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Attendance monitoring is an essential task for any organization and traditional methods such as pen and paper or electronic card systems have their limitations with advancements. The proposed model is a real time face presence monitoring system using Raspberry Pi. The system captures image of individuals using camera and processes the images using deep learning algorithms to detect faces. These faces are then matched against a pre-existing database of known individuals. Door interlock controls add an extra layer of security, ensuring that only authorized person have access to restricted areas. The system provides a user-friendly interface to view everyone's attendance records and generate attendance management reports. The proposed system provides a reliable and accurate solutions for attendance tracking and can be used in various organizations. [ABSTRACT FROM AUTHOR]

A growing field in India that can assist farmers in many ways is the identification of plant diseases. Plant disease reduces the mass production in agriculture. Every time due to heavy loss in agriculture, farmers face many challenges to raise up their needs. This study analyzes various diseases that affect various plants due to the following criteria like environmental condition, types of plant formed from Monocot and Dicot family, crop rotation and Ph value of the soil. The existing system analyzed the diseases based on one of the criteria. Although the strategy is to be followed to identify plant diseases at the early stage with various aspects so that the production in agriculture could be increased. The majority of machine learning algorithms already exist and may be improved upon to yield more accuracy. Xanthomonas wilt, Black and Yellow Sigatoka, Bunchy Top Virus, Black Sigatoka, Fusarium wilt, Cassava Bacterial Blight (CBB), Cassava Brown Steak Disease (CBSD), Cassava Green Mite (CGM), and Cassava Mosaic Disease (CMD), Yellow vein mosaic virus, and Powdery Mildew are a few of the diseases of the plants grown on crop rotation which are the subject of study of this paper. Plant illnesses are classified according to disease in the many open-source datasets that are accessible. For deeper understanding, we have provided a list of references. Here, we've examined the approaches that are now in use for automatically detecting various diseases of plant leaves. We present an approach for accurately identifying early signs of plant diseases from a variety of perspectives. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) and Machine Learning (ML) transforming the Insurance industry by improving efficiency, reducing costs, and providing a better customer experience. As these technologies continue to evolve, more innovation can be expected in the Insurance industry. A Bibliographic analysis is conducted for scientific mapping based on 1,084 SCOPUS-indexed publications between the year 2000-2022 using VOSviewer Application. The Analysis was conducted based on Publications by year, Source, Author, Affiliation, Country, Type, Subject Area and Funding Sponsors. The research found the result, the year 2022 had the highest publication of 203, through documents per year by the source of Lecture Notes In Computer Science Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics at 43 publications, Bauder, R.A. author is the maximum contributor, Harvard Medical School has been the major affiliate, United States of America, has the maximum 341 publications, Conference paper has the majority participation at 412 documents at 38 percent and Keywords "Artificial intelligence", Machine Learning" and "Insurance" has the highest occurrence. The maximum number of publications inthe field of computer science at 28 percent. Overall, this bibliographic analysis provides a comprehensive overview of the current state of research in AI and Machine Learning in the Insurance industry and highlights the potential for further innovation and development in this field. [ABSTRACT FROM AUTHOR]

Floods in Malaysia are described as flash floods and monsoon floods by the Department of Irrigation and Drainage (DID). Forecasting is a specialized form of predictive analysis used to predict future trends or behavior from existing data. Weather forecasting predicts the weather in the future from current data circumstances. As flood forecasting models become more accurate, however, their capacity to accurately predict flooding decreased as the forecast continues. Therefore, to understand the different techniques used to forecast flood levels, a systematic review of the literature was conducted. This paper's main objective is to examine the most common variable used to forecast floods, utilizing the systematic review technique. From the main focus, we can identify the research questions, such as the most commonly used prediction method and its accuracy. In the end, two of the most common variables used for flood forecasting are rainfall and water level. This study's data can help others forecast floods using standard variables that yield the best accuracy possible. [ABSTRACT FROM AUTHOR]

In most nations, lung cancer (LC) is the main cause to mortality cases occurring due to tumors to males and females. Lung nodules (masses) are recognized through digital technologies support precise diagnosis using medical imaging techniques such as CT-scan, X-rays and MRI. As the nodules are normally connected to the blood vessels, identification of lung nodules is a difficult task. Several studies illustrate that diagnosing or detecting such cases earlier is regarded the best successful method to counter this disease. Many systems have been developed that automatically identify lung tumors by using magnetic resonance spectroscopy and pattern recognition methods. Despite the encouraging consistency of the classification, none of these schemes have set their course in clinical practice, which also concerns the lack of clarity concerning the process of taking decisions. In this paper, we present a systematic mapping study on lung tumors recognition. After four sorting methods, we obtained 375 relevant studies in total. After applying four filters manually, 48 papers as primary studies related to the main topic are selected as listed in Appendix (A). The selected papers classified them with respect to several facets. The results provide an overview of the existing relevant studies in reported in the literature, highlight focus areas and research gaps. [ABSTRACT FROM AUTHOR]

