To prevent fatal heart failure early detection of Heart attack is essential steps. Heart Disease prediction happened well in advance using Machine learning, Artificial algorithms in Information Communication Technology, and the use of a combination of multiple algorithms can further improve accuracy of predictions. However, it is important to note that the accuracy of algorithms in predicting heart disease depends on the quality and quantity factors. Important privacy concerns and potential biases are also very much essential in data. Reason for heart disease includes improper food habits and an increase in fat content due to less physical work. The diagnosis of heart illness is mainly related to symptoms, and clinical examination of patients. In this study, we try to classify and predict heart diseases at early stages on specifical features by fusing Various Machine Learning Algorithms. Machine learning when being used in health-care is able to detect disease earlier and accurately. This work yielded an accuracy of 88.5

Recently there has been a sudden increase in the demand for second-hand cars as consumers have been unable to afford brand-new cars due to a variety of factors, including high pricing, limited availability, financial inability, etc. The second-hand automobile industry, however, is still in its development phase and is mostly controlled by the informal sector. When buying a second-hand automobile, creates the possibility of fraud. In order to predict the price of a used automobile without favoring the consumer or the merchandiser, a high-accuracy model is needed. To obtain high accuracy, a variety of regression algorithms such as support vector regression (SVM), linear regression, random forest regression, decision tree and polynomial regression are utilized. R-squared was calculated to evaluate how well each regression performed. Of all the regressions used, Random Forest’s R-squared was the highest. Here are some potential areas of novelty that were further explored to improve the accuracy of the Random Forest model to 0.9234:

Technology is evolving markedly and so is the demand for brilliant everyday solutions. Perceiving the response/reaction of the partakers is significant in enhancing and boosting the performance of the particular event which makes collection and interpretation of reviews and feedback very crucial. Facial Expressions are a clear illustration of a person’s emotion, intention and cognitive activity. So this project focuses on detecting faces and recognizing Facial Emotions based on Deep Learning Models such as Multi-Task Cascaded Convolutional Neural Network (MTCNN) & Convolutional Recurrent Neural Network (CRNN) respectively, which are in accordance with the CNN Model. We have utilized OpenCV libraries for pre-processing & Matplotlib to manipulate the results into a 2D graphical illustration. All of this is hosted onto a webpage through Flask API. The UI has options to collect input through webcam in real-time and also a provision for the user to drop input images directly.

Energy production is transformed dynamically, and the basic infrastructure containing information and technology is gradually being built. The Internet of Things (IoT) can be an assembly of individual objects at anytime, anywhere, to anyone, to everything, by means of any network and service. Consequently, IoT can denote an enormous, active international configuration of a network of Internet-connected objects through network tools. The further most significant and important claims of IoT are smart grid (SG). SG can be a data net united into the power grid to assemble and analyze data that cannot be inherited as of transmission lines, distribution, and customer substations. With the development and modernization of smart electronic devices in traditional energy grids and their revolution in SGs, there is a prerequisite for retrieving and processing information from these devices in real time or near real time. In addition, data mining requires that field devices can also communicate with each other through a central reference point. Owing to the interoperability of the IoT within smart networks, communication between devices not normally designed for the fullest possible exchange of information, information that will be available from anywhere with Internet access. It works better and quicker than the first transmission and distribution systems. Building computerization is the adopted flow chart of the automated and intelligent events essential in any building/shopping center, i.e., temperature regulator, door regulator, and pressure controller. IoT is a platform for testing networks of various sensors/actuators on the Internet. Safe and fast communication and management is probable from anywhere in the world; this is the foremost application of IoT. The IoT facilitates the storage and analysis of information as well as third-party intervention.

