European Journal of Molecular & Clinical Medicine

These days, a lot of information is exchanged on social media, and it can be difficult to tell what information is accurate and what information is false. As soon as they read the content, people start sharing their problems or ideas without first checking your validity. Its spread is a result of this as well. The most common sources of misleading and unverified information are rumours and fake stories, which should be exposed as soon as possible to prevent their unexpected effects. Online forums are where most smart phone users choose to read tales. News websites disseminate breaking news and offer a source of confirmation. How to spread news and articles on social media platforms like WhatsApp groups, Facebook pages, Twitter, and other tiny blogs and social networking sites is the subject at hand. It is risky for the general population to take these rumours and news stories seriously. There is an urgent need to put an end to rumours, especially in growing nations like India, and to concentrate on legitimate, established issues. This essay demonstrates a paradigm and a technique for gathering misleading information. The proposed model's outcomes are contrasted with those of other models. The suggested model performs well and explains the results' accuracy to a maximum of 93.6% accuracy.

Stroke is a medical disorder that harms the brain by rupturing the blood vessels there. It can also happen when the passage of blood and other nutrients to the brain is interrupted The World Health Organization (WHO) claims that stroke is the main global cause of mortality and disability. The prediction of heart attacks has been studied, however likelihood of a brain stroke is depicted in very few works. Due to this evertheless, numerous machine learning models are created to forecast the potential for a brain stroke. This essay contains a variety of physiological variables with machine learning techniques, such as Decision Tree Classification, Random Forest, and Logistic Regression K-Nearest Neighbors, support vector machines, and classification likewise Naive Bayes.

A major challenge in the search for a cure for cancer is predicting the illness status of the disease. For instance, distinguishing between benign and malignant tumours helps doctors diagnose cancer more accurately. Although technology advancements produced data on patients with various illness stages, it would be crucial to assess how well machine learning algorithms accomplish predictions. In this article, we suggest employing machine learning algorithms such a variation of AdaBoost, deepboost, xgboost, and support vector machines. We then analyse them using area under curve and accuracyon actual clinical data linked to thyroid cancer, colon cancer, and liver cancer. Results from experiments demonstrate the SVM's strong performance.

Agriculture is essential to the Indian economy. Population growth faces the most serious threat to food stability. Population growth raises demand, facing farmers to produce more to keep up with the demand. Crop yield prediction technology can assist ranchers in improving efficiency and productivity. Correct manure rates are required for the cultivation of oilseed crop yield. When nutrients are scarce or over-fertilized, yields are significantly reduced and the environmental burden is enhanced. To resolve these concerns, our proposed work employs machine learning techniques in the prediction of the yield of oilseed crops using organic manure as well as the amount and type of agricultural manure to be used for a specific crop in different districts of Tamil Nadu. The training set consists of actual yield data from 1961 to 2007 and the validation set consists of data from 2008 to 2019. The proposed algorithm’s results are compared to those of other machine learning algorithms namely bagging, random forest, linear regression and naive bayes with accuracy rates of 98.5%, 96.5%, 94.5%and 92.5% respectively. According to the study, bagging (Bootstrap Aggregation) outperforms other algorithms for crop yield prediction, while the boosting algorithms perform better for recommendation systems for determining which crop to plant, which type of organic manure to use and how much manure to use in a specific area and time.

Monitoring the traffic of the network for social media servers has become important task to avoid malicious traffic. DDOS attacks can crash the server by denying the service with malicious traffic. Various Machine Learning (ML) models has developed to identify the real traffic and malicious traffic based on network parameters. The aim of the research work is to study the performance of the various machine learning models on DDOS datasets. The feature selection methods are evaluated with ML models. The study concludes the high performance has achieved by using PCA feature selection technique on DDOS classification datasets.

Diabetic retinopathy is an eye-related neurological disorder, the diabetic patient eye damaged by blood vessel in the retina area of the eye. Computational methodology is a proper way for detecting and predicting the diabetic retinopathy disease. The aim is to identify and detect the Diabetic Retinopathy, so this present work focusses on detection of Diabetic Retinopathy. This work proposed the novel WMD-MSVM -Weighted Mahalanobis Distance based Multiclass Support Vector Machine oriented; upon Diabetic Retinopathy diagnosis system for the purpose of feature selection, also ROI extraction method being utilized to fetch features from Diabetic Retinopathy images. From the results, it is clear that the performance of WMD-MSVM on instance selected training dataset yields improved detection accuracy compared with the performance of WMD-MSVM on full-training-dataset. There is an improvement of around 1% of detection accuracy in case instance selected dataset. This proposed work is benefit for diabetic patients to gain the proper treatment by physicians at an early stage for Diabetic Retinopathy. This computational approach to detect   the diabetic which results the best solutions for ophthalmology. The diabetic image analysis and machine learning approach considered as a challenging research area that aims to provide a computational approach to assist in the early diagnosis and detection of Diabetic Retinopathy problems.