There has been a substantial growth of technology and it has evolved a lot, and with the evolving technology credit cards seem to be replacing cash payments. There is no second thought about credit cards getting used on a large scale but this extensive use brings a huge number of fraudulent uses of credit cards. In this paper, discussions on different technologies and methods that are used to detect and prevent credit card fraud have been presented. In the paper discussion on the statistics of the credit card frauds detected using machine learning and deep learning has been presented. In this paper, we have presented the survey on Machine learning techniques and analyzed their accuracy and speed, also we have discussed the methods that are used in detecting credit card fraud using deep learning and artificial intelligence. Furthermore, in this paper, various types of datasets are explained in detail. Also, the paper highlights the challenges and possible solutions to detect fraud. [ABSTRACT FROM AUTHOR]

To increase the safety and security of the house, a smart home safety system is developed, taking into many practical considerations. This home safety kit is formed using the Internet of Things (IoT) which includes face detection and face recognition with Artificial intelligence & Machine learning by training the dataset. The technical methodology is developed and implemented in a Smart Home Safety System Kit, which will work with the essential support of a Cloud-based application. The same data is stored with a local storage system for quick access, and at the same time, data are stored Cloud for critical situations. WIFI transmitter and receiver, and microcontroller (NODE MCU) are used with Arduino, and many drivers are installed to take care of each purpose. The Node MCU is an available open-source with low-cost IOT Platform. It initially consisted of firmware running on Espressif Systems hardware based on the ESP12 module and ESP8266 WiFi SoC. [ABSTRACT FROM AUTHOR]

In this world of ever-increasing texts, chats and literature users nowadays are posting countless comments on various social media platforms, news websites, and discussion forums. While some comments bring the positive side of the matter, whileother remarks are detrimental or combative in nature. Manually monitoring of such a large volume of comments and chats is a tedious and a time taking task. Therefore, various machine learning based techniques are employed to cater the task of detecting the levels of toxicity in the comments. We gathered information from 20 primary research papers that were found to be relevant in the field of toxic comment classification. We explored all the papers thoroughly taking into consideration the publishing date andconferences in which they were published. We noted the data set used, performance criteria, machine learning techniques employed and other important features such as diversity of toxicity classes and comparative report stated in them. We concluded our work with a detailed overview of the existing issues and a suggestion list of future topics that could be explored and implemented to make the system more accurate in classifying the toxic comments. [ABSTRACT FROM AUTHOR]

The easy accessibility of social media to everyone generates serious problems. Misleading news affect the mental health of peoples. False news can easily be created and propagated using online platform by using an Un-anonymous account. it is required to control the spread of fake news on social media. In this paper we used several linguistic features for fake news classification along with machine learning model. The linguistic features used are number of characters, number of words, noun-count, and number of articles. This study provides the heuristic solution by using both the news text and Linguistic features of text for better news classification. LIAR dataset is used for experiments. Our method outperforms the existing method. [ABSTRACT FROM AUTHOR]

Sentiment analysis is a process of judging people thinking, imaginations, thoughts, personality through their textual words, emotions, different types of pictures including emoji's, paintings and behavior etc. Sentiment analysis is widely spread in multiple fields and the deployment of the approach for judging the sentiments of educational sector stakeholders is barely a researched field. Teacher and students are the backbone of our educational system who works behind the improvement of the educational infrastructure and development of any country. Their sentiments while teaching and learning both affect the future of our new generation. In this review paper, we highlight different methods, approaches and tools related to sentiment analysis. Classification tools include Machine learning and lexicon analysis, whereas Statistical tools include ANOVA test, T-test etc. [ABSTRACT FROM AUTHOR]

Diabetes is one of the leading global health emergencies of the 21st century. The threatening side effects proves it to be a dangerous epidemic. This paper proposes a model to solve the problem in the existing system by comparing various data mining techniques in order to cluster and classify them. The paper studies a huge sample of diabetic patients common to diabetes and cardiovascular disorders. The method adopted is to cluster the data into three prior categories of high, medium and low clusters. Once clustered, optimum algorithmic technique is implemented to classify the data into mild and severe fragments in order to determine the chances of occurrence of a heart disorder due to the impact of diabetes. The aim is to implement an algorithm that can classify the data collected in order to infer whether or not a diabetic patient is prone to a heart disorder or not. [ABSTRACT FROM AUTHOR]

