Manipal University

About: Manipal University is a education organization based out in Manipal, Karnataka, India. It is known for research contribution in the topics: Population & Health care. The organization has 9525 authors who have published 11207 publications receiving 110687 citations.

Light-weight hashing method for user authentication in Internet-of-Things

Abstract: The goal of Internet-of-Things (IoT) is that every object across the globe be interconnected under the Internet Infrastructure. IoT is expanding its application domain to range from environmental monitoring to industrial automation thereby leading to vast research challenges. Vast presence of devices in the Internet has increased the possible challenges faced by devices and data. The devices communicate on a public channel that is more likely to be accessed by unauthorized users and disturb the privacy of genuine users. The existing solutions that ensure data authenticity and user privacy, use MD5 and SHA-family of hashing algorithms under digital signature schemes. These algorithms create a trade-off between the security concern and energy consumption of IoT devices. To provide an energy efficient authentication method, we propose a customized BLAKE2b hashing algorithm with modified elliptic curve digital signature scheme (ECDSA). The parameters considered for the evaluation of the proposed methods are signature generation time, signature verification time and hashing time. The experiments are conducted under client server model using Raspberry Pi-3. The proposed method has shown about 0.7–1.91% improvement in the signature generation time and 7.67–9.13 % improvement in signature verification time when compared with BLAKE2b based signature generation/verification. The proposed method is resistant to Man-in-the-Middle attack, Distributed DoS attack (DDoS), pre-image resistance, second pre-image resistance and collision resistance. Based on the performance obtained by the experiments, it can be inferred that the proposed scheme is feasible for resource-constrained IoT devices.

Preliminary studies on acute and subacute toxicity of an antidiabetic herbal preparation, Dianex

Abstract: Dianex, a polyherbal formulation intended to use for diabetic patients, has been screened for toxic effects. For acute toxicity studies, Dianex was administered orally in graded doses of 0.75-10 g/kg to the mice. For subacute toxicity studies, different doses of Dianex (1.0, 1.5 and 2.5 g/kg) were administered orally to the rats once daily for 30 days. Animals were observed for physiological and behavioural responses, mortality, food and water intake and body weight changes. Hematological evaluation was performed weekly. All the animals were sacrificed on 31st day and changes in organ weights and histology were examined. Biochemical studies were done in liver and serum. No mortality was observed up to 10 g/kg of Dianex in acute toxicity study. Daily administration of as high as 2.5 g/kg dose of Dianex did not result in any mortality or changes in gross behaviour, body weight, weight and histology of different organs or serum and liver biochemistry. However, significant increase in RBC count and hemoglobin level was observed in the treated animals at all doses. Other peripheral blood constituents were in the normal range. The dose of Dianex to produce significant antidiabetic activity in mouse, 0.25-0.5 g/kg, is much lower than the doses used in the present study. Therefore such doses may be safe for daily administration without causing any serious side effects.

Impact of passing mesenchymal stem cells through smaller bore size needles for subsequent use in patients for clinical or cosmetic indications

Abstract: Numerous preclinical and clinical studies have investigated the regenerative potential and the trophic support of mesenchymal stem cells (MSCs) following their injection into a target organ. Clinicians favor the use of smallest bore needles possible for delivering MSCs into vascular organs like heart, liver and spleen. There has been a concern that small needle bore sizes may be detrimental to the health of these cells and reduce the survival and plasticity of MSCs. In this report, we aimed to investigate the smallest possible bore size needle which would support the safe delivery of MSCs into various tissues for different clinical or cosmetic applications. To accomplish this we injected cells via needle sizes 24, 25 and 26 G attached to 1 ml syringe in the laboratory and collected the cells aseptically. Control cells were ejected via 1 ml syringe without any needle. Thereafter, the needle ejected cells were cultured and characterized for their morphology, attachment, viability, phenotypic expression, differentiation potential, cryopreservation and in vivo migration abilities. In the second phase of the study, cells were injected via 26 G needle attached to 1 ml syringe for 10 times. Similar phenotypic and functional characteristics were observed between ejected and control group of cells. MSCs maintained their cellular and functional properties after single and multiple injections. This study proves that 26 G bore size needles can be safely used to inject MSCs for clinical/therapeutics purposes.

N-Gram Assisted Youtube Spam Comment Detection

Abstract: This paper proposes a novel methodology for the detection of intrusive comments or spam on the video-sharing website - Youtube. We describe spam comments as those which have a promotional intent or those who deem to be contextually irrelevant for a given video. The prospects of monetisation through advertising on popular social media channels over the years has attracted an increasingly larger number of users. This has in turn led to to the growth of malicious users who have begun to develop automated bots, capable of large-scale orchestrated deployment of spam messages across multiple channels simultaneously. The presence of these comments significantly hurts the reputation of a channel and also the experience of normal users. Youtube themselves have tackled this issue with very limited methods which revolve around blocking comments that contain links. Such methods have proven to be extremely ineffective as Spammers have found ways to bypass such heuristics. Standard machine learning classification algorithms have proven to be somewhat effective but there is still room for better accuracy with new approaches. In this work, we attempt to detect such comments by applying conventional machine learning algorithms such as Random Forest, Support Vector Machine, Naive Bayes along with certain custom heuristics such as N-Grams which have proven to be very effective in detecting and subsequently combating spam comments.