A Negotiation Framework for the Cloud Management System using Similarity and Gale Shapely Stable Matching approach

( Rajkumar Rajavel ),( Mala Thangarathinam ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.6

One of the major issues in emerging cloud management system needs the efficient service level agreement negotiation framework, with an optimal negotiation strategy. Most researchers focus mainly on the atomic service negotiation model, with the assistance of the Agent Controller in the broker part to reduce the total negotiation time, and communication overhead to some extent. This research focuses mainly on composite service negotiation, to further minimize both the total negotiation time and communication overhead through the pre-request optimization of broker strategy. The main objective of this research work is to introduce an Automated Dynamic Service Level Agreement Negotiation Framework (ADSLANF), which consists of an Intelligent Third-party Broker for composite service negotiation between the consumer and the service provider. A broker consists of an Intelligent Third-party Broker Agent, Agent Controller and Additional Agent Controller for managing and controlling its negotiation strategy. The Intelligent third-party broker agent manages the composite service by assigning its atomic services to multiple Agent Controllers. Using the Additional Agent Controllers, the Agent Controllers manage the concurrent negotiation with multiple service providers. In this process, the total negotiation time value is reduced partially. Further, the negotiation strategy is optimized in two stages, viz., Classified Similarity Matching (CSM) approach, and the Truncated Negotiation Group Gale Shapely Stable Matching (TNGGSSM) approach, to minimize the communication overhead.

Effect of edaravone in diabetes mellitus-induced nephropathy in rats

Varatharajan, Rajavel,Lim, Li Xin,Tan, Kelly,Tay, Chai Sze,Teoh, Yi Leng,Akhtar, Shaikh Sohrab,Rupeshkumar, Mani,Chung, Ivy,Abdullah, Nor Azizan,Banik, Urmila,Dhanaraj, Sokkalingam A.,Balakumar, Pitch The Korean Society of Pharmacology 2016 The Korean Journal of Physiology & Pharmacology Vol.20 No.4

Edaravone, a synthetic-free radical scavenger, has been reported to reduce ischemia-reperfusion-induced renal injury by improving tubular cell function, and lowering serum creatinine and renal vascular resistance. The present study investigated the effect of edaravone in diabetes mellitus-induced nephropathy in rats. A single administration of streptozotocin (STZ, 55 mg/kg, i .p.) was employed to induce diabetes mellitus in rats. The STZ-administered diabetic rats were allowed for 10 weeks to develop nephropathy. Mean body weight, lipid alteration, renal functional and histopathology were analysed. Diabetic rats developed nephropathy as evidenced by a significant increase in serum creatinine and urea, and marked renal histopathological abnormalities like glomerulosclerosis and tubular cell degeneration. The kidney weight to body weight ratio was increased. Moreover, diabetic rats showed lipid alteration as evidenced by a significant increase in serum triglycerides and decrease in serum high-density lipoproteins. Edaravone (10 mg/kg, i .p., last 4-weeks) treatment markedly prevented the development of nephropathy in diabetic rats by reducing serum creatinine and urea and preventing renal structural abnormalities. In addition, its treatment, without significantly altering the elevated glucose level in diabetic rats, prevented diabetes mellitus-induced lipid alteration by reducing serum triglycerides and increasing serum high-density lipoproteins. Interestingly, the renoprotective effect of edaravone was comparable to that of lisinopril (5 mg/kg, p.o, 4 weeks, standard drug). Edaravone prevented renal structural and functional abnormalities and lipid alteration associated with experimental diabetes mellitus. Edaravone has a potential to prevent nephropathy without showing an anti-diabetic action, implicating its direct renoprotection in diabetic rats.

