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The underground electricity distribution cables are subjected to stress and pressure as they are laid underneath the earth. Due to many factors, these cables create different types of faults that i...

The Wireless Sensor Networks (WSN) comprises of countless transfer hubs that gather the ecological data from the sensor hub and send them to a sink or (cluster head) CH. The improvement of communication advancements has propelled the utilization of WSNs and the remote communication. Since WSNs turned out to be progressively mainstream, the nature of service extend by the WSN in the part of information collection, maintaining accuracy and transmission in a convenient way has caused more to notice the research framework of network system designers. However, the hubs are inclined to failure because of the vitality limitation, device failure, communication link error and malicious attack. The node insufficiency is the one of the most vital issue in the WSN. The proposed TIC calculation using fuzzy logic discovers the trust score for reinforcement hand-off hub task to defeat the issue in establishing the network.

Wireless mesh networks have numerous advantages in terms of connectivity as well as reliability. Traditionally the nodes in wireless mesh networks are equipped with single radio, but the limitations are lower throughput and limited use of the available wireless channel. In order to overcome this, the recent advances in wireless mesh networks are based on multi-channel multi radio approach. Channel assignment is a technique that selects the best channel for a node or to the entire network just to increase the network capacity. To maximize the throughput and the capacity of the network, multiple channels with multiple radios were introduced in these networks. In the proposed system, algorithms are developed to improve throughput, minimise delay, reduce average energy consumption and increase the residual energy for multi radio multi-channel wireless mesh networks. In literature, the existing channel assignment algorithms fail to consider both interflow and intra flow interferences. The limitations are inaccurate bandwidth estimation, throughput degradation under heavy traffic and unwanted energy consumption during low traffic and increase in delay. In order to improve the performance of the network distributed optimal congestion control and channel assignment algorithm (DOCCA) is proposed. In this algorithm, if congestion is identified, the information is given to previous node. According to the congestion level, the node adjusts itself to minimise congestion. &nbsp

Diabetes occurs among different age group populations due to insulin usage in the body. The pancreas produces insulin to convert the food into glucose, and it is observed by the blood cells to produce energy. In some people, the pancreas produces less insulin, and they are called type I diabetic patients. Hence, those people are suffering from type I diabetes and are advised to take insulin regularly through injection. But they don’t know the exact quantity of insulin to be taken on their day-to-day basis. The consumption level of insulin varies for every individual. The single individual consuming insulin also varies every day based on their regular activities, food, stress level, exercises, etc. Only through a regular blood test the patients may know the insulin level in their body. The patient needs the advice of physicians every day to change the consumption of insulin. It is difficult to get an appointment with the physicians to meet and know their blood glucose level and the quantity of insulin required for their bodies. Thus, there is a need for a computer-aided diagnosis system to solve this struggle among type I diabetic patients. By using the proposed system, hospitals could reduce the inpatients during critical times, while they visit the physicians only for testing the insulin level. The proposed IoT-based machine learning algorithm is suitable to efficiently handle the condition to predict the glucose level of the human body and suggest the quantity of insulin required for the diabetic patient. The patients are advised to access the system from anywhere without a physician’s help. The proposed technique is accurately predicting the required insulin level to assist diabetic patients. Insulin conception varies among the patients when they are fasting, after meals, and during sickness. First, patients should know their type of insulin. The patient must check the blood glucose level through the glucometer, and this has been recorded in the system to track the status. The noninvasive Dexcom G4 platinum sensor is used to update the patient’s glucose level reading every minute, and it will be displayed in an android device application. The patient’s glucose level is compared with the parameters in the dataset under different categories. In order to reliably predict the dosage of insulin needed for the patient on a specific day, the machine learning algorithm found an exact pattern-matching criterion. The graphical user interface collects the daily activities of the patients and analyzes their previous history of insulin consumption to make a wise decision on insulin calculation. The statistical results are projected through the graph which denotes the insulin dosage conceived by the patients on a particular period. This helps the physicians to suggest quick solutions during the time of admit due to any other illness in a hospital. The historical data enables the physicians and patients to act smoother in handling the diabetic disease. The proposed system improves the economy of the country by saving the life of diabetic patients.

In these days, credit card fraud detection is a major concern in the society. The use of credit cards in e-commerce sites and various banking sites has been increased rapidly in recent times. As modernization will have both positive and negative impacts, the use of credit cards in online transactions has made it simple; likewise, it also led to the increase of the number of fraud transactions. As part of the activities happening, it is always advised for the e-commerce sites and the banks to have automatic fraud detection system. Credit card fraud might result in huge financial losses. While look for the solutions for credit card frauds that are happening, machine learning techniques provide favorable solutions. The proposed system uses a random forest application in solving the problem and to attain more accuracy when compared to the other algorithms used till now. All the basic classifiers have the same weight but random forest algorithm has relatively high and others have relatively low weights because of the randomization of bootstrap sampling of a making decision and selection of attributes cannot guarantee that all of them have the same stability in decision making.

