Medicines Used by the People of Anaikaraipatti in Madurai for Children and Elders

Siddha medicine are used by the people traditionally to cure disease. Usually the people live around village don’t spend money for medicines. Instead they use the leaves and other medicinal herbs which grow in their village. By knowing the medicinal properties, they cure various diseases. They cure many diseases using this medicine. This type of Siddha medicines is still in use among village people. So, this Article is about the medicinal herbs and how these herbs cure the disease among the children and elders

Overhead Management Strategies for Internet of Things Devices

Overhead (time and energy) management is paramount for IoT edge devices considering their typically resource-constrained nature. In this thesis we present two contributions for lowering resource consumption of IoT devices. The first contribution is minimizing the overhead of the Transport Layer Security (TLS) authentication protocol in the context of IoT networks by selecting a lightweight cipher suite configuration. TLS is the de facto authentication protocol for secure communication in Internet of Things (IoT) applications. However, the processing and energy demands of this protocol are the two essential parameters that must be taken into account with respect to the resource-constraint nature of IoT devices. For the first contribution, we study these parameters using a testbed in which an IoT board (Cypress CYW43907) communicates with a server over an 802.11 wireless link. Although TLS supports a wide-array of cipher suites, in this paper we focus on DHE RSA, ECDHE RSA, and ECDHE ECDSA, which are among the most popular ciphers used due to their robustness. Our studies show that ciphers using Elliptic Curve Diffie Hellman (ECDHE) key exchange are considerably more efficient than ciphers using Diffie Hellman (DHE). Furthermore, ECDSA signature verification consumes more time and energy than RSA signature verification for ECDHE key exchange. This study helps IoT designers choose an appropriate TLS cipher suite based on application demands, computational capabilities, and energy resources available. The second contribution of this thesis is deploying supervised machine learning anomaly detection algorithms on an IoT edge device to reduce data transmission overhead and cloud storage requirements. With continuous monitoring and sensing, millions of Internet of Things sensors all over the world generate tremendous amounts of data every minute. As a result, recent studies start to raise the question as whether to send all the sensing data directly to the cloud (i.e., direct transmission), or to preprocess such data at the network edge and only send necessary data to the cloud (i.e., preprocessing at the edge). Anomaly detection is particularly useful as an edge mining technique to reduce the transmission overhead in such a context when the frequently monitored activities contain only a sparse set of anomalies. This paper analyzes the potential overhead-savings of machine learning based anomaly detection models on the edge in three different IoT scenarios. Our experimental results prove that by choosing the appropriate anomaly detection models, we are able to effectively reduce the total amount of transmission energy as well as minimize required cloud storage. We prove that Random Forest, Multilayer Perceptron, and Discriminant Analysis models can viably save time and energy on the edge device during data transmission. K-Nearest Neighbors, although reliable in terms of prediction accuracy, demands exorbitant overhead and results in net time and energy loss on the edge device. In addition to presenting our model results for the different IoT scenarios, we provide guidelines for potential model selections through analysis of involved tradeoffs such as training overhead, prediction overhead, and classification accuracy

Non-Invasive Biomarkers for the Diagnosis of Endometriosis and Polycystic Ovary Syndrome

Benign gynecological disorders can affect a high percentage of women of reproductive age, ages 15-44 years. These conditions can affect the lifestyle of the individual and can be associated with infertility. The gold standard to identify and diagnose endometriosis requires invasive surgical procedures, while the Rotterdam Criteria is used to identify and diagnose polycystic ovary syndrome. The purpose of this paper is to discuss, describe, and characterize potential non-invasive biomarkers that are present in various pathological stages of both endometriosis and polycystic ovary syndrome. These biomarkers include CA125, Serum galectin-9, hsa-miRNA-154-5p, miRNA-93, miRNA-320 and ET-1, miRNA-222, miRNA-146a, and miR-30c used in a panel1-6

Boolean Artex Spaces Over Bi-monoids

We define Complimented Artex Space over a Bi-monoid. We define Boolean Artex Space over a Bi-monoid. We give an example of a Boolean Artex space over a bi-monoid. We  prove that the homomorphic image of a Complimented Artex Space over a Bi-monoid is a Complimented Artex Space over the Bi-monoid. We also prove that the homomorphic image of a Boolean Artex Space over a bi-monoid M is a Boolean Artex Space over the bi-monoid M.  We also prove that the Cartesian product of Complimented Artex Spaces over a Bi-monoid  is Complimented Artex Space over the Bi-monoid. Finally we prove the Cartesian product of Boolean Artex Spaces over a bi-monoid M is a Boolean Artex Space over the bi-monoid M. Keywords : Complimented, Distributive Artex Spaces, Homomorphism

SubArtex Spaces Of an Artex Space Over a Bi-monoid

We define SubArtex Space of an Artex space over a  Bi-monoid. We give some examples of SubArtex spaces. We  prove the necessary and sufficient condition for a subset of an Artex space over a bi-monoid to be a SubArtex space. We  prove another equivalent  Proposition for the necessary and sufficient condition for a subset of an Artex space to be a SubArtex space.  We prove a nonempty intersection of two SubArtex spaces of an Artex space over a bi-monoid is  a SubArtex space. Also we prove a nonempty intersection of a family of SubArtex spaces of an Artex space over a bi-monoid is  a SubArtex space.  Finally, we prove, in this chapter, by giving an example, that  the union of two SubArtex spaces need not be a SubArtex spac