Anti-pyretic and anti-inflammatory activity of chloroform extract of Croton roxburghii in standard animal models

Sivakumar, T,Rajavel, R,Karthikeyan, D,Duraisamy, R,Srinivasan, K,Kumar, S Suresh,Karki, Subhas S Kyung Hee Oriental Medicine Research Center 2008 Oriental pharmacy and experimental medicine Vol.8 No.3

The chloroform extract of Croton roxburghii (Family: Euphorbiaceae) was evaluated for its antipyretic effects in Brewer's yeast induced hyperthermia in rats. The anti-inflammatory effect of the Croton roxburghii was also evaluated by using carrageenan, dextran, histamine, serotonin induced rat paw oedema and cotton pellet induced granuloma (chronic) models in rats. The chloroform extract of Croton roxburghii (CECR) exhibited significant anti-pyretic and anti-inflammatory effect at the dose 50, 100 and 200 mg/kg. Maximum inhibition (55.32%) was notedat the dose of 200 mg/kg after 3 h of drug treatment in carrageenan induced paw oedema, whereas the Indomethacin (standard drug) produced 61.33% of inhibition. The extract exhibited significant anti-inflammatory activity in dextran induced paw edema in a dose dependent manner. In the chronic model (cotton pellet induced granuloma) the CECR (200 mg/kg) and Indomethacin (10 mg/kg) showed decreased formation of granuloma tissue by 52.32% and 56.32% respectively. The extract also exhibited a significant antipyretic response in Brewer's yeast induced pyrexia in rats. Thus, the present study revealed that the CECR exhibited significant antipyretic and anti-inflammatory activity in the tested animal models.

Activity recognition of stroke-affected people using wearable sensor

Anusha David,Rajavel Ramadoss,Amutha Ramachandran,Shoba Sivapatham 한국전자통신연구원 2023 ETRI Journal Vol.45 No.6

Stroke is one of the leading causes of long-term disability worldwide, placing huge burdens on individuals and society. Further, automatic human activity recognition is a challenging task that is vital to the future of healthcare and physical therapy. Using a baseline long short-term memory recurrent neural network, this study provides a novel dataset of stretching, upward stretching, flinging motions, hand-to-mouth movements, swiping gestures, and pouring motions for improved model training and testing of stroke-affected patients. A MATLAB application is used to output textual and audible prediction results. A wearable sensor with a triaxial accelerometer is used to collect preprocessed real-time data. The model is trained with features extracted from the actual patient to recognize new actions, and the recognition accuracy provided by multiple datasets is compared based on the same baseline model. When training and testing using the new dataset, the baseline model shows recognition accuracy that is 11% higher than the Activity Daily Living dataset, 22% higher than the Activity Recognition Single Chest- Mounted Accelerometer dataset, and 10% higher than another real-world dataset.

Novel Results on Stability Analysis of Neutral-type Neural Networks with Additive Time-varying Delay Components and Leakage Delay

R. Samidurai,S. Rajavel,R. Sriraman,Ahmed Alsaedi,Fuad E. Alsaadi,Jinde Cao 제어·로봇·시스템학회 2017 International Journal of Control, Automation, and Vol.15 No.4

The objective of this paper is to analyze the stability analysis of neutral-type neural networks with additivetime-varying delay and leakage delay. By constructing a suitable augmented Lyapunov-Krasovskii functionalwith triple and four integral terms, some new stability criteria are established in terms of linear matrix inequalities,which is easily solved by various convex optimization techniques. More information of the lower and upper delaybounds of time-varying delays are used to derive the stability criteria, which can lead less conservative results. Theobtained conditions are expressed with linear matrix inequalities (LMIs) whose feasible can be checked easily byMATLAB LMI control toolbox. Finally, two numerical examples are given to demonstrate the effectiveness of theproposed method.

