This idea presents a subjective view on the work that has been done combining machine learning with brain image visualization to advance the understanding of brain's analytical capability to identify and imagine objects through sound, motions, or emotions and thereby applying the same concepts to a computer and displaying the objects or thoughts generated by the brain onto the computer screen. As we humans, learn anything step by step by gathering necessary information and then picturing(visualizing) it for understanding purpose, so will the computer, using a lot of datasets. It will also analyze each process step by step and picturize it by comparing with the data in its dataset. After acquiring enough data the computer shall then generate a pattern based on the observations collected, each time it comes across a word or a thought, it shall then compare it with the signal pattern that had been generated earlier. Also this invention shows the possibility of future development of this technology for a more realistic and intellectual robots or humanoids that can help mankind.

This paper intends to highlight a solution that has been obtained by the amalgamation of machine learning and brain image visualization. This solution has been presented to improve the comprehension of a brain’s logical proficiency to recognize and visualize objects and elements by means of noise, movements, and possible through emotions as well. This is achieved by executing the same ideas on a computer and exhibiting the objects or ideas that are produced by the brain on the screen of the computer. In relation to the tendency of humans in learning everything sequentially by amassing data and then producing a picture of it in our mind, the computers will follow the same concept by making use of datasets. Likewise, it evaluates every process consecutively by creating an image and associating it with the data presented in the dataset. After the extraction of ample data the computer further produces a pattern depending on the collected interpretations. Every time a word or a notion flashes, it tends to compare the word with the previously produced signal pattern. It also potentially exhibits the advancement of this technology for enhanced and smart robots that benefit mankind.

Epilepsy is a neurological illness of around 1-2 percent world population prevalence. The feature of epilepsy is repeated seizures known as "epileptic seizures." The human brain is also the most outstanding and sophisticated organ of any system of human body. It has great space-time dynamics. The electroencephalographic signal is the spontaneous electrical activity recorded in the brain during a short period of time. The word EEG refers to a signal from the head that is obtained from the brain. The neuronal bombardment in the brain causes it. The EEG signal provides useful information on brain function and neurobiological illnesses, as it provides visual indications of the recorded waveform and enables approaches for computer-supporting signal processing to be characterized. This is why the most advanced digital signal processing techniques for EEG signal analysis are being used. Our research focuses on analyzing and classifying the signals received by EEG employing signals, such as waves. The EEG characteristics are taken from the statistical analysis of wavelet transformation. The second aim is to enhance classification accuracy once the feature has been extracted. A total of 300 EEG data subjects were analyzed. These data have been classified into three categories: normal, epileptic, epileptic and non-seizure. We employed for this purpose a back-propagation-based neural network classifier. The second aim is to enhance classification accuracy once the feature has been extracted. 100 subjects from the set and from the data dividing the proposed algorithm for training, testing and validation were assessed for extraction and categorization of functions.

With the good development of robotic technology, good communication of robots regarded as the most sought after achievement by researchers these days. If the robot can identify the feelings and intentions of the contact person, which can lead to more robots useful. Electroencephalography (EEG) is considered one of the most effective recording methods the emotions and motives of the brain user. The various types of machine learning are as follows used successfully to separate EEG data accurately. K-closest neighbor, Besesi network, Artificial Neural and Support Vector Machine networks are within the appropriate machine learning methods for classifying EEG data. The purpose of this concept is to explore different machine learning techniques to differentiate EEG data associated with specific emotional / emotional conditions. Different ways based on differences signal-processing techniques are studied Different numbers of EEG data elements are used to identify those that give the best results different classification techniques. Various methods are designed to format the database for EEG data. Formatted data sets were tested in various machine learning techniques in order to find out which process can place EEG data accurately according to it emotional / emotional conditions. On the other hand this study is to study the electronic learning methods by various EEG tools. Continuous EEG is an exciting approach to cerebral function performance testing in intensive care unit and more. A systematic approach of this work derives various important outcome on EEG.

Phishing involves attempts to trick the user by extracting crucial important information that should otherwise not be leaked. Data like bank account numbers, social media accounts, company revenue reports, online transactions secrets are some of the examples that an at- tacker hopes to extract by exploiting the vulnerability of the user. In today’s day and age, it is very important to warn the populace against such attacks and provide relevant security awareness. Through our research, we aim to provide a brief study of how machine learning and im- age visualization can be used to detect phishing web pages using a two part implementation technique. First part involves analysis and overview of different machine learning classifiers across different datasets and features to find the most relevant technique to detect phishing websites efficiently. The second part introduces a novel approach of how discrete fourier transform and image comparison can be used to compare suspicious web pages with real ones. This study aims at providing a new direction for researchers to understand how machine learning and automation can be inculcated in phishing detection to avoid any data breach.

