Hasil yang diperoleh yaitu terbentuknya 3 itemset yang terdiri dari: 1; 2; 5 yang dapat didefinisikan sebagai berikut: (1 = Keju; 2 = Coklat; 5 = Pisang Keju) dengan minimum support 70%, maka Nilai Confidence dapat diperhitungkan. Uji coba untuk Nilai Confidence yaitu 70%, dari 3 itemset yang terdiri dari (1 = Keju; 2 = Coklat; 5 = Pisang Keju), maka terbentuknya 6 pola perhitungan confidence. Dari nilai confidence yang sudah terbentuk, maka dapat dihitung nilai lift ratio yang menunjukkan bahwa Lift Ratio > 1, maka hubungan positif yang dimaknai [Coklat, Pisang Coklat] dan [Keju] lebih sering muncul bersamaa