The use of digital images has become more important in numerous industries, including journalism, medical diagnosis, law enforcement, and forensics. Images could be easily modified due to the availability of photo-altering tools and software, which can distort the original information. The availability of low-cost and simple image-editing software has also drastically decreased the time and money needed to engage in image tampering. This destroys the purity of the image, making it vulnerable to misuse by anyone. Copy-move, splicing, and enhancement falsification are prominent methods used to create fake images. Using a combination of two or more images, a new image is created called splicing, which may be shared across many online channels and used to spread information and impact target audiences. The discovery could have beneficial as well as negative results. Consequently, it is important to provide a method that can identify the splicing forgery in an image. The study used a Deep Learning (DL) model named MobileNet to distinguish between authentic and manipulated images. Both the training and testing of the DL model make use of the CASIA data. Resizing and enhancing methods are used to process the collected data. Accuracy, F1 score, and recall are employed to test the reliability of the created DL model.

Industry 4.0 is the recent revolution in automotive and manufacturing industries for the production of smart devices to make the complete digitalization. One of the significant applications of IIoT is predictive maintenance. In hazardous places, prediction about disaster is helpful to manage the people's safety and reduction of damages. Machine Learning (ML) approaches are very useful for prediction. ML develops a mathematical, trainable model to analyze the data and apply with different kinds of existing methods for interrogating data. In this chapter, it is going to be discussed about how the advanced and automated data processing is connected with the new types of Computer Vision (CV) applications which influences directly with the human lives and safety of physical assets in hazardous places. The activities such as disaster early warning, recovery, and reconstruction system are to be studied. Also, some of the practical issues in the planning and recovery processes will be discussed.

Air Quality assessment and forecasting are the essentials today and they attracted many researchers. Environmental organizations regularly monitor and predict the air contaminants to make the public awareness, provide a better environment, and suitable for human health. Physical factors like climate changes, Industrialization, Fires and Urbanization are some of the factors which directly affect and reduce the air quality. All these data are time-series and real-time data. The primary pollutant is PMx that affect the respiratory systems and cardiac activity of humans. The secondary pollutants are SO2, CO, NOx, and O-3. Each has allowable range of concentration levels. In this work, meteorological elements are collected in different locations in last 5 years, with time window of 24 h and mapped to the concentration level of pollutants. The Machine Learning(ML) Methods such as Non-Linear Artificial Neural Network(ANN), Statistical Multilevel Regression, Neuro- Fuzzy and Deep Learning Long-Short-Term Memory (DL-LSTM) are used; to find the current concentration level of pollutants and will be useful for Real Time Correction (RTC) to give a feedback that can be used to reduce the contaminants in air for further days. The results are compared with the parameters such as R-2, RMSE and MAPE. Using these methods, the concentration level of contaminants is predicted with the deviation of R-2 in the range of 0.71-0.89. The results proved that DL-LSTM suits well when comparing to the ANN, Neuro-fuzzy and regression algorithms.

Many of the natural disasters are threatens the human lives and also assets of the people. Hazard mapping is one way to connect the particular location into kind of hazardous based on the history of the events in the specific geographic zone. The weather changes may trigger into many natural disaste

A modern model long-term composed service (LCS) with a group recommendation system has an indefinite lifespan. An LCS is used as a long-term business goal, and for a business committed to its customers, support will be provided to customers enabling them to book, e.g. an automotive service through online web services by providing information that the LCS then uses to offer more support. However, identifying the exact service to meet the user requirement is essential. Service composition has been identified as the key task in achieving various QoS performances. There exist various approaches that involve service composition according to the throughput and popularity. However, they fail to achieve the expected performance. Towards improving the performance of the LCS, a novel LCS that is based on the user queries of a group of persons is developed to give the best business services based on previous travel details and services. The method carries out service selection and composition according to the ratings provided by users towards any service. Additionally, the method considers the user-to-service rating and service-to-service rating, which are measured according to the coupling quality. Therefore, the proposed novel LCS provides better services based on the user ratings for particular business queries. The method ranks the services according to the rating values to perform service composition, with consideration of the detection of similar user groups and utilization of the rating values in service selection. We aim to propose a novel LCS work based on group ratings and a group of services. This work is intended to reduce the time complexity of changes in the LCS network using the group recommendation system.