Over the past two decades, artificial intelligence (AI) and machine learning (ML) have undergone considerable development. There have been various applications in medicine and dentistry. Their application in orthodontics has progressed slowly, despite promising results. The available literature pertaining to the orthodontic applications of AI and ML has not been adequately synthesized and reviewed. This review article provides orthodontists with an overview of AI and ML, along with their applications. It describes state-of-the-art applications in the areas of orthodontic diagnosis, treatment planning, growth evaluations, and in the prediction of treatment outcomes. AI and ML are powerful tools that can be utilized to overcome some of the clinical problems that orthodontists face daily. With the availability of more data, better AI and ML systems should be expected to be developed that will help orthodontists practice more efficiently and improve the quality of care.

In an information-intensive culture, computer technology has begun to reach other conventionally realizable sectors. Alongside this trend, several high-tech machines have emerged. Concurrently, a variety of high-tech devices have emerged in the traditional medical area in order to aid physicians in the treatment process. The introduction of sophisticated picture recognition technology has decreased a significant amount of the doctor's effort required to evaluate the tiny sick cells within the human body. The modernization, rationalization, and intelligence of the design and production of illness detection equipment utilizing image extraction technologies are increasing. In the medical industry, feature-based image recognition technology disease diagnosis equipment analyses and diagnoses pathology using picture collecting. From 2018 to 2022, the current study examines several image extraction algorithms applied to the identification of canine illnesses. Using classification, researchers have reached a high level of sensitivity and specificity in medical picture analysis. This evaluation not only identifies obstacles, but also identifies and presents fresh research prospects for this field's scholars

In a modern environment, every organization becomes an industrial technology to manage the entire industry and help improve the performance of its employees. By empowering leaders to adopt new technologies that increase the effectiveness of human management, environmental competition has enabled workers to be more productive and productive. IoT has proven to be an important part of the organization because it allows you to attract potential customers through the automated and robotic process using ML and other formats to help you achieve greater productivity and get more jobs in less time. Today's market is based on the use of technology for the rapid delivery of data and information used to make smarter decisions. The key is to understand how these tools help to identify the full potential of employees and improve their performance in different industries.

Dementia, often known as Alzheimer's disease, is a serious neurodegenerative condition that kills brain cells and causes irreversible memory loss. The global burden of disease from a Disenormous. In order to stop the course of Alzheimer's disease (AD), early detection is essential. For him and his family, early diagnosis of Alzheimer's disease is really helpful. In this publication, we examined previous research on detecting Alzheimer disease using Machine Learning (ML) and Deep Learning (DL) techniques. We examined numerous machine learning and deep learning approaches in this survey to compare them and determine which one performs better

The infection of the covid-19 fears the world population. As prevention strategies have not been developed, existing clinical approaches are only applicable to treat covid-19-positive individuals. Identifying the severity of the patient's illness is crucial for reducing the covid-19-related mortality rate. It is the pathology reports that are used as the foundation for determining the severity of the disease by the clinical specialists. However, a clinician's skill in making a diagnosis has a significant impact on how correct that diagnosis turns out to be. This manuscript described a supervised learning technique for performing computer-assisted covid-19 mortality scope using the pathology reports of the target patient. The experimental examination of the value of the suggested approach for anticipating mortality scope with few false alarms.

A text classification is a well formed process using various measurable properties and computerized logical procedure to fetch a pattern from different classes.Since classification is important for the pattern recognition process, there are some issues with well-formed classification in this process, which is one of the important issues for proper development and improvement of productive data examinations. On behalf of the versatility of learning and the ability to deal with complex calculations, classifiers are consistently best suited for design patter recognition issues. The aim of this paper is to present a result based comparative study of different classifiers and the optimal recognition of results computation through the Devanagari Handwritten characters and numerical values. Different classifiers were used and evaluated in this investigation including k-Nearest Neighbor (k-NN), Support-Vector machine (SVM), Naïve Bayes, Decision Tree, Random Forest, and Convolution Neural Network (CNN). To accomplish the experiment purpose, this paper used an unbiased dataset with including 123 samples that consists of 123 characters and 123 numerical values. Python 3.0 with sciket learn machine learning open-source environment library have been used to evaluate the performance of the classifiers. The performances of the classifiers accessed by considering the different matrices including dataset volume with best split ratio among training, validation, and testing process, accuracy rate, Ture/False acceptance rate, True/False rejection rate and the area covered under the receiver operating characteristic curve. Similarly the paper shows the correlation of the accuracy of the experiments obtained by applying to chosen the classifier. On behalf of the exploratory results, the
infallible classifiers considered in this test have free rewards and must be executed in a hybrid manner to meet the thigh precision rates.In the views of test work, their result compressions and the examination to be performed, it is argued that the Random Forest classifier is performing in a way that the current use of the classifier to recognize the Devanagari Handwritten character and the numerical values with the accuracy rate 87.9% for the considered 123 samples.

More than a year since the novel coronavirus was first discovered, its presence is
still prevalent throughout the world. In such grave situations if technology can help
humans combat the spread, then why not explore it. Therefore, keeping this in mind,
several researchers have already started investigating artificial intelligence algorithms to
find solutions to predict coronavirus using X-ray images of chest. But due to lack of
dataset during the initial days of their research, many came up with framework using pre
trained image classification models such as VGG-16, Inceptionv3, ResNet-50 and others.
In this paper, the performance of two machine learning algorithms which are support
vector machine and decision tree has been evaluated. Further developed deep learning
model applying convolutional neural network to classify the chest x-ray images as covid-19
or normal. The final CNN model was also integrated with a user interface and hosted on
web server for easy access which allows anyone to upload the chest x-ray image from his
computer or mobile and check the result.

