Intelligent Technologies for Automated Electronic Systems

In many nations, motorcycle accidents are a big public issue. Despite public awareness campaigns, the problem continues to grow as a result of poor riding habits such as riding without a helmet, dangerous driving, drunk driving, riding without enough sleep, and so on. Because of late help to those who have been in accidents, the rate of deaths and disabilities is quite high. People who are implicated suffer significant economic and social consequences as a result of them. As a result, various research organizations and large motorcycle companies have developed safety systems to safeguard riders from harm. Furthermore, a dElectronics and Communication Engineeringnt motorcycle safety system is hard to execute and costly. The integration of modern communication technologies into the automotive industry allows for greater assistance to people wounded in traffic accidents, a reduction in the time it takes emergency services to respond, and an increase in the amount of knowledge they have about the occurrence. The number of fatalities might be greatly reduced if the resources necessary for each disaster could be determined more precisely. The developed scheme calls for every vehicle to be equipped with an on-board unit that detects and reports accident scenarios to an exterior control unit that assesses the depth of the problem and provides the needed resources to aid it. The creation of a prototype based on off-th- -shelf equipment indicates that this technology can considerably reduce the time it takes to send emergency services following an accident. 

The disease, Malaria, is caused by Plasmodium Parasite, and is transmitted via female Anopheles mosquito bite. There are 4 variants of plasmodium which cause malaria, they are, 1) Plasmodium falciparum, 2) Plasmodium vivax, 3) Plasmodium ovale, and 4) Plasmodium malariae. Though there are several clinical and laboratory techniques for finding the presence of malaria, the accuracy and the time required to determine the presence of the parasite are inadequate. Therefore, in this work, we have come up with a system that uses image-processing techniques to determine the presence of malaria in Human RBCs. In addition, the system determines the severity and stage of the malarial parasite. Malaria is brought on by the Plasmodium parasite and spread via female Anopheles mosquito bites. Plasmodium falciparum, Plasmodium vivax, Plasmodium ovale, and Plasmodium malariae are the four plasmodium species that cause malaria. Although there are a number of clinical and laboratory methods for detecting the presence of malaria, the speed and precision needed to do so are insufficient. As a result, in this study, we have developed a system that employs image-processing methods to ascertain if there is malaria present in human RBCs. The technique also establishes the malarial parasite's stage and intensity.

Defocus blur is extremely common in images captured using optical imaging systems. It may be undesirable, but may also be an intentional artistic effect, thus it can either enhance or inhibit our visual perception of the image scene. For tasks, such as image restoration and object recognition, one might want to segment a partially blurred image into blurred and non-blurred regions. In this project, we propose a sharpness metric based on the the Local maximum edge position octal pattern and a robust segmentation algorithm to separate in- and out-of-focus image regions. The proposed sharpness metric exploits the observation that most local image patches in blurry regions have significantly fewer certain local binary patterns compared with those in sharp regions. Using this metric together with image matting and multiscale fuzzy inference, this work obtained high-quality sharpness maps. Tests on hundreds of partially blurred images were used to evaluate our blur segmentation algorithm and six comparator methods. The results show that our algorithm achieves comparative segmentation results with the state of the art and has high speed advantage over others.

Analytics is one of the front runners nowadays as we have data piling in various sizes and quantities and in a dynamic fashion too. Data Analytics and in particular, predictive analytics is a hot cake in the days of social media and social networks as we grow from data banks to data rivers. This chapter is a glimpse of the basics of analytics and a few predictive analytic models currently employed in the analytical circle like Multiple regression, logistic regression and K nearest neighbor model. As we are in the era of machine learning and artificial intelligence, having a predictive analytical tool in our toolkit is all the more necessary.

Simulated systems are used for modelling and analysis of systems for which an analytical solution is either not accessible or difficult to achieve. Simulation is a highly flexible and adaptable discipline within computer science. Because it is simpler than conventional approaches, which are often challenging, simulation is also chosen as a method of system analysis. Because of this, simulation is an area with extensive application and demand, making it interesting and beneficial to have a chapter dedicated to researching simulation with a case study of modelling a Queuing system.