In the present scenario everyone turned into smart applications by including the intelligence into the applications and this reduces the burden of frequent interruption or control by the humans. Smartness makes the ability to interconnect more real time parameters by M2M (Machine to Machine) interaction and make wise decisions in harmful situations. Many low-cost devices are available in the market to collect the real time data and transmit them using Internet of Things (IoT). So, the control can be done remotely. In this work, few basic applications in smart buildings are going to be studied to analyze with the technical advances which can bring better solutions automatically. The details collected from different sensors will be useful for analytics and the need for smart design models for better buildings. The services needed in smart building such as security control, energy management, control and monitoring of HVAC system, water management, lighting systems, health system of elders and fire detection are going to be surveyed. The major objective of this study is to identify the issues faced by current methodologies with these applications and give a guideline for future research. From analyzing the relevant methods and needs it is observed that, the buildings construction and usage can depend on the applications, the smart system can respond intelligently for further events. In future all the traditional buildings will be automotive based on HVAC.

Nowadays, prediction of abnormality plays a vital role in healthcare applications for deciding the treatment and guiding for proper treatment on time. The amniotic fluid is the water of the womb, and it is a strong indicator of congenital fetal anomaly. The automatic calculation of amniotic fluid index (AFI) and shape features of varying gestational periods will be useful to predict the perinatal outcome of high risk in maternity patients. Some perinatal outcomes are expected fetal weight, head circumferences and need of new-born ICU which decide the mode of delivery. These perinatal outcomes will be helpful in increasing the live birth and reducing the risk of premature delivery. The aim of this work is to identify the abnormal AFI of expectant mothers to alert the clinicians. Computer-aided diagnosis supports the clinicians in decision-making process. In the proposed work, using the training set of ultrasound images, the shape templates are developed by using deformable methods. Contour points in the edges will be helpful to find the AFI. After that, features are extracted and fuzzy logic algorithm is used to classify the given image into one of the four categories such as oligohydramnios, borderline, normal and hydramnios state for expectant mothers and their impact on fetal growth. The outcome of the proposed approach is measured in two different ways. The first outcome is that calculated AFI will be compared with the value calculated by the radiologist/clinicians, and the second outcome is that along with AFI, shape feature with contour points and gestational age are used for making decision/classification such as normal, borderline, oligohydramnios and hydramnios, and the classified results will also be compared with the expert's opinion. The outcomes are represented quantitatively. The results proved that AFI calculated by the proposed work was matching 94% with the expert opinion and classification of test image into any one of the categories such as normal, borderline, oligohydramnios and hydramnios fetched average accuracy of prediction up to 92.5%.

The high risk of air pollution in urban area makes it dangerous for the quality of human life. Monitoring and controlling of physical environments from remote locations with considerable precision is possible with the help of Wireless Sensor Networks (WSNs). Rapid urbanization and heavy usage of vehicles have exacerbated air pollution in the recent years. Monitoring air pollution is convoluted yet a very important task at hand. The exact parameter estimation is needed for the real time air quality monitoring systems and to make quick decisions on timely basis to monitor and control the air quality. Data was traditionally collected periodically which was both costly and time-consuming affair. This complexity can be handled by the use of WSN which helps attain instant readings. A collection of computing elements that connect and interact with each other is referred to as a Cyber Physical System (CPS). Data necessary for monitoring and controlling is routed among the nodes using routing protocols. In this paper, for making the correct decision on route selection, fuzzy logic is employed. Fuzzy logic concept is used to monitor risk of air pollutant on time in the particular industry and area. This work involves the monitoring and validating air control using the CPS method for multicast routing.