Burnout Syndrome or burn syndrome is a disease caused by exhaustion or work
overload, this has a high impact due to the consequences occurring in different
situations. This document presents a systematic review of the literature on the
impact of information technologies to measure the level of burnout syndrome.
We reviewed 65 articles from the period 2015-2020 on Burnout syndrome with
indicators like causes, consequences and treatments from the databases IEEE
Xplore, Sciencedirect, SpringerLink and Proquest

The main reason of increasing mortality rate among women is the breast cancer. It makes several hours with the less availability of systems to identify the diagnosis of cancer manually. Hence there is a need to develop an automatic system for early detection of cancer. Several researchers have focused in order to improve performance and achieved to obtain satisfactory results. But unfortunately it will be very difficult to detect the cancer in beginning stages because the symptoms may be inappropriate.Therefore, there is a need to determine and acquire a new knowledge to prevent and minimizing the risk of getting effected with cancer. Machine learning (ML) is algorithms are widely used in detecting breast cancer patterns and predict the grading level. Machine learning techniques can be used to classify the stage of cancer, where machine can be trained from past data and build a model so that it can predict the category of new input.In this paper we used K-nearest neighbors (K-NN) and Support Vector Machine (SVM) on the dataset collected from UCI repository to detect breast cancerwith respect to the results of accuracy the efficiency of algorithm is also measured and compared.

One of the biggest revolutions of modern history is the invention of agriculture for a healthier lifestyle. It significantly changed the human culture and played an important role in the development of the population and biological improvements in food production and domestication. Study into agriculture is then planned by improving the disease diagnostics method with the use of newer information technology to enhance efficiency and quantity for agricultural production and its allied operation. This project focuses on the identification and diagnosis of plant leaf diseases of tomatoes and pomegranate based on visual symptoms, anthracnose, and powdery mildew. Machine learning and image processing using SVM, KNN require many steps to identify and distinguish disease signs

The main aim of this paper is to know whether the temperature have any impact on the increase of corona virus. Covid-19, this name has brought a drastic change in our day-to-day life. People of Telangana have lived through 10 months of the Covid-19 pandemic and there might be more to come. Till date 2.8lakh official cases of covid-19 have been registered in Telangana and there may be many more which have gone unnoticed. In our busy life, we are neglecting our health and no one is maintaining a proper hygiene. And we have been more addicted to junk foods rather than nutrition, because of this reason covid-19 became a threat to our life. So proper precautions and awareness must be spread among people to avoid spread of virus. Fever, cough, breathing problems etc are the
symptoms of this covid-19. If we neglect these symptoms it leads to a severe problem like pneumonia, kidney failure and eventually leads to death of that person. At this moment we don’t have any vaccine to cure this disease, the only prevention or avoiding corona is to boost our immune system. To overcome this pandemic situation, firstly we need to know the important factors that increases in covid-19 cases. In this paper, machine learning techniques are used to identify how temperature varies with the increase of covid-19. Which means we find how the effect of temperature depends on the number of covid-19 cases in Telangana

One of the main challenges in machine learning classification is handling
imbalanced data because imbalanced data can produce result bias towards the majority
class and a poor performance of classification. Therefore, in this paper, an improved
workflow is introduced to cater this issue. After combination of Synthetic Minority Oversampling
Technique (SMOTE) and Tomek Links or known as SMTmk method is
performed, additional step is required to further increase the performance of machine
learning classification especially in Specificity field. The step is completed by reducing the
number of majority class based on the ratio of minority class. Three machine learning
algorithms is used to test the classification result which are Extreme Gradient Boosting,
Random Forest and Logistic Regression. Result recorded in this research shows that the
ratio of 7 to 1 is better than the established methods which are SMOTE and hybrid method
of SMOTE and Tomek Links.

In today’s world, digitization plays an extremely prominent role in day-to-day applications.Its future deployment, needs an Internet of Things (IoT) to embrace automation, remote monitoring and predictive analysis. IoT is a device connected with an internet and it’s a combined embedded technology including actuator and sensor device. Also, it encompasses, wired and wireless communication devices, and real-world physical objects connected to the
internet. IoTis majorly used in diversified fields like smart classroom, smart banking, smart home, smart agriculture, smart healthcare application etc. Typically, IoT requires intelligence, to achieve theautomation process in an efficient way in many applications. Artificial Intelligence (AI) paves the way to makes the IoT smarter and efficient by its approaches. Due to enormous amount of data being generated in various applications, IoT combined with Machine Learning(ML) and Deep Learning(DL) models is used to enhance the functionality in complex applications. In this survey the applicationof AI, ML and DLmodels deployed in IoT are deeply explored.