Diabetic Retinopathy is a progressive disease that affects diabetic patients and changes the width and tortuosity of the retinal blood vessels. The preferred center of attention is to predict the n...

Wireless mesh networks have numerous advantages in terms of connectivity as well as reliability. Traditionally, the nodes in wireless mesh networks are equipped with a single radio, but the limitations are lower throughput and limited use of the available wireless channel. To overcome this, the recent advances in wireless mesh networks are based on a multi-channel multi-radio approach. Channel assignment is a technique that selects the best channel for a node or to the entire network just to increase the network capacity. To maximize the throughput and the capacity of the network, multiple channels with multiple radios were introduced in these networks. In this work, algorithms are developed to improve throughput, minimize delay, reduce average energy consumption, and increase the residual energy for multi-radio multi-channel wireless mesh networks.

The aim of this paper to predict a student performance using traditional and machine learning techniques: Bayes algorithm, linear regression, logistic Regression, k-nn algorithm, decision tree. Naive based algorithm is the emerging field which compose the procedure of verified students details like semester marks, assignment, attendance, lab work which are used to improve students’ performance. This paper shows a model of students data prediction based on Bayes algorithm, linear regression, logistic Regression, k-nearest neighbor, decision tree and suggest the best algorithm among these algorithms based on performance details. Classification is an important area to predict and application in a variety of fields. In the view of full knowledge of the algorithm underlying probabilities, Bayes decision theory shows the optimal error rate. Decision tree algorithm is been used successfully in expert systems in capturing prediction. Mainly the decision tree classifiers are used to design and classify the student’s data with Boolean class labels. Linear regression is a linear approach to modeling the relationship between the details of students in scalar response.

Type 2 diabetic patients have the chance of a sight-threatening diabetic retinopathy disease. It affects the retina of the eye and causes damages to the human vision. The pathological fundus images...

Diabetic Retinopathy is one of the ophthalmic reasons for visual deficiency. The favored fixate of consideration is on the estimation of deviation in the breadth of the retinal veins and the new vessel development. To witness the progressions, segmentation has to be made primarily. A framework to improve the quality of the segmentation result over pathological retinal images is proposed. The proposed method uses adaptive histogram equalizer for preprocessing, pulse coupled neural Network model for automatic feature vector generation and extraction of the retinal blood vessels. The test result represents that the proposed method is enhanced than other retinal competitive methods. The evaluation of the proposed approach is executed over standard public DRIVE, STARE, REVIEW, HRF, and DRIONS fundus image datasets. The proposed technique improves the segmentation results in terms of sensitivity, specificity, and accuracy.

Diabetic Retinopathy (DR) occurs due to Type-II diabetes. It causes damages to the retinal blood vessels and reason for visual impairment. The predicted center is around the probability of variation in the estimation of retinal veins, and the crisp enrolls vessel development inside the retina. To witness the changes segmentation of retinal blood vessels has to be made. A framework to upgrade the quality of the segmentation results over morbid retinal images is proposed. This framework utilizes Contrast Limited Adaptive Histogram Equalization (CLAHE) for eliminating the background from the source image and enhances the foreground blood vessel pixels, Tandem Pulse Coupled Neural Network (TPCNN) model is endorsed for automatic feature vectors generation, and Deep Learning Based Support Vector Machine (DLBSVM) is proposed for classification and extraction of blood vessels. The DLBSVM parameters are fine-tuned via Firefly algorithm. The STARE, DRIVE, HRF, REVIEW, and DRIONS fundus image datasets are deliberated to assess the recommended techniques. The results render that the proposed technologies improve the segmentation with 80.61% Sensitivity, 99.54% Specificity, and 99.49% Accuracy.

Diabetic Retinopathy (DR) occurs due to Type II diabetes. At the early stage, if it is identified one can save their vision. Later stage, retinal detachment leads to 100% vision loss. An automatic computer based system is needed for diagnosis. There are diverse tools and automatic and semi-automatic systems are available. But the system is not identifying and measuring the narrow blood vessels accurately, because of the noise and imaging problems. Also, while tracking the retinal vessel, the narrow vessels are equally taken into consideration as wider vessels. Thus the proposed segmentation and classification techniques extract the blood vessels and measure the profile features of fundus images obtained from dissimilar modalities significantly.