Ramesh Sah - Academia.edu (original) (raw)

Papers by Ramesh Sah

Nepalese Veterinary Journal, 2018

Study was carried out to explore the management system of pig including health care and marketing... more Study was carried out to explore the management system of pig including health care and marketing, adopted by farmers in Hattikharka of Dhankuta and Phakchamara of Terhathum districts. A semi-structured questionnaire was distributed to a total of 200 pig farmers of selected sites. Farmers were surveyed to acquire information on management systems of pig such as, housing, breeding, feeding, health care and marketing. Majority of pig farmers kept Pakhribas black pig, chawanche and their crosses. Population of local pigs was found three times more than improved breeds in both sites. Generally, 1-5 pigs were raised by a farmer. Bamboo, wood and mud were mostly used as housing material. Stall feeding system was adopted by most of the farmers. Locally available feeding materials such as, rice bran, rice polish, maize, sisno, karklo, swill feeding, leftover food and green grasses like rayo, latte sag, kande jhar, twigs of fodder, khubhindo, pumpkin etc were mostly used for pig. Rai, Sherpa...

2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN)

arXiv (Cornell University), Jul 12, 2021

Stress detection and monitoring is an active area of research with important implications for the... more Stress detection and monitoring is an active area of research with important implications for the personal, professional, and social health of an individual. Current approaches for affective state classification use traditional machine learning algorithms with features computed from multiple sensor modalities. These methods are data-intensive and rely on handcrafted features which impede the practical applicability of these sensor systems in daily lives. To overcome these shortcomings, we propose a novel Convolutional Neural Network (CNN) based stress detection and classification framework without any feature computation using data from only one sensor modality. Our method is competitive and outperforms current state-of-the-art techniques and achieves a classification accuracy of 92.85% and an f 1 score of 0.89. Through our leave-one-subject-out analysis, we also show the importance of personalizing stress models.

2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC)

Detecting stress from wearable sensor data enables those struggling with unhealthy stress coping ... more Detecting stress from wearable sensor data enables those struggling with unhealthy stress coping mechanisms to better manage their stress. Previous studies have investigated how mechanisms for detecting stress from sensor data can be optimized, comparing alternative algorithms and approaches to find the best possible outcome. One strategy to make these mechanisms more accessible is to reduce the number of sensors that wearable devices must support. Reducing the number of sensors will enable wearable devices to be a smaller size, require less battery, and last longer, making use of these wearable devices more accessible. To progress towards this more convenient stress detection mechanism, we investigate how learning algorithms perform on singular modalities and compare the outcome with results from multiple modalities. We found that singular modalities performed comparably or better than combined modalities on two stress-detection datasets, suggesting that there is promise for detecting stress with fewer sensor requirements. From the four modalities we tested, acceleration, blood volume pulse, and electrodermal activity, we saw acceleration and electrodermal activity to stand out in a few cases, but all modalities showed potential. Our results are acquired from testing with random holdout and leave-one-subject-out validation, using several machine learning techniques. Our results can inspire work on optimizing stress detection with singular modalities to make the benefits of these detection mechanisms more convenient.

2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2019

Recent advances in machine learning and deep neural networks have led to the realization of many ... more Recent advances in machine learning and deep neural networks have led to the realization of many important applications in the area of personalized medicine. Whether it is detecting activities of daily living or analyzing images for cancerous cells, machine learning algorithms have become the dominant choice for such emerging applications. In particular, the state-of-the-art algorithms used for human activity recognition (HAR) using wearable inertial sensors utilize machine learning algorithms to detect health events and to make predictions from sensor data. Currently, however, there remains a gap in research on whether or not and how activity recognition algorithms may become the subject of adversarial attacks. In this paper, we take the first strides on (1) investigating methods of generating adversarial example in the context of HAR systems; (2) studying the vulnerability of activity recognition models to adversarial examples in feature and signal domain; and (3) investigating the effects of adversarial training on HAR systems. We introduce Adar11Software code and experimental data for Adar are available online at https://github.com/rameshKrSah/Adar., a novel computational framework for optimization-driven creation of adversarial examples in sensor-based activity recognition systems. Through extensive analysis based on real sensor data collected with human subjects, we found that simple evasion attacks are able to decrease the accuracy of a deep neural network from 95.1% to 3.4% and from 93.1% to 16.8% in the case of a convolutional neural network. With adversarial training, the robustness of the deep neural network increased on the adversarial examples by 49.1% in the worst case while the accuracy on clean samples decreased by 13.2%.