Autism spectrum disorder (ASD) is a psychiatric disorder which leads to
neurological anddevelopmental growth of a person which starts in early age and gets
carried throughout their life.It is a condition associated with significant healthcare costs
and early diagnosis can reduce these.Unfortunately, waiting time is lengthy for an ASD
diagnosis and it is cost effective. Due to theincrease in economy for autism prediction and
the increase in the number of ASD cases across theworld is in need of easily implemented
and effective screening methods by GUI results. Toovercome the time complexity for
identifying the disorder advanced technologies can be used suchas machine learning
algorithms to improve precision, accuracy and quality of the diagnosisprocess. Machine
learning helps us by providing intelligent techniques to discover the affectedpatient, which
can be utilized in prediction and to improve decision making. And hence, wepropose the
data set features related to autism screening of adult and child to be used for
furtheranalysis and to improve the classification of ASD cases.

Non-functional requirements play a critical role in choosing various alternative model and ultimate implementation criteria. It is extremely significant in the earlier stages of software development that requirement engineering produces successful technology and eliminates system failure. The recent work has shown that the automated extraction and classification of quality attributes from text files have been demonstrated by artificial intelligence approaches including machine learning and text mining. In the automated extraction and classification of nonfunctional specifications, we suggest a supervised categorization approach. To test our approach to obtain interesting outcomes, a very well-known dataset is used. In terms of security and performance, we obtained a specific range of 85% to 98% and obtained a best result together for security, performance and usability.

Web 2.0 facilitates the expression of views through diverse Internet applications which serve as a rich source of information. The textual expressions have latent information that when processed and analysed reveal the sentiment of the user/people. This is known as sentiment analysis, which is the process of computationally extracting the opinions and viewpoints from textual data and it is also known as opinion mining, review mining or attitude mining, etc. Aspect-level sentiment analysis is one among the three main types of sentiment analysis, where granule level processing takes place in which the different aspects of entities are harnessed to identify the sentiment orientations. The emergence of machine learning and deep learning techniques has made a striking mark towards aspect-oriented sentiment analysis. This paper presents a survey and review of different works from the recent literature on aspect-based sentiment analysis done using machine learning techniques.

Emerging growth of smart devices and application of smart screens in every consumer product enable the peoples to get compulsive interaction with the touch screen. Smart devices with flexible interactive option and evaluation of data analytics and machine learning keeps the humans get addicted with the devices day and night. Even though it offers us numerous advantages over time, Studies revealed that physiological changes are more prospected due to excessive interaction with the smart devices. Nowadays, human health history is ruined by technology in the form of endless connection with such device usage for long period which causes health issues like mental stress, behavior related problems. There are many sources to address stress in our routine life. Many studies have depicted the most impacted factors in smart devices. This paper focus on detailed study depicting the impacts of smart devices human body influenced by touch screen devices highly towards sense organs. Moreover, the biggest sense organ skin that acts as a communication medium for conducting such tiny electrical signals got affected a. Evaluation of big data analytics everywhere in medical industry to keep the patient information more secure and provides statistical view of such problem globally. Healthcare analytics has been changed into digital mode to keep patient’s data most secure, less expensive, and easily accessible. Here, the problem begins with touch screen-based intervention in stress, including literature review in contrast with previous work in blood pressure and the proposed evaluation approaches.

Heart disease (HD) is one of the most common diseases, and early diagnosis of this disease is a vital activity for many health care providers to avoid and save lives for their patients. Heart disease accounts to be the leading cause of death across the globe. Health sector contains hidden information which helps in making early decisions by predicting existing disease such as coronary heart disease using machine learning methods. The proposed Hybrid Linear Regression Model (HLRM) implemented in two phases. Initially, data preprocessing is done; missing values are imputed with KNN and simple mean imputation and next Principal Component Analysis is used to extract the most contributing attributes for the cause of disease. Second, Stochastic Gradient Descent is the linear regression used to record the probability values of dependent variables, in order to determine the relationship between the dependent and independent variables. The overall prediction accuracy of the proposed model is observed as 89.13%. The outcome of this study will help as a reference for medical practitioners and also as a research platform for the academia

The emergence of scalable frameworks for machine learning to efficiently analyse and derive valuable insights from these data has triggered growing volumes of data collected. Huge spatial data frameworks cover a wide variety of priorities, including tracking of infectious diseases, simulation of climate change, etc. Conventional mining techniques, especially statistical frameworks to handling these data, are becoming exhausted due to the rise in the number, volume and quality of spatio-temporal data sets. Various machine learning tasks have recently shown efficiency with the development of deep learning methods. We therefore include a detailed survey in this paper on important impacts in the application of deep learning techniques to the mining of spatial data.

The instinct of human beings is very complex. The understanding of this instinct needs a strong
dimensional analysis of the knowledge of discourse. The computer systems are now trained to realize
how things operate in a real-time environment for intelligent analysis. This effort although very
progressive has a restriction. There is an intelligence gap which makes human one step above the
machine. The fuzzy logic system can be employed to make a machine understand this intelligence gap
in a better way. In other words, fuzzy logic is a computational Intelligence technique that makes a
computer understand and think the way humans do. The fuzzy logic system is now attracting
scientists and engineers around the world since by integrating its abilities with soft computing
techniques like neuro and chaos computing, genetic algorithm, probability reasoning, and immune
networks, it can handle the problems that had not been solved before. Not only has the fuzzy system
significantly enhanced knowledge-based or expert system technology, but it has also primarily
changed the granularity of intelligence. For example, with the help of the fuzzy logic system,
industrialists of home appliances are today embedding intelligence in specific productsThe aim of this
study is two folds: first, to understand the fuzzy logic system for effective decision making and
second, to demonstrate the presence of this intelligence gap through real-time examples. The
examples are selected cautiously to illustrate and demonstrate the applications of the fuzzy logic
system for every reader.