"Online learning is not the next big thing; it is the new big thing." "eLearning is changing. And we will see new models, new technologies, and designs emerge. So, let us drop the "e" – or at least give it a new and wider definition." These quotes influenced us to build this paper. During this unprecedented time, we all are using online resources. Our paper will enhance the teaching experience of the students. Especially school-going kids, it is the duty of teacher, influencer, and faculty to enhance the teaching experience so that, one can learn at their ease without any hustle. Our paper aims at building a website and android application (in the coming days) which will identify the individual words of any sentences entered. It will parse the string entered, remove the unwanted characters, and pass the remaining string to our AI model. As a result, the AI model will return the parts of speech the individual word belongs to. We have always seen students struggling to find that a given word is either a noun, or an adverb, or a preposition, and so on. We have searched the web but could not find any relevant websites doing the same task. There is a gigantic search engine on which we can search individual words but again, for sentences, people struggle. This may sound very simple for AI/ML enthusiasts but building a model and deploying that on a website and android application is not a cup of tea. Tech giants like Google, Facebook, and Microsoft are building different AI models and deploying them successfully on various platforms like Website, Android Application, iOS. However, we have done it successfully. [ABSTRACT FROM AUTHOR]

Recommendation systems (RS) suggest the right item to the right user. It predicts the user's rating to an item and based on this rating RS provides the suggestion to users. In today's world many online applications are already using the Recommendation system that provides a recommendation for a particular item like books, movies, music etc. in an automated fashion. This paper proposed a system that helps to find the best suitable Medicineaccording to diseasesby using library "recommenderlab" in R. This study proposed a system that gives the best medicine available according to the user rating available in database. User makes their decision according to their recommendation provides by the proposed system for finding best suitable medicine from available database [ABSTRACT FROM AUTHOR]

Recent times artificial intelligence, machine learning and deep learning methodologies are utilized successfully in various fields of engineering, especially for health monitoring of civil engineering structures. Offshore platforms are exposed to various environmental, and machinery loads which excite multi-vibration and it produces big data. In order to analyze this huge data appropriately for health monitoring of offshore structures, the application of deep learning techniques is growing. This paper highlights review of studies reported on use of deep learning techniques in the health monitoring of offshore platforms which are excited by vibrations. [ABSTRACT FROM AUTHOR]

This paper proposes quantum machine learning (QML) to assist fast demapping of low order modulations of future wireless communications beyond the fifth generation (5G), where the 5G new radio (NR) demappers are used as examples since standards for 5G-Advanced and the sixth generation (6G) are still unavailable. We demonstrate that QML circuit can be constructed to solve the problem of demapping involving many points, which is in general difficult and intractable, especially when the number of modulation constellation points are significantly large. In this paper, we use an amplitude encoding technique to map the received signal constellations, followed by data processing prior to the measurement, for further processing. The proposed QML circuit is confirmed to be able to demap successfully the signals of 5G NR, i.e., complex binary phase shift keying (C-BPSK) modulations with the training data of only the four complex symbols. These results are expected to stimulate other variational quantum algorithms (VQA) for higher order modulations of 5G-Advanced or 6G communications. [ABSTRACT FROM AUTHOR]

From the past few years spreading of the pests and diseases in plants have been increasing significantly. Paddy plants are the most important crop used in our country for the food, so it is most necessary to detect any disease of them within a short period of time for ensuring a proper and healthy growth of paddy plants. The process of manual disease detection requires labor and a large amount of time. Then utilizing the leaf images of plants for recognizing and classifying of diseases is the more focused research topic in the agriculture field. A survey on recognition and classification of paddy leaf diseases using image processing and machine learning techniques is presented in this paper based on the disease infected leaf images of paddy plants. Firstly the concept of various plant diseases and the standard process of plant disease detection are discussed in this paper. A study and survey on the totally 5 papers of work is carried out in detail by covering a work on leaf diseases of the paddy plants based on the certain criteria. Such criteria are different preprocessing techniques, various diseases/ classes, different segmentation methods, various classifiers and accuracy of employed techniques. The experimental results of these various techniques are compared and evaluated to design a best on detecting and classifying of paddy plant leaf diseases. [ABSTRACT FROM AUTHOR]

Many types of machines widely used in Industrial application process. The main objective is to recognize faults failure based on motor speed and vibration signal. To find fault machine with the use of vibration analysis in turning equipment and induction motor. Every defect can associated with cost and performance of the machine. Due to these associated risk such as cost, time, efficiency early detection, isolation and failure prediction is important. Using machine learning algorithm to predict the fault failure rate of machine easily. Signal level are monitored with vibration analysis. Depends upon the vibration signal faults are identified and classified. In this paper we discuss to classify the bearing the fault using different data mining techniques. To predict the imbalance condition, fault in bearing, and broken rotor bars faults are predicted using machine learning algorithm. [ABSTRACT FROM AUTHOR]

