Nowadays, heart disease is the leading cause of death globally, with an estimated 610000 lives each year due to heart condition. One of the most common causes of heart disease is high blood pressure (HBP), fasting blood sugar (FBS), diabetes, cholesterol, Body mass Index (BMI), heart rate (HR). Diagnosis of heart disease is more prevalent nowadays; this involves a lot of accuracy and uncertainty due to the large-scale data decision-based on doctors may fail in some cases. Data mining is an intelligent diagnostic tool in healthcare. Thus, it is imperative to predict that each menace stage depends on age, sex, blood pressure, diabetes symptoms, what we can do for precaution by diagnosing the disease and proper treatment at the right moment. The purpose of the research work is to develop different predictive models using different forecasting measures and perform comparative analysis. In this work, we have used Cleveland and Statlog datasets with Naive Bayes (NB), K-Nearest Neighbors (KNN), and Logistic Regression (LR), Support vector machine (SVM), Decision Tree (DT), and Random Forest (RF) Classifier to develop various predictors. The experiment result shows that the random forest classifier gives better accuracy and results on both datasets when we perform a comparative analysis of them among all other classification models.

This paper incorporates the Credit Card Fraud Detection models to study and identify legitimate and fraud transactions. This research intends to recognize the false transactions while avoiding incorrect fraud classifications. The informational collection or dataset (Credit Card Fraud Detection) utilized in the proposed work is given by Kaggle which can be at https://www.kaggle.com/mlg-ulb/creditcardfraud. Before uploading on the website (Kaggle.com), these features are renamed and re-defined as PCA (Principal Component Analysis). There are general features in which 28 out of them are renamed as V1 through V28 (all numeric qualities). Rest three of the features showcase the time, calculated amount and whether that transaction was fraudulent or not. The response variable is 1 for a false transaction and 0 for a safe transaction. The chosen data set does not contain missing qualities. The dataset contains 284,807 transactions in which most of the transactions are very small and very few of the transactions come even closer to the maximum.

Different algorithms are implemented in this study. Python Machine Learning libraries are used to perform those algorithms. The model studied in this research work are K-Nearest Neighbour, logistic regression, random forest model, XGBoost model. As the XGBoost is showing more accuracy than other models. Out of these algorithms, XGBoost model is preferable over the Random Forest model and Logistic Regression model.

Sensors embedded in wireless mesh nexus calls for widely acclaimed area of research, due to its framework and internal components that integrate and work in synchronization mainly for perception of information. Being the backbone potential to various fields and applications, like critical infrastructure, disaster recovery situations, object and inventory tracking, health and medical setup, wireless sensor network has caught the attention of intruders to extract the information or disrupt its transmission. Sensor nodes are geographically aligned in mesh architecture, doing the needful. This paper revolves around the well-known “Black Hole Attack” by probing its behavior in depth and also detecting it in view of various parameters. The put forth technique focuses on energy consumption model primarily and different energy parameter values are recorded under the Invasion-case and Non-invasion case. The simulation and experimental results hence forth prove the presence of malignant attacker.

Over the years, developments in smart phone technology has boost up their use among users. This has captivated malware authors’ attention. Malware attacks in various forms has troubled users by stealing their personal information, banking information and much more. Android users have been strained most because of Android’s open nature. Throughout this time, efforts have been made to devise software and methods to detect android malwares. Starting from anti-virus software to Machine Learning and now Deep Learning, researchers have put forward various techniques to get to grips with the problem. Many Deep Learning Techniques have been put forward like Deep Neural Network, Convolutional Neural Network, Recurrent Neural Network, Deep Belief Network and Autoencoders. This paper looks at and analyzes supervised Deep Learning classifiers to detect Android malware.

The current work deals with the designing and performance analysis of a Triple Material Double Gate Cylindrical Gate All Around (TMDG-CGAA) MOSFET in the nanometer regime. The proposed structure has combined the gate engineering technique by used a double gate structure and the material engineering techniques by used three different materials in the gate electrode to incorporate the benefits of both the techniques. The proposed structure has been verified by using the ATLAS tool, which is a 3-dimensional tool. A comparative study of SG-CGAA, DG-CGAA, DMDG-CGAA, and TMDG-CGAA has been accomplished by using the same device parameters. The threshold voltage (Vth) is extracted from the transfer characteristic of the above MOSFETs. Along with the threshold voltage extraction the other parameters like DIBL and I_on/I_off ratio are also calculated. Results disclosed that the TMDG-CGAA MOSFET gives better immunity to the SCEs like DIBL. Also, the ratio of on current is to off current is also high in the TMDG-CGAA MOSFET in comparison to SG CGAA MOSFET and DGDM-CGAA MOSFET structures. In the short channel planar devices, the potential barrier is reducing so that the threshold voltage is also reducing. But in the proposed structure the device shows better performance, it increases gate controllability and reducing the SCEs and increment in the high drain current along with the high packaging density, which is needed in the application of Ultra Large Scale Integration (ULSI).