Today internet is playing a vital role in the modern world. From young aged children to old aged people are utilizing the technology. By the use of this technology to determine the mental taught of the student towards the learning environment the sentimental analysis method is performed. For this they created a separate portal for the student which has unique ID and password for individual one. Inside the web portal it consists of various sets of question and it records the performance of the student towards it. The feedback can be subdivided into three categories mainly positive, negative, and neutral. By using the feedback they can predict the sentiments of the student. By using the machine learning algorithm they can predict the sentiment of the students in the learning aspects. Student sentiment has been get distinguished by the use of the polarity. This is can uses the random forest algorithm. The random forest algorithm can provides the accuracy of about 92% with effective outcome.

Skin types of diseases are most common among the globe, as people get skin disease due to inheritance, environmental factors. In many cases people ignore the impact of skin disease at the early stage. In the existing system, the skin disease are identified using biopsy process which is analyzed and medicinal prescribed manually by the physicians. To overcome this manual inspection and provide promising results in short period of time, we propose a hybrid approach combining computer vision and machine learning techniques. For this the input images would be microscopic images i.e histopathological from which features like color, shape and texture are extracted and given to convolutional neural network (CNN) for classification and disease identification. Our objective of the project is to detect the type of skin disease easily with accuracy and recommend the best and global medical suggestions.
This paper proposes a skin disease detection method based on image processing and machine learning techniques. The patient provides an image of the infected area of the skin as an input to the prototype. Image processing techniques are performed on this image and feature values are extracted and the classifier model predicts the disease. The proposed system is highly beneficial in rural areas where access to dermatologists are limited. For this proposed system, we use Pycharm based python script for experimental results.

Malicious software (malware) plays a vital role in cybercrime security. As the number of malicious attacks and its target sources is increasing, it is difficult to find and prevent the attack due to its change in behaviour. Most of the traditional malware detection models are based on the statistical, analytical, and machine learning models. Detection of malware usually utilizes virus signature methods to defend against malicious software. Most antivirus tools to categorize malware depend on regular expression and pattern. Antivirus is less likely to update their databases to detect and prevent malware as file features have to update a newly created malware. The practically maximum human effort was required in order to generate attack signatures. In this paper, different types of malware detection models and their problems are discussed. This paper provides an extensive survey on the malware attack detection using traditional supervised, unsupervised models. Different types of malware attacks and their variations in behaviour are discussed in offline and online systems.

Identification of disease at a beginning stage is very essential for higher treatment. It’s a awfully complicated task for medical researchers to predict the illness within the early stages because of delicate symptoms. Typically the symptoms turn out to be evident once it's too late. to beat this issue, this project aims to boost disease designation victimization machine learning approaches. The most objective of this analysis is to use categorization techniques to spot the liver patients from healthy people. This project conjointly aims to match the categorization techniques supported their presentation factors. To serve the medical community for the designation of disease between patients, a graphical computer interface is urbanized victimization python (Node RED). The GUI will be promptly used by doctors and medical practitioners as a screening tool for the disease.

In today’s world online rating systems are extensively used for making their decisions on a particular product or item on the web. For getting benefit, some people are always trying to manipulate corresponding systems by giving unreasonable ratings. So, resolving true ratings of such products comes extremely important and it is a crucial problem. Fake reputation is the problem that is being occurred by unreasonable ratings. In this paper we propose Multi linear regression algorithm in machine learning which will give correct reputation by eliminating unreasonable ratings

Antigenic drift in epitopic part of a virus, especially for Hepatitis C Virus is a well-known fact. However, this problem can be overcome due to the fact that the epitopes are dispersed amongst a few proteins that are already filtered out by researchers. Minor changes or variation in the sequential structure of this protein group results in amendment of their epitope structures which ultimately renders any vaccine or drug ineffective against the target organism. Therefore the problem is reduced to first revisiting the identification steps of altered sequences of these 10 proteins which is quite achievable experimentally and secondly identification of epitopic part out of these altered peptide sequences. Hidden Markov models (HMMs) have been comprehensively deployed in analysis of bio-molecular sequences. The work presented in this paper deals with the recognition step of epitopes through probabilistic machine learning model Viterbi of HMM and achieved significantly high efficiency towards this direction. As a consequence, a considerably high precision was obtained for T-cell based linear epitope recognition.