ArXiv, 2020

Machine learning has increasingly become the most used approach for inference and decision making... more Machine learning has increasingly become the most used approach for inference and decision making in wearable sensor systems. However, recent studies have found that machine learning systems are easily fooled by the addition of adversarial perturbation to their inputs. What is more interesting is that the adversarial examples generated for one machine learning system can also degrade the performance of another. This property of adversarial examples is called transferability. In this work, we take the first strides in studying adversarial transferability in wearable sensor systems, from the following perspectives: 1) Transferability between machine learning models, 2) Transferability across subjects, 3) Transferability across sensor locations, and 4) Transferability across datasets. With Human Activity Recognition (HAR) as an example sensor system, we found strong untargeted transferability in all cases of transferability. Specifically, gradient-based attacks were able to achieve hig...

2020 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), 2020

Alcohol related disorder has increasingly become a serious public health issue. Stress detection ... more Alcohol related disorder has increasingly become a serious public health issue. Stress detection and intervention is considered a key element in a treatment strategy towards preventing alcohol dependent individuals from relapsing. In this paper, we present a proof-of-concept approach to study the usability of a wearable device and viability of a mobile health application to prevent alcohol relapse by detecting moments of stress and providing adaptive interventions in real-time.

JMIR Formative Research, 2021

Background Previous research has highlighted the role of stress in substance misuse and addiction... more Background Previous research has highlighted the role of stress in substance misuse and addiction, particularly for relapse risk. Mobile health interventions that incorporate real-time monitoring of physiological markers of stress offer promise for delivering tailored interventions to individuals during high-risk states of heightened stress to prevent alcohol relapse. Before such interventions can be developed, measurements of these processes in ambulatory, real-world settings are needed. Objective This research is a proof-of-concept study to establish the feasibility of using a wearable sensor device to continuously monitor stress in an ambulatory setting. Toward that end, we first aimed to examine the quality of 2 continuously monitored physiological signals—electrodermal activity (EDA) and heart rate variability (HRV)—and show that the data follow standard quality measures according to the literature. Next, we examined the associations between the statistical features extracted f...

Veterinary Parasitology: Regional Studies and Reports, 2019

The study was planned to apply the PCR method for detection of T. gondii infection in sheep, goat... more The study was planned to apply the PCR method for detection of T. gondii infection in sheep, goats and cattle aborted fetuses from Mymensingh, Bangladesh. A total of 58 fetal tissue samples (brain, liver, heart, skeletal muscle and placenta) of sheep (5), goats (5) and cattle (2) were selected for study. Aborted fetuses were taken from serologically positive mothers by indirect ELISA. Among them 24 and 34 samples were subjected for PCR assay by using TgB1 and TgTox4 primers respectively. DNA fragments were visualized under UV illumination after gel run. The results demonstrated 15.52% tissue samples from sheep and goat aborted fetuses were positive for T. gondii parasite. Among different tissue samples, brain, liver and heart showed presence of T. gondii parasite. None of tissue samples showed positive in case of cattle. The results of the PCR exhibited that T. gondii infection might be considered as one of the major causative agents for abortion in ewes and does. Further studies are needed to improve our knowledge on different genotypes of T. gondii that infect sheep, goat and cattle population in Bangladesh.

Veterinary Parasitology: Regional Studies and Reports, 2018