A characterization for ∗-isomorphisms in an indefinite inner product space

AbstractLet H1 and H2 be indefinite inner product spaces. Let L(H1) and L(H2) be the sets of all linear operators on H1 and H2, respectively. The following result is proved: If Φ is [∗]-isomorphism from L(H1) onto L(H2) then there exists U:H1→H2 such that Φ(T)=cUTU[∗] for all T∈L(H1) with UU[∗]=cI2, U[∗]U=cI1 and c=±1. Here I1 and I2 denote the identity maps on H1 and H2, respectively

APPLICATION OF HYBRID PSOGA FOR OPTIMAL LOCATION OF SVC TO IMPROVE VOLTAGE STABILITY OF POWER SYSTEM

Due to huge increase in power demand, modern power system networks are being operated under highly stressed conditions. This has resulted into the difficulty in meeting reactive power requirement and maintaining the bus voltage within acceptable limits. Voltage instability in the system occurs in the form of a progressive decay in voltage magnitude at some of the buses. The problems of voltage instability and voltage collapse are the major concerns in the operation of power system. It is very important to do the power system analysis with respect to voltage stability. Flexible AC Transmission System (FACTS) device in a power system improves the stability, enhances the voltage stability margin and reduces the power losses. Identification of location of FACTS device in the power system is very important task. Research is carried out to investigate application of Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and hybrid PSOGA to find optimal location and rated value of SVC device to minimize the voltage stability index, total power loss, load voltage deviation, cost of generation and cost of FACTS device to improve voltage stability in the power system. Optimal location and rated value of SVC device have been found for different loading scenario using PSO, GA and PSOGA. It is observed from the results that the voltages stability margin is improved, voltage profile of the power system is increased, load voltage deviation is reduced and real power losses also reduced by optimally locating SVC device in the power system. The proposed algorithm is verified with IEEE 14 bus and 30 bus power system

Some Special Artex Spaces Over Bi-monoids

We introduce a Special Artex Space over a bi-monoid namely Distributive Artex space over a bi-monoid. We give some examples of Distributive Artex space over bi-monoids. We prove the Cartesian product of any two Distributive Artex Spaces over a bi-monoid is a Distributive Artex Space over the bi-monoid. Also we prove the Cartesian product of a finite number of Distributive Artex Spaces over a bi-monoid is a Distributive Artex Space over the bi-monoid. We prove under the Artex space homomorphism f : A ? B, the homomorphic image of a SubArtex Space of an Artex space A over a bi-monoid is a SubArtex space of B. We prove the homomorphic image of a Distributive Artex Space over a bi-monoid is a Distributive Artex Space over the bi-monoid. We  prove a SubArtex space of a Distributive Artex space over a bi-monoid  is a Distributive Artex space over the bi-mpnoid. We solve three problems on Bounded Artex spaces over bi-monoids. 1.A SubArtex space of a Lower Bounded Artex space  over a bi-monoid need not be a Lower Bounded Artex space over the bi-monoid. 2. A SubArtex space of an Upper Bounded Artex space  over a bi-monoid need not be an Upper Bounded Artex space over the bi-monoid and 3. A SubArtex space of a Bounded Artex space  over a bi-monoid need not be a Bounded Artex space over the bi-monoid. Keywords : Distributive Artex space, Bounded Artex space, Homomorphic imag

Custom Deep Learning Model for the Diagnosis of Cervical Carcinoma

Cancer is the second most common cause of death in the majority of the world due to late diagnosis. Most cancer cases are typically discovered at an advanced stage, which lowers the likelihood of recovery because proper therapy cannot be given at that time. In particular, for incurable cancers, which may result in a reduced life expectancy due to the rapid progression of the disease, the sooner cancer is identified, the more effective the therapy may be. Early detection also lessens the financial effects of cancer because treatment in the early stages is much cheaper than treatment in later stages.The method suggested is an end-to-end deep learning method in which the input photos are sent directly to the deep model, which makes the decision. The proposed Ensemble of deep learning modelIV3-DCNN to detect cancer in pap-test images. The model's precision, FScore, Specificity, Sensitivity, and accuracy of 99.4%, 99.23, 95.48, 97.9, and 99.2%. Last but not least, the suggested strategy would be very beneficial and successful, especially in low-income nations where referral mechanisms for patients with suspected cancer are frequently lacking, resulting in delayed and fragmented care

Particle swarm optimized extreme learning machine for feature classification in power quality data mining

This paper proposes enhanced particle swarm optimization (PSO) with craziness factor based extreme learning machine (ELM) for feature classification of single and combined power quality disturbances. In the proposed method, an S-transform technique is applied for feature extraction. PSO with craziness factor is applied to adjust the input weight and hidden biases of ELM. To test the effectiveness of the proposed approach, eight possible combinations of single and combined power quality disturbances are assumed in the sampled form and the performance of the proposed approach is investigated. In addition white gaussian noise of different signal-tonoise ratio is added to the signals and the performance of the algorithm is analysed. The results indicate that the proposed approach can be effectively applied for classification of power quality disturbances