Machine learning based outlier detection methods are widely used in various domains. However, an ensemble of such detection methods could leverage detection performance. The existing ensemble methods made up of multiple unsupervised learning algorithms lack in ideal strategy for choosing right candidates as constituent detectors. It resulted in mediocrity in model stability and accuracy. To overcome this problem, in this paper, we propose an ensemble framework based outlier detection system in high dimensional data. It has ideal mechanism for effectively choosing base outlier detectors. Out of many candidate outlier detectors, the ones that yield highest performance are combined. An algorithm named Average Selection and Ensemble of Candidates for Outlier Detection (ASEC-OD). Many real world datasets are used for empirical study. The results of experiments revealed that the proposed framework outperforms many existing methods

The agriculture plays a prevailing job in the development of the nation's economy. Atmosphere and other natural changes has become a significant danger in the agribusiness field. AI is a fundamental methodology for accomplishing viable and viable answers for this issue. Harvest Prediction includes anticipating the best output from accessible authentic information like climate parameters and soil parameters. This recommender system uses real time data as input to the machine learning. The sensors collect data from the soil and send that data to the cloud (firebase). Then the machine learning model retrieves that data and predicts the best crop and sends that crop to the cloud. We develop an android application which retrieves the sensor values from the cloud and displays them. This forecasting facilitates the farmer to forecast the best crop earlier than cultivating onto the agriculture field, which in turn increases the productivity.

A large number of researches have been conducted in the field of AI. This paper is all about the enhancements made in this popular field. Making a machine that is able to understand the background ideas of the words is very essential as it can increase the chances of better translation as well as can execute conversations as humans do. In particular, this paper states the difference between the AI and the Non-AI tasks. The work is generated for new candidates coming in the area of AI as well as some issues related to AI are also talked about.

This paper audits and talks about examination propels on "logical AI" in PC vision. We centre on a specific zone of the "Seeing People" (LAP) topical space: primary imitations and character investigation. Our point is to variety the computational knowledge and PC vision networks mindful of the significance of creating logical systems for PC helped dynamic applications, for example, robotizing enlistment. Decisions dependent on character attributes are being made routinely by human asset offices to assess the up-and-comers' ability of social inclusion and their capability of profession development. Be that as it may, deducing character attributes and, as a rule, the procedure by which we people structure a first impression of individuals, is profoundly emotional and might be one-sided. Past investigations have shown that knowledge machineries can figure out how to imitate human choices. In this paper, we go above and beyond and figure the issue of clarifying the choices of the models as methods for distinguishing what visual perspectives are significant, seeing how they identify with choices recommended, and potentially picking up knowledge into unfortunate negative inclinations. We structure another test on reasonableness of knowledge machineries for first impressions examination. We portray the setting, situation, assessment measurements and starter results of the opposition. Supposedly this is the first exertion regarding difficulties for logic in PC vision. Moreover, our test configuration involves a few other measurable and subjective components of oddity, including a "competition" setting, which joins rivalry and coordinated effort.

Adaptive join algorithms have recently attracted a lot of attention in emerging applications that provide data through autonomous data sources in diverse network environments. Their main advantage over traditional joining technologies is that they can give the results they join as soon as the first input duplex is available, thereby improving the pipeline by joining the results and hiding the source or network delay. In this paper, we first suggest the new adaptive two-way join algorithm DINER (Dual Indexed-Loops Reactive Join) to increase the result rate. Diner combines two main components: the novel Retrofit technology, which allows algorithms to quickly switch between memory processing and a clea
r flushing approach aimed at increasing the productivity of memory tuples in producing results in the stage of reaching online. We are expanding the application of specific technology for a more challenging setup: managing more than two inputs. The Multi Active Relational Join Algorithm (MARA) is a multi-path joint operator that claims its principles from DINER. Tara surpasses previous compatible joint algorithms in our experiments with real and synthetic data sets, makes the best use of available memory and produces duplicates of results at significantly higher rates. In the presence of multiple experiments, our experiments show that MARA can produce a high percentage of initial results and surpass existing technologies for adaptive multi-path joining.

The ability to predict the reaction of breast tumors to neoadjuvant chemotherapy from the get-go over the span of treatment can delineate patient’s dependent on the reaction for explicit tolerant treatment procedures. From now on, reaction to neoadjuvant chemotherapy is measured as being based on physical examination or breast imaging (mammogram, mri, or normal MRI). There is a powerless connection with these projections and with the actual tumor size as measured by the pathologist through authoritative procedure. Given the numerous options open to Neoadjuvant chemotherapy (NAC), it is important to develop a plan to predict response over the care period. Sadly, as long as certain people are not seen as responding, their condition can never again be specifically resectable, so this situation should be preserved at a strategic remove from progressing response appraisal protocols throughout the care regimen. This paper provides a review of all the existing frameworks of machine learning involved to perform accurately neoadjuvant chemotherapy responses

Healthcare is a predictable task to wipe out human life. Coronary heart disease is sickness that impacts the human coronary heart. Cardiovascular sicknesses will forecast with the aid of several techniques that helped in making choices about the modifications that maintain excessive-risk patients which resulted in the discount of their dangers. The purpose of demise ratio of those sicknesses may be very high. It is very imperative to become aware of if the individual has heart disorder or now not. In medical field it is very important to find the occurrence of prediction of the heart diseases. Accurate Prediction results are very efficient to treat the patient’s medical history before the attack occurs. The techniques Data mining and Machine learning plays a essential role to predict the occurrence of heart diseases. These techniques diagnose these diseases with the help of dataset in healthcare centers. Various models used to reduce the number of deaths ratio. Models based on several algorithms such as Support Vector Machine (SVM), Decision Tree(DT), Naïve Bayes(NB), K-Nearest Neighbor(KNN), and Artificial Neural Network (ANN) are implemented to predict heart disease. The accuracy of these models helps to diagnose the diseases with better results. This paper summarized the performance of all algorithms which are used to predict and diagnose heart diseases.