Anti-pyretic and anti-inflammatory activity of chloroform extract of Croton roxburghii in standard animal models

T Sivakumar,R Rajavel,D Karthikeyan,R Duraisamy,K Srinivasan,S Suresh kumar,Subhas S Karki 경희대학교 융합한의과학연구소 2008 Oriental Pharmacy and Experimental Medicine Vol.8 No.3

The chloroform extract of Croton roxburghii (Family: Euphorbiaceae) was evaluated for its antipyretic effects in Brewer’s yeast induced hyperthermia in rats. The anti-inflammatory effect of the Croton roxburghii was also evaluated by using carrageenan, dextran, histamine, serotonin induced rat paw oedema and cotton pellet induced granuloma (chronic) models in rats. The chloroform extract of Croton roxburghii (CECR) exhibited significant anti-pyretic and antiinflammatory effect at the dose 50, 100 and 200 mg/kg. Maximum inhibition (55.32%) was noted at the dose of 200 mg/kg after 3 h of drug treatment in carrageenan induced paw oedema, whereas the Indomethacin (standard drug) produced 61.33% of inhibition. The extract exhibited significant anti-inflammatory activity in dextran induced paw edema in a dose dependent manner. In the chronic model (cotton pellet induced granuloma) the CECR (200 mg/kg) and Indomethacin (10 mg/kg) showed decreased formation of granuloma tissue by 52.32% and 56.32% respectively. The extract also exhibited a significant antipyretic response in Brewer’s yeast induced pyrexia in rats. Thus, the present study revealed that the CECR exhibited significant antipyretic and anti-inflammatory activity in the tested animal models. The chloroform extract of Croton roxburghii (Family: Euphorbiaceae) was evaluated for its antipyretic effects in Brewer’s yeast induced hyperthermia in rats. The anti-inflammatory effect of the Croton roxburghii was also evaluated by using carrageenan, dextran, histamine, serotonin induced rat paw oedema and cotton pellet induced granuloma (chronic) models in rats. The chloroform extract of Croton roxburghii (CECR) exhibited significant anti-pyretic and antiinflammatory effect at the dose 50, 100 and 200 mg/kg. Maximum inhibition (55.32%) was noted at the dose of 200 mg/kg after 3 h of drug treatment in carrageenan induced paw oedema, whereas the Indomethacin (standard drug) produced 61.33% of inhibition. The extract exhibited significant anti-inflammatory activity in dextran induced paw edema in a dose dependent manner. In the chronic model (cotton pellet induced granuloma) the CECR (200 mg/kg) and Indomethacin (10 mg/kg) showed decreased formation of granuloma tissue by 52.32% and 56.32% respectively. The extract also exhibited a significant antipyretic response in Brewer’s yeast induced pyrexia in rats. Thus, the present study revealed that the CECR exhibited significant antipyretic and anti-inflammatory activity in the tested animal models.

Improving classification accuracy of cancer types using parallel hybrid feature selection on microarray gene expression data

Lokeswari Venkataramana,Shomona Gracia Jacob,Rajavel Ramadoss,Dodda Saisuma,Dommaraju Haritha,Kunthipuram Manoja 한국유전학회 2019 Genes & Genomics Vol.41 No.11

Background Data mining techniques are used to mine unknown knowledge from huge data. Microarray gene expression (MGE) data plays a major role in predicting type of cancer. But as MGE data is huge in volume, applying traditional data mining approaches is time consuming. Hence parallel programming frameworks like Hadoop, Spark and Mahout are necessary to ease the task of computation. Objective Not all the gene expressions are necessary in prediction, it is very essential to select important genes for improving classification accuracy. So feature selection algorithms are parallelized and executed on Spark framework to eliminate unnecessary genes and identify only predictive genes in very less time without affecting prediction accuracy. Methods Parallelized hybrid feature selection (HFS) method is proposed to serve the purpose. This method includes parallelized correlation feature subset selection followed by rank-based feature selection methods. The selected subset of genes is evaluated using parallel classification algorithms. The accuracy values obtained are compared with existing rank-weight feature selection, parallelized recursive feature selection methods and also with the values obtained by executing parallelized HFS on DistributedWekaSpark. Results The classification accuracy obtained with the proposed parallelized HFS method is 97% and 79% for gastric cancer and childhood leukemia respectively. The proposed parallelized HFS method produced ~ 4% to ~ 15% improvement in classification accuracy when compared with previous methods. Conclusion The results reveal the fact that the proposed parallelized feature selection algorithm is scalable to growing medical data and predicts cancer sub-types in lesser time with higher accuracy.