Virtualization is a cloud-computing technology that only needs one CPU to work. Virtualization makes it look like many machines are working together. Virtualization focuses mainly on efficiency and performance-related tasks because it saves time. This paper primarily focuses on operating system virtualization. It is the modified form of a standard operating system that allows users to operate different applications that produce a virtual environment to perform various tasks on the same machine by running other platforms. This virtual machine helps compare the performance of Type1 and Type2 hypervisors based on how much work they do and how much memory they use.

Cloud computing provides a dynamic model that provides many more services to users, as well as organizations, that can purchase based on their requirements. Cloud offers services such as storage for data, a platform for application development and testing, providing an environment to access web services, and so on. Common issues in a cloud environment are maintaining the application performance with Quality of Service (QoS) and Service Level Agreement (SLA) provided by the service providers to the organization. The major task done by the service providers is to distribute the workload among multiple servers. An effective load-balancing technique should satisfy the user requirements through efficient resource allocation in Virtual Machines. A review of various LB techniques that result in overall performance and research gaps is discussed in this paper. 

The current electronic voting system can be hacked easily. There are a lot of methods adopted to avoid malpractice. This research provides secured voting and avoids human intervention that results in smooth and secure conduction of elections. This research adopts biometric fingerprint recognition and face recognition of the voter for authentication. In an electronic voting system, the first step in the verification process can be easily achieved with the voter fingerprint data available in this database. The second step of verification involves the face recognition of the voter by the data already present in the database. If two-phase verification is done, the voter can proceed with the voting process and present his/her vote. Then the vote will be encrypted. This prevents fake votes and ensures perfect polling without any corruption. We have created a fingerprint-based voting system where the user does not have to take hisher ID with his/her necessary information. If the details match the previously stored information of registered fingerprints, a person is allowed to cast his or her vote. If not, a warning message will be displayed and the person is excluded from voting. In an election counting stage, the admin will decrypt and count the votes. 

This study presents a technique for solving the real-time decision-making difficulty in farming due to sudden changes in situations like atmospheric changes, monsoons, pest attacks, etc. The future of farming technologies is collecting and analyzing big information in agriculture to maximize effectiveness that is operational and minimize work costs. But there tend to be more styles to comprehend with all the IoT, therefore the Internet of Things will touch many more companies than simply farming. This study is focused on adapting the capability of IoT for data collection of features of crops and for automated decision-making with data analytics algorithms. 

One of the key predictions that had combined bio-sciences with innovation was bio-metrics, which represents a tool for security and criminology analysts to develop more accurate, robust, and certain frameworks. Biometrics, when combined with different combination techniques like feature-level, score-level, and choice-level combination procedures, remained one of the most researched technologies. Starting from uni-modular biometrics as unique marks, faces, and iris, they progress to multimodal bio-metrics. By presenting a similar investigation of frequently used and referred to uni- and multimodal biometrics, such as face, iris, finger vein, face and iris multimodal, face, unique mark, and finger vein multimodal, this paper will attempt to lay the groundwork for analysts interested in enhanced biometric frameworks. This comparative research includes the development of a comparison model based on DWT and IDWT. The method towards combining the modalities also entails applying a single-level, two-dimensional wavelet (DWT) that has been cemented using a Haar wavelet to accomplish the best pre-taking care of to eliminate disturbance. Each pixel in the picture is subjected to a different filtering operation in order to determine the peak signal to noise extent (PSNR). This PSNR analyses the mean square error (MSE) to quantify the disruption to hail before playing out the division of the largest dataset to the chosen MSE. In the most recent advancement, each pixel's concept is fixed up using the opposing two-dimensional Haar wavelet (IDWT), creating a longer image that is better able to recognise approbation, affirmation, and confirmation of parts. The MATLAB GUI is used to implement the diversions for this enhanced blend investigation, and the obtained outcomes are satisfactory.

Performance prediction is the forecast of future performance conditions based on past and present information. Forecasts can be made about companies, departments, systems, processes, and employees. This study focuses on assessing employee performance in terms of employee behavior, work, and growth potential. Organizations benefit when their employees perform well. Therefore, predicting employee performance plays an important role in a growing organization. To this end, we propose three machine learning algorithms: a support vector machine, a decision tree (j48), and a naive Bayes classifier. These can predict employee behavior in the workplace. Comparing the results, the Naive Bayes algorithm shows better results than the other two algorithms on the basis of metrics such as timeliness, error loss, and accuracy.

