College of Engineering, Pune

About: College of Engineering, Pune is a based out in . It is known for research contribution in the topics: Sliding mode control & Control theory. The organization has 4264 authors who have published 3492 publications receiving 19371 citations. The organization is also known as: COEP.

Traditional Medicine to Modern Pharmacogenomics: Ayurveda Prakriti Type and CYP2C19 Gene Polymorphism Associated with the Metabolic Variability

Abstract: Traditional Indian medicine—Ayurveda—classifies the human population into three major constituents or Prakriti known as Vata, Pitta and Kapha types. Earlier, we have demonstrated a proof of concept to support genetic basis for Prakriti. The descriptions in Ayurveda indicate that individuals with Pitta Prakriti are fast metabolizers while those of Kapha Prakriti are slow metabolizers. We hypothesized that different Prakriti may have different drug metabolism rates associated with drug metabolizing enzyme (DME) polymorphism. We did CYP2C19 (Phase I DME) genotyping in 132 unrelated healthy subjects of either sex by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) technique. We observed significant association between CYP2C19 genotype and major classes of Prakriti types. The extensive metabolizer (EM) genotype (*1/*1, *1/*2, *1/*3) was found to be predominant in Pitta Prakriti (91%). Genotype (*1/*3) specific for EM group was present only in Pitta Prakriti. Poor metabolizer (PM) genotype (*2/*2, *2/*3, *3/*3) was highest (31%) in Kapha Prakriti when compared with Vata (12%) and Pitta Prakriti (9%). Genotype (*2/*3) which is typical for PM group was significant in Kapha Prakriti (odds ratio = 3.5, P = .008). We observed interesting correlations between CYP2C19 genotypes and Prakriti with fast and slow metabolism being one of the major distinguishing and differentiating characteristics. These observations are likely to have significant impact on phenotype-genotype correlation, drug discovery, pharmacogenomics and personalized medicine.

Intrusion Detection System Using Bagging Ensemble Method of Machine Learning

Abstract: Intrusion detection system is widely used to protect and reduce damage to information system. It protects virtual and physical computer networks against threats and vulnerabilities. Presently, machine learning techniques are widely extended to implement effective intrusion detection system. Neural network, statistical models, rule learning, and ensemble methods are some of the kinds of machine learning methods for intrusion detection. Among them, ensemble methods of machine learning are known for good performance in learning process. Investigation of appropriate ensemble method is essential for building effective intrusion detection system. In this paper, a novel intrusion detection technique based on ensemble method of machine learning is proposed. The Bagging method of ensemble with REPTree as base class is used to implement intrusion detection system. The relevant features from NSL_KDD dataset are selected to improve the classification accuracy and reduce the false positive rate. The performance of proposed ensemble method is evaluated in term of classification accuracy, model building time and False Positives. The experimental results show that the Bagging ensemble with REPTree base class exhibits highest classification accuracy. One advantage of using Bagging method is that it takes less time to build the model. The proposed ensemble method provides competitively low false positives compared with other machine learning techniques.

Internet of Things (IoT) for Precision Agriculture Application

Abstract: Internet is experiencing a very explosive growth nowadays with the amount of the devices connecting to it. Earlier we had only personal computers (pCs) and Mobile handset connected to internet but now with Internet of Things i.e. IoT concept of connecting things with internet, millions of device are connecting with it. This development of IoT leads to the idea of machine to machine communication which means that two machines can communicate to each other and also all the data which was previously with private server can now is available on internet so the user can access it remotely. Application of IoT is feasible in almost all industries particularly where speed of communication is not an issue. This paper proposes the application of cloud based IoT in the agriculture domain. Precision agriculture is basically a concept which insists to provide right amount of resources at and for exact duration of time. These resources can be any things such as water, light, pesticides etc. To implement precision agriculture the benefits of IOT has been utilized in the proposed paper. The fundamental idea is to sense all the required parameter from the agriculture field and take required decision to control the actuator. These agriculture parameters are Soil Moisture, Temperature & Relative Humidity around plant, Light intensity. Based on the reading sensed by the sensor suitable action is taken i.e. irrigation valve is actuated based on soil moisture readings, valve for fogger (for spraying water droplet) is actuated based on the Relative humidity(RH) readings etc. This paper proposed the development of the sensor node capable of measuring all these parameter and creating the actuation signal for all the actuator. On top of that sensor nodes are also capable of sending this data to cloud. An Android application is also developed in order to access all these agricultural parameter.

CL-IoT: cross-layer Internet of Things protocol for intelligent manufacturing of smart farming

Abstract: Internet of Things (IoT) for Intelligent Manufacturing of Smart Farming gained significant attention from researchers to automate various farming applications called Smart Farming (SF). The sensors and actuators deployed across the farm using which farmers receive periodic farm information related to temperature, soil moisture, light intensity, and water used, etc. The clustering-based methods are proven energy-efficient solutions for Wireless Sensor Networks (WSNs). However, by considering long-distance communications and scalable networks of IoT enabled SF; the present clustering solutions cannot be feasible and having higher delay and latency for various SF applications. To focus on requirements SF applications, an efficient and scalable protocol for remote monitoring and decision making of farms in rural regions called CL-IoT protocol proposed. A cross-layer-based clustering and routing algorithms have designed to reduce network communication delay, latency, and energy consumption. The cross-layer-based optimal Cluster Head (CH) selection solution proposed to overcome the energy asymmetry problem in WSN. The parameters of different layers like a physical, medium access control (MAC), and network layer of each sensor used to evaluate and select optimal CH and efficient data transmission. The nature-inspired algorithm proposed with a novel probabilistic decision rule functions as a fitness function to discover the optimal route for data transmission. The performance of the CL-IoT protocol analyzed using NS2 by considering the energy-efficiency, computational-efficiency, and QoS-efficiency factors. Compared to state-of-art IoT-based farming methods, the CL-IoT reduces energy consumption, communication overhead, and end-to-end delay up to a certain extent and maximizes the network throughput.