Agriculture productivity has large effect on the economy of the country. The agriculture productivity can be increased of the by the detect plant disease at the early stage. The automated techniques plays an important role to detect diseases at early stage and detection will be accurate. The automated techniques detect the disease when the symptoms are start appearing on the leave of the plant. This paper presents an automated technique for the plant disease detection which is based on the four operations which are pre-processing, segmentation, feature extraction, and classification. This paper also covers literature survey of various techniques which are already proposed by the authors. The symptoms analysis of the plant leaf is done using GLCM algorithm and classification of the disease is done using voting classification which are key aspects of this paper. The voting classification method is the combination of the decision trees, support vector machines, k nearest neighbor methods which will improvise accuracy of the disease detection at early stage.

The objective of this research is to analyze the security flaws of CAPTCHA generating model in order to build more resilient CAPTCHAs without such risks associated with human attempt and fail attempts. In this work, a more efficient DOCR-CAPTCHA model has presented which is deep learning approach based on an optical character recognition (OCR) to address the concerns of lower efficiency and inadequate performance of existing CAPTCHA detection algorithms. First, the DOCR-CAPTCHA model preprocesses the images to enhance the quality by following gray scale conversion, cropping and resizing the image. Second, it extracts the character to create a dictionary and mapping each character with labeling. Next, it performs classification using OCR technique and train the model. It also performed the validation on the same data. The simulation has done on the CAPTCHA images dataset. The recognition rate of this simulated model results achieved a high accuracy rate of 99.98 percent and minimized error rate of 0.0051 for the CAPTCHA train dataset. It has also compared with the existing YOLO technique and found that it has outperformed than YOLO.

This project presents the design of smart IOT based indoor farming analysis and monitoring using fuzzy logic techniques. Since climatic parameters of indoor farming are dependent on each other therefore, it is bit difficult to control them for this reason a system is designed which will monitor and analyse all the parameters on the basis of fuzzy logic algorithm. The main purpose of designed system is to monitor and to control parameters with the help of actuators present in the system to produce special quality vegetables at a faster rate and by revoking the necessity of the arduous activities generally associated with farming. Apart from that, remote monitoring and controlling of the artificial environment to eliminate the dependency on the natural habitat to ensure year-round availability of all plants and vegetables is one of the main objectives of this project. Environmental factors included air temperature, air humidity, illumination, soil moisture, and CO2 concentration. The sensor layer is IOT based consisting of Soil Moisture Sensor, Humidity and Temperature Sensor and LDR sensor. The whole system was identified on an application with a static IP and a domain name. The recorded temperature and humidity are stored in a cloud database (ThingSpeak), and the results are displayed in a webpage, from where the user can view them directly. So this project is to implement the automation in Farming system, where it increases the efficiency in farming effective water irrigation, motion monitor around the indoor field and fertility of soil.

Stroke is the world’s second-largest cause of death, and it continues to be a significant health burden for both people and national healthcare systems. Hypertension, heart illness, diabetes, glucose metabolism dysregulation, atrial fibrillation, and lifestyle variables are all potentially modifiable risk factors for stroke. Stroke is a medical emergency. A stroke occurs when blood flow to a part of your brain is interrupted or reduced, preventing brain tissue from getting oxygen and nutrients. Brain cells begin to die within minutes. The authors aimed to derive a model equation for developing a stroke pre-diagnosis algorithm with the potentially modifiable risk factors. Ischemic embolic and haemorrhagic strokes account for the bulk of strokes. An ischaemic embolic stroke occurs when a blood clot develops distant from the patient’s brain, generally in the heart, and travels via the circulation to lodge in the patient’s smaller brain arteries. Another form of brain stroke is haemorrhagic stroke, which occurs when a blood vessel in the brain ruptures or spills blood. Stroke is the world’s second-largest cause of mortality and one of the most life-threatening diseases for people over the age of 65. By the method proposed, we could mitigate the strokes occurring by approximate 96 percent of the items from the data received from the patient.