The discipline of artificial intelligence (AI), which trains computers to comprehend and analyse pictures using computer vision, is flourishing, particularly in the medical industry. The well-known non-invasive diagnostic procedure known as CCTA (Coronary Computerized Tomography Angiography) is used to diagnose cardiovascular disease (CD). Pre-processing CT Angiography pictures is a crucial step in computer vision-based medical diagnosis. Implementing image enhancement preprocess to reduce noise or blur pixels and weak edges in a picture marks the beginning of the research stages. Using Python and PyCharm(IDE) editor, we can build Edge detection routines, smoothing/filtering functions, and edge sharpening functions as a first step in the pre-processing of CCTA pictures. 

The patient-monitoring smart wheelchair system is a mobility aid for people with disabilities and continuously tracks the user's vital body metrics. Four interfaces—eyeball control, gesture control, joystick control, and voice control—have been created for wheelchair control in order to cater to various limitations. The image of the eyeball is captured using a camera. In order to make the necessary decisions based on the position of the eyeball, LabVIEW is used. The wheelchair movement can also be controlled by the other three modes. Anti-collision mechanisms are implemented using ultrasonic sensors. In the wheelchair, body temperature and heart rate monitoring provision is made. If any parameter is outside of a safe range, this system will notify the appropriate medical authorities and the wheelchair user's chosen individuals. The finished product is an innovative assistive technology that would simplify and lessen the stress in its user's life.

Cyberattacks on the power grid serve as a reminder that while the smart Internet of Things (IoT) can help us control our lightbulbs, it also runs the risk of putting us in the dark if attacked. Many works of literature have recently attempted to address the issues surrounding IoT security, but few of them tackle the serious dangers that the development of quantum computing poses to IoT. Lattice-based encryption, a likely contender for the next post-quantum cryptography standard, benefits from strong security guarantees and great efficiency, making it well-suited for IoT applications. In this article, we list the benefits of lattice-based cryptography and the most recent developments in IoT device implementations.

The Internet of Things (IOT) is a new technology that is anticipated to improve human lives. According to Cisco research, by 2020, there will be a vast array of IOT devices that will span every industry, including transportation, healthcare, and smart gadgets for every aspect of daily life. IOTs are improving user experience by making smart devices smarter and their services of high quality. The devices' unfettered access to the whole network makes the IOT's security issues more susceptible. The research paper will contribute to the presentation of a compiled report on the security issues with IOTs and the cryptographic techniques utilised to address them.

The recently developed technology in autos makes traffic signal prediction devices obligatory. It teaches users how to drive safely and manoeuvre their vehicles effectively. Due to drivers' various forms of attention, the number of accidents is rising alarmingly nowadays. The danger of distracted driving, which causes accidents, is decreased thanks to this technology, which also assists in identifying and providing information based on data. The notion of machine learning is presented, and the concepts of supervised learning, unsupervised learning, and reinforcement learning are covered under the heading of categorization and serve as the main principle. Linear regression, neural networks, naive Bayes, random forests, support vector machines, clustering, etc. are some types of models that machine learning may give. This study describes how to train a model using machine learning, with the basic principle being to divide the data into training, testing, and validation. The last section of this chapter discusses how to access machine learning methods to improve the quality of a machine learning project. The suggested approach provides an explanation of the combined model of the modern convolutional neural network (CNN) and the classic support vector machine (SVM) for traffic sign identification. Essentially, a CNN model was trained to produce this model. Several CNN model designs, including LeNet, AlexNet, and ResNet-50, may be used here. The subsequent layers of CNN's output may be utilised as features. These characteristics were added to SVM for categorization purposes. 

Machine learning entails making changes to the systems that carry out artificial intelligence (AI)-related tasks. It displays the many ML kinds and applications. It also explains the fundamental ideas behind feature selection methods and how they can be applied to a variety of machine learning (ML) techniques, including artificial neural networks (ANN), Naive Bayes classifiers (probabilistic classifiers), support vector machines (SVM), K Nearest Neighbour (KNN), and decision trees, also known as the greedy algorithm.