This paper focuses on driver drowsiness detection based on eye by using Digital Image processing technique. The proposed system is a non intrusive driver drowsiness monitoring system that is able to detect drowsiness of driver in wearing spectacles and dim light level. The proposed system is implemented or tested by using webcam to capture image then face and eye detection techniques used. The intensity of eye blinking area is checked to determine whether eye is close or open. An alarm is made on eye close condition. The samples are collected from left side, right side and with spectacles. The accuracy of the system is 98% and time taken to perform a sample is 30-50 seconds. Which is quite satisfactory.

With the changing trends of the communication system, less expensive, light weighted, and antennas having the characteristics of low profile are required. Microstrip antennas are the perfect fulfillment of these needs. Multiple frequencies patch, 2X1 patch antenna is designed and presented in this paper. Performance parameters of antenna parameters such as S11, gain, radiation plots, and smith chart are plotted. In this reported work two different frequencies has been used. Patches are designed on 4.5GHz and 5.1GHz. Results are obtained using High Frequency Structure Simulator (HFSS).

The photonic crystal waveguides is quite significant in designing the optical integrated circuits. Half Adder is the important part of any ICs. Here, we have designed an all optical half adder. This adder is designed with T-shaped square lattice photonic crystal. The structure is optimized with the line and point defect to act as Adder. These structure yields output at sum and carry port using phase interference method. Contrast ratio is used to measure the performance. The contrast ratio for sum is 6.50 dB and for carry is 10.47 dB. The footprint of proposed structure is 56 µm². The design is viable to open a way in designing future optical integrated circuits.

Optical networks are the key elements in demanding all-optical logic devices. This paper gives the detailed description of AND, OR, NOT & NAND gates designs simulations based on nonlinear element Semiconductor Optical Amplifier Mach Zehnder Interferometer structure. The FWM, XGM, XPM nonlinearity forms are used to achieve the ultra-high-speed (40Gbps) and Boolean expression of logic gates. Refractive index of SOA changes, due to the nonlinear character of the semiconductor optical amplifier which produces a high gain for ultra-high speed (40Gbps) of all logic gates. These proposed logic gates operate at 10Gbs (input data rate). The entire work is performed on the Opti system software for component analysis and Boolean expression testing. Realization of Boolean expression with high Q factor and ultrahigh speed operation and reconfiguration of logic gates is performed .

Due to rising load demand throughout the world and depleting nature of existing fossil fuels, the penetration of renewable energy sources (RES) to the grid is increasing. However, the uncertain nature of RESs makes it difficult to directly integrate with the existing conventional power plants. Therefore, it is quite important to consider the intermittent nature of RESs in the formulation and solution procedure. Out of the different RESs, the wind energy is taken into account in this paper and the uncertain nature of wind power is modeled using the ramp detection technique. Ramp events play an important role to fulfill the generation- demand properly. If these ramp events will not controlled, they will produce more pressure on wind turbines and turbine will be destructed. So for protected and steady process, it is essential that executer should have the knowledge about these ramp events. In this paper ramp ups and ramp downs of Dug site in Rajasthan are evaluated for sustainable wind resource assessment. After this, the comparison of these ramp events are done seasonally in which three seasons are taken namely: winter, summer and spring at a particular site for the wind power potential assessment.

The focus of power producers has shifted from conventional energy sources to sustainable energy sources because of the depletion of fossil fuels and carbon emission causing global warming and climate change. Solar cells are the most prominent option to deal with these problems. The precise estimation of solar cell parameters is very much required before their installation to achieve high efficiency.

In recent years applications of several optimization algorithms for parameter estimation of the solar cell have been addressed. More recently, Opposition Based Grey Wolf Optimizer (OGWO) which is an advanced version of Grey Wolf Optimizer (GWO) has been proposed. The wide applicability of this variant has been examined over some real problems. This fact motivated authors to employ this variant on parameter extraction process. The main motivation behind the implementation of OGWO on solar cell parameter estimation process is the efficiency of this version to deal with complex optimization problems. To estimate the PV cell parameter values, measurement of voltage and current are considered at three important points. Results of OGWO are compared with the results of other variants of GWO on these two models and for three films (Mono crystalline, poly crystalline and thin film). Results reveal that OGWO produces better.

In the power system network, the constant increase in demand for active and reactive power has restricted the possibility for network expansion which many times cause serious problems. The power system must be capable to keep acceptable voltage at all nodes in the network in a normal operating situation and during times of disturbance. Voltage instability is a severe concern in the system due to an increasing and unpredictable voltage drop. The research provided in this paper deals with many facts about the issue of voltage stability.