Recent growth in technology makes our life faster and easier. This development in technologies leads to enhance the traffic hazards. In this paper, Internet of Things based accident prevention and detection system is proposed to deduce accidents and save human life. The vehicle performance has been continuously monitored for safety purposes. This technique is annoying to maintain speed balance to avoid accident and provide safety to the driver. Hence, a novel approach is proposed to avoid accidents and save the victims while accidents occur. Sensors are exploited to give alarm 'ON' when distance between two vehicles is too short. If accident happens, then the camera is automatically turned on and captures the images around 180 degree angles. This emergency alert information including the location is transferred to nearest police station, ambulance service and relatives through GSM modem. The major component of the project includes a Arduino, motion sensor, touch sensors, relay and GSM modem.

Cloud computing is broadly utilized rising innovation for putting away and sharing information over web yet at the same time confronting loads of security and protection issues. These difficulties incorporate client's mystery information misfortune, information spillage and uncovering of the individual information security. Considering the security and protection inside the cloud there are different dangers to the client's delicate information on distributed storage. This paper is review on the security and protection issues and accessible arrangements. Additionally present diverse open doors in security and protection in cloud condition. Furthermore, cloud specialist co-ops (CSPs) can likewise deliver the talked about issues to offer better security and protection.

Brain Stroke is the third leading reason of death or major disabilities and needs computer guided assistance to diagnose at an earliest stage of disease. Stroke results in great physical functioning restrictions, which negatively impacts the quality of life for survivors and also care givers. MRI of brain is mainly used for accurate diagnosis even though its cost is high. In this work, a Hybrid Genetic Algorithm (HGA) is proposed for feature selection with Independent Component analysis and parameters optimization of Multi layer Perceptron (MLP). The classification results are compared with simple KNN and MLP Classifiers.

The proper functioning of the heart can be ascertained through ECG or Electro-Cardio-Gram. It is a form of signal that gives important information about the working of heart with respect to time and analysing ECG signals without manual intervention is an important application. Early detection of heart diseases or abnormalities is very important as it can help in prolonging life time and also to increase the quality of life. The ECG signals are captured by placing several electrode pads on the body at various positions. A better diagnosis of the heart disease can be achieved by using Multi-lead ECGs as it acquires signals simultaneously. This work focuses on the suggested Discrete Wavelet Transform (DWT) used in processing ECG recordings and also to extract certain attributes. The process of feature extraction and dimensionality reduction can be effectively performed using Principal Component Analysis (PCA). A population of knowledge structures is maintained in Genetic Algorithm (GA) called chromosomes. Each of these represents a candidate solution to the given problem. An algorithm having it basis on the governing laws of one dimensional collision between two bodies from physics was proposed and named as Colliding Bodies Optimization (CBO). This protocol is a modern population based stochastic optimization algorithm. Naive Bayes, K-Nearest Neighbor (KNN) and Classification and Regression Tree (CART) classifiers are used. Through the outputs it is clear that the proposed method performs well when compared against other methods.

Background: Many medical surveys report that the number of breast cancer death has been increasing 5% to 15% in every 10 years, since the year 1940. One of the benign states of breast cancer is micro-calcifications, but all the micro calcifications may not develop the breast cancer. Earlier diagnosis is helpful to give the proper treatment and reduce the fatality rate and Computer Aided Diagnosis will be helpful to the radiologists to enhance the visualization the micro-calcifications and masses. Method: In this work, Mammogram image segmentation with global histogram was used. The parameters for segmentation are selected based on Expectation Maximization (EM) algorithm. Multi Resolution Wavelet Analysis is used for feature extraction. Optimized EM is used for segmentation. Firefly Algorithm is used for statistical parameter optimization of EM. Conclusion: The proposed method's efficiency is evaluated by the parameters such as total correct fraction, sensitivity, specificity, dice coefficient and total volume error. The firefly-optimized EM segmentation results are compared with simple EM segmentation. Quantitative measures of proposed method proved that firefly optimization is efficient than simple EM algorithm.