AI is the usage of man-made consciousness (AI) that enables systems to subsequently to take in and improve as a matter of fact without being unequivocally modified. AI focuses on the improvement of PC programs that can get to data for learning itself. Machine Learning tasks are classified into supervised, unsupervised, and reinforcement learning. In this paper, we discuss in-depth comparisons of all supervised and unsupervised algorithm.

The Stock market is a shambolic place for prediction as there are plenty of factors that affect the stock market simultaneously. Numerous studies have been conducted regarding this field, in hopes that one day accurate stock values can be predicted. This paper introduces a hybrid algorithm that incorporates Twitter sentiment analysis and Long Short Term Memory to predict next day closing values of a stock. Our proposed algorithm exploits the temporal correlation between public sentiment and its effect on stock values. We use Part-of-speech tagging to perform sentiment analysis and Long Short Term Memory for foretelling the next day closing price of the stock, both of these combined gives us a decent picture regarding the future of the stock.

Utilization over Internet has been altogether expanded during most recent couple of
decades. People groups saving additional time via web based networking media locales. In
this exploration proposition, we are intrigued to anticipate the character of clients by
assessing their tweets. Up to this point, to accurately measure customer’s characters, they
predictable to get a character test. Th is made it unrealistic to utilize character examination
in plentiful online networking spaces. In this examination proposition, we apply neural
systems by which a client's character can be precisely anticipated through the freely
accessible data on their T witter profile. We will portray the sort of information gathered,
our strategies for assessment , and the AI methods that permit us to effectively foresee
character. This data is essential for organizations to target possible buyers or look for client
suppo sitions in case of enhancement as a business methodology. In this way, this work
examines online networking information to anticipate huge character characteristics, for
example characteristics or qualities explicit to a person. The main strides towards we b
based life locales, raises information size and volume. The measure of information that is
transferred to these person to person communication administrations is expanding step by
step. Along these lines, there is gigantic prerequisite to contemplate the exceptionally
unique conduct of clients towards these administrations. This is a starter work to
demonstrate the client designs and to contemplate the viability of AI prescient displaying
approaches on driving long range interpersonal communication admini stration Facebook.
We demonstrated the client remark patters, over the post on F B Pages anticipated that
what number of comments a position is required to obtain in next H h ou rs.

Girls and Women have been encountering a ton of savagery and badgering in broad daylight places in different urban communities beginning from following and prompting inappropriate behaviour or rape. This paper examines essential centres around the function of web-based media in advancing the security of ladies in different areas with exceptional reference to the part of online media sites and applications including Twitter stage Facebook and Instagram. This paper additionally focuses around how a feeling of obligation on part of culture can be built up the basic Indian individuals. Tweets on Twitter which typically contains pictures and text and furthermore composed messages and statements which centres around the security of ladies in different urban areas can be utilized to peruse a message among the Youth Culture and instruct individuals to make exacting move and rebuff the individuals who disturb the ladies. Twitter and other Twitter handles which incorporate hash label messages that are generally spread over the entire globe as a stage for ladies to communicate their perspectives about how they feel while we go out for work or travel in a public vehicle and what is the condition of their brain when they are encircled by obscure men and if these ladies have a sense of security? By analyzing the tweets polarity from the Twitter API. In Further improvements, we can use it in any Social Media Platform.

In today’s internet era various websites through which a number of individuals purchase items. There are certain online forums which request their users to provide confidential data such as card number, cvv, pin number etc. for various malicious practices. These websites are referred as Phishing Websites. Therefore, to distinguish between the authentic website and the malicious website we suggested an intelligent, adaptable, and efficient model that utilizes Machine learning techniques. We carry through the project using the algorithm of classification and different methods to gather the phishing websites dataset to verify its validity. These spoofing websites are differentiated on certain significant attribute such as encryption standards, Domain Identity, URL and security. The project will utilize machine learning concept thus informing the user if the website is legal or not. This software is highly secured and can be utilized by many E-commerce ventures so as to provide hassle free transaction. Machine Learning design utilized in the project gives good results when compared with other standard classification algorithms. Detection of Phishing web site is ML intelligent and effective model that’s supported victimization classification or association data processing algorithms. The algorithms we are using here is logistic regression. We are also using decision tree classifier so that we can make a point-to-point comparison between them which will help us to know parameters like accuracy and time taken.

Confidence and security prevent businesses from fully embracing cloud platforms. To protect the
clouds, providers must first secure virtualized data-center resources, uphold user privacy, and protect data
integrity. The authors suggest that the trust overlay network be used through multiple data centers to implement
a reputation system to build trust between service providers and data owners. Data coloring and software
watermarking methods protect shared data objects and highly distributed software modules. These strategies
protect multi-path authentication, enable a single sign-on in the cloud, and tighten access control for sensitive
data in the public and private clouds. Protection against tampering is tamper proofing, so unauthorized changes
to the software (for example, removing a watermark) can lead to passive code. We will briefly examine the
technology available for each type of protection. P2P technology opens our work to low cost copyrighted
content delivery. The advantages are mainly delivery cost, high content availability and copyright compliance in
exploring P2P network resources.