The major emphasis of this paper is to connect the FACTS device (Static VAR Compensator) at the system’s sensitive location to rectify the voltage instability issue. The SVC enables the transmission lines to be loaded near to their thermal limits, pushing the power to travel through the optimal paths. The SVSI is used to identify the optimal location of SVC. IEEE 14 bus system is used as test system to check the efficacy of proposed technique, Different cases (like base, 5% increment in load, 5% decrement in load) are considered here also. In each case voltage deviation (VD) is calculated and found superior as compared to other techniques. It has been observed that SVSI give better location of SVC rather than other indexes.

The appointment of power frameworks dependent on wind vitality is creating at a speedy pace wherever all through the world in perspective on the extended tendency of using reasonable power source resources and environmental worries regarding power generation. In wind vitality, the forecast of wind speed is over basic. The creating development in wind vitality enables for progressively exact models for wind speed expectation. To avoid both expensive overabundance age and degeneration, this paper acquaints a system with predict the breeze speed, on which the breeze vitality made, relies upon all the more gainfully. This can be cultivated by quantifiable systems wherein information in tremendous numbers are assembled, penniless down for the employable relationship using Multivariate Linear Regression (MLR) models. The parameters are utilized in the expectation of wind speed hourly verifiable information of temperature, pressure, relative humidity, past wind speed, and wind direction for the term of a half year and these parameters are gathered utilizing seven areas of Rajasthan. The results, which are gathered by utilizing MLR, at that point separated with constant information for validation.

Ultrasonic machining (USM) is a process used for machining material with high hardness and brittle nature that are difficult to machine. The USM has an upper hand on the other non-conventional process like electron discharge and laser beam machining which have thermal effect on workpiece. Workpiece chracteristics are also not limited by chemical and electrical properties. This paper discusses about the rotary ultrasonic drilling (RUD) machining process which is a variant of USM and the recent developments that it gone under through the RUD. This paper also reviews about the various methodology adopted by researchers to achieve optimum output parameters like Material Removal Rate, Surface roughness, Edge chipping etc. by altering the process characteristics like spindle speed, feed rate, ultrasonic power etc. as well as machine characteristics like tool type, support length etc. The paper also deals with the material used with the RUD worldwide and the findings of researchers on them.

India’s energy scenario has changed in recent years. The Intended Nationally Determined Contribution (INDC) commitment towards climate change of reducing the emission to 0.604 kg/kWh has set the new target of renewable source capacity to reach 175 GW by the year 2022. India’s tropical climate condition favours the capability of renewable energy sources, among them direct use of solar radiation and wind can be harnessed effectively to increase the energy capacity. Solar is the most secure, copious and non-polluting source of energy. The photovoltaic system capture the solar radiation has emerged as global player and the government has targeted to install 100 GW (40 GW rooftop capacity) of grid connected PV installation by the year 2022.

The PV power plant located at Mechanical Engineering Department of SKIT, Jaipur is studied to evaluate the performance under the composite climate condition. The performance indices include the energy and economic parameters such as reference yield, array yield, final yield net present value, cost of energy and money payback period. The analysis involves a simulation tool PVsyst. The energy parameters found for the installed capacity also involve performance ratio, calculated throughout the year. The result shows the final yield for the plant as 4.5 kWh/kWp/day. The economic evaluation shows the money payback period as 4.2 years.

Diesel engine contributes the most in the field of automobile, agriculture, construction and power generation, Due to serious environmental problem and rising cost of diesel, lot of research work is going on to replace conventional source of energy by a renewable source of energy. Diesel engine cannot be replaced because of its efficient performance at higher power and reliability with alternative engines. Diesel engines emissions contribute the most in the environmental problem, which is causing serious health problems for humans. The major emissions are smoke and NOx which need to be controlled in an effective manner. To find out the suitable additives to reduce exhaust emissions an enhance performance of CI engine, this experiment was carried out. For this purpose, detailed experiments were conducted with different sets of diesel–2–methoxy ethyl ether–nitromethane blends. Mixing of 5% MXEE and 2.5% NM with 92.5% diesel on volume basis (D–MXEE5–NM2.5) showed optimum results of emission and performance. Compared to the fuel mixture D-MXEE5-NM2.5 with diesel, an increase in BTE was found to be 4.90% and a decrease in smoke by 25.28%. However NOx increased by 17.94%.

Efficient energy utilization is helpful to promote the use of renewable energy resources such as solar energy. The depletion and scarcity are well known issues faced with the conventional energy resources. Hence, it is need of the hour to utilize thermal energy in an efficient manner to counter these issues. Therefore, it is essential to design a thermal energy storage system which can have high energy storage density. Latent heat thermal energy storage (LHTES) is preferred choice for storing thermal energy in a compact space compared to the sensible heat thermal energy storage (SHTES). However, previous literature report various issues associated with design and development of LHTES such as low thermal conductivity of the storage materials phase change materials (PCMs), improper heat transfer, etc. Hence, it is required to use techniques to enhance the rate of heat transfer while charging /discharging of LHTES. Further, heat transfer can be increased by proper considerations of design and operational parameters. This paper summarizes the design modifications in recent years that improve the effectiveness of LHTES for various applications.