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All Journal Proceeding of the Electrical Engineering Computer Science and Informatics
Sonali Agarwal
Indian Institute of Information Technology
Computer Science & IT Electrical & Electronics Engineering

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Classification of Physiological Signals for Emotion Recognition using IoT Sadhana Tiwari; Sonali Agarwal; Muhammad Syafrullah; Krisna Adiyarta
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1943

Abstract

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.
Person tracking with non-overlapping multiple cameras Sanjay Kumar Sonbhadra; Sonali Agarwal; Muhammad Syafrullah; Krisna Adiyarta
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2049

Abstract

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.
Email classification via intention-based segmentation Sanjay Kumar Sonbhadra; Sonali Agarwal; Muhammad Syafrullah; Krisna Adiyarta
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2084

Abstract

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.
Aggressive driving behaviour classification using smartphone's accelerometer sensor Sanjay Kumar Sonbhadra; Sonali Agarwal; Muhammad Syafrullah; Krisna Adiyarta
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2091

Abstract

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%.
Co-Authors Krisna Adiyarta Mohammad Syafrullah Sadhana Tiwari Sanjay Kumar Sonbhadra