In recent years, it is in public to use the surveillance cameras for continuous monitoring of public and private spaces because of increasing crime. Most current surveillance systems need a human operator to constantly watch them and are ineffective as the amount of video data is increasing day by day. Surveillance cameras will be more useful tools if instead of passively recording; they generate warnings or real-time actions when unusual activity is detected. But recognizing and classifying human activity as normal or abnormal from a live video stream is a stimulating job in the pitch of CPU vision. There is a need for a smart surveillance system for the automatic identification of abnormal behaviour of humans for a specific-scene. Presentpaperstretches an overview of different machine learning methods used in recent years to develop such a model. It also gives an exposure to the recent works in the field of anomaly detection in surveillance video and its applications

Breast Cancer is one of the life threatening disease among females all over the world. This killer disease however when it can be detected in its early stages can be a life saver for many. Radiologists uses the mammography images to detect the presence and absence of Breast Cancer. The field of Bio-informatics leverages the Machine learning techniques for diagnosis of Breast cancer in particular. This research work experiments with the two most popularly used Supervised Machine Learning Algorithms, K-Nearest Neighbour and Naive Bayes. This work predicts Breast Cancer on the The Breast Cancer Data Set (BCD) taken from the UCI Machine Learning Repository. A comparative analysis between the two approaches are made in terms of its performance metrics using CV techniques. The proposed work has achieved a best accuracy of 97.15% by employing the KNN algorithm and a lowest error rate of 96.19% using NB classifier.

Real-time student engagement tracking is an important step towards education. Current approach doesn’t consider student engagement detection using biometric features. In this project, we propose a hybrid architecture invoking student’s eye gaze movements, head movements and facial emotion to dynamically predict student attention and engagement level towards the tutor and based on the output value the content is changed dynamically. Hence this concept has a huge scope in e-learning, class room training, analyse human behaviour. This project covers main process like Eye Ball, facial emotion and head movements Human Beings. For feature extraction step, we used Principal Component Analysis (PCA) for facial emotion recognition, Haar Cascade for pupil detection and Local Binary Patterns for recognizing head movements and OpenCV for machine learning model generation and comparison.

This research deals with the major problem that we face in major part of India and across most of the world. The research deals with the main problem of traffic congestion and road accidents that is basically caused because of the improper parking management. So, it is mandatory for all the cities to have a well managed parking system. However, in the past many researches has been conducted to propose an solution that leads to suitable smart paring algorithm. On reading more about the researchers conducted in the past, it was clear that each research has its own pros and cons. This paper reflects on the research conducted to design an algorithm that leads to a cloud based smart algorithm that is secure and is convenient enough to develop a system that can be used to manage the available slots and can notify the users about the available parking slot beforehand to the client. The paper also focuses on the result analysis part that clearly shows that the algorithm designed is more accurate than other algorithms used in the past. We have designed our algorithm using ACO, decision tree, and GPS mapping over cloud. The idea of working on this research was to provide a solution that is cost effective, helps people on large scale and maintains the laws and order.

The smart grid discusses to next generation power grids, with multi-directional streams of electricity and evidence to make aextensive distributed network. Through smart grid, the power system converts smart by communicating, sensing, control and applying intelligence. For superlative system, the smart grid technologies are furthercompanionable to certify many roles which can elevate with the amalgamation of the use of substance generation and transmission. The Smart grid is also kept the environment free from pollution; diminish the cost, effective operations, against all categories of threats and danger. Machine learning process isthe calculations which help in information handling to discover concealed examples or the forecast of results. The target of this archive is to look at the most utilized strategies of machine learning, for example, Vector Machine Backing, Descriptive Discriminant Analysis, Decision Trees what's more, Neural Networks, in Smart Grid applications. To this end, an examination is done in important distributions of the current writing.

Graph theory is a branch of discrete mathematics that deals with the connections among
entities. It has been proven to be a very beneficial and powerful mathematical tool and has a
wide range of applications to handle complex problems in various domains. The aim of this
work is two folds: first, to understand the basic notion of graph theory and second, to
emphasis the significance of graph theory through a real-time application used as a
representational form and characterization of brain connectivity network, as is machine
learning for classifying groups depending on the features extracted from images. This
application uses different techniques including preprocessing, correlations, features or
algorithms. This paper illustrates an automatic tool to perform a standard process using
images of the Magnetic Resonance Imaging (MRI) machine. The process includes preprocessing,
building the graph per subject with different correlations, atlas, relevant feature
extraction according to the literature, and finally providing a set of machine learning
algorithms that can produce analyzable results for physicians or specialists. Further, to
demonstrate the importance of graph theory, this article addresses the most common
applications for graph theory in various fields.

This Paper introduces the implementation of different supervised learning techniques for producing accurate estimates of ground water, including meteorological and remotely sensed data. The models thus developed can be extended to be used by the personal remote sensing systems developed in the Center for Self-Organizing Intelligent Systems (CSOIS). To analyze these data and to extract relevant features, such as essential climate variables (ECV), specific methodologies need to be exploited.. The new algorithm enhances the temporal resolution of high spatial resolution of soil moisture observations with good quality and can benefit multiple soil moisture-based applications and research