Most real-world classifications problem provides an uneven, skewed dataset. Imbalanced datasets are a key issue in machine learning. The challenge for any machine learning algorithm to learn from imbalanced dataset is correctly classifying cases from the minority class. Imbalanced dataset leads machine learning algorithms to misclassify cases from the minority class which is undesirable for real world classification applications and also degrades the performance of algorithm. Glass identification dataset is one of the datasets that serve as a example of skewed imbalance real world dataset. The majority of research on glass identificationtasks ignores the data outlier and class imbalance found in this dataset. Class imbalance, data outlier, and insignificant featuresare only a few of the factors that contribute to the poor performance of distance-based machine learning methods such as kNN. Thus, this paper explores the performance of one of the most simple and efficient machine learning classifier kNN. The authors performed interquartile range (IQR) thresholding to eliminate data outliers, ReliefF ranking to find significant features and SMOTE to cope with the class imbalance occurring in the dataset. The Inverse distance weighted kNN is utilized for classification in the improved dataset. The results revealed that using the strategies mentioned improves the performance of the kNN algorithm while also providing accuracy that is better and more dependable than much of the research done in this dataset. In this paper the authorsfound the highest accuracy of glass identification task is 78.923% with F-measure 0.791. [ABSTRACT FROM AUTHOR]

Modern Operating Systems have evolved with many enhanced features and optimizations. Although updates and features are constantly added to the systems to make our lives easier, the file management system remains behind the times. This project aims to make a File Management system to address modern-day user needs and make traditional file managers more adaptable to present times. We aim to develop this tool to incorporate contemporary requirements and perform functions unseen in conventional file managers. The project focuses on three main attributes: file and folder management, storage use reduction, and better accessibility for a user-friendly experience. In file management, some mundane tasks or processes require occasional attention and manual maintenance by the user. The idea behind this Intelligent File Manager isto reduce manual effort and make file management quick and simple with just a click. In terms of reduction in storage usage, insufficient storage issues arise over time. This forces users to browse through hundreds or thousands of files, folders, or images, only to realize that most of them are the same or way too similar, then delete them manually. Our Intelligent File System removes all these manual tasks and achieves storage optimization through automated detection and deletion of duplicate files. We strive to make the existing file system more simple, intuitive, and intelligent with better enhancements and features than its predecessors. We also intend to provide the user with a better experience that involves minimal to almost no manual work when working with file systems, with the help of Machine Learning and Automation Script [ABSTRACT FROM AUTHOR]

The Optimization of cost in any product is plays the important role. The Gradient descent method is used to solve the problem of local minimization. This is an Optimizing algorithm that also used for minimize the cost function in various machine learning. Basically we discussed the types of Gradient descent algorithm like Batch gradient descant, Stochastic gradient descant and Mini batch descant. This paper will be discussed on the different parts of Gradient descant and stochastic gradient descant algorithm. This paper is the comparative study of both algorithms. [ABSTRACT FROM AUTHOR]

Researchers use many classification algorithms with image filers to work on image classification. The goal of this study is to see how well machine learning classifiers perform with different WEKA image filters on image dataset. In this paper we present the comparison of different ML classifiers with image filters using Waikato Environment for Knowledge Analysis (WEKA). WEKA is open source data mining software that comprises of a collection of machine learning algorithms. This paper, we introduces four important data mining techniques K-NN,J48, Naive Bayes and Random forest classifiers for image classification with image filters on the birds image dataset using WEKA tool. In this paper, we compare these classifiers with image filers over different parameters and selected optimal classification algorithm. As per our results Random forest classifier working well on our small image dataset and we got 90% accuracy with image filters. To improve the performance of the classifiers we used ensemble technique. Then after we got 96% accuracy with ensemble image filters on this resultant image dataset. In this paper we concluded that ensemble technique will improve the performance of the classifiers with image filters. [ABSTRACT FROM AUTHOR]

"The best ever non– alcoholic drink which is consumed in India after water is tea ". India has privileged to stand in thetop listing World in tea production. In India, tea plantation contributes tremendously in the economic development. Nowadays,machine learning is playing significant role in the field of agriculture. With the help of machine learning in tea crop, which results in productivity enhancement and better classification of tea leaves, weather prediction, smart farming, crop disease detection, and to analyze the various effects of soil in future prediction. This paper aims at providing detailed information on various machine learning approaches proposed in the past five years by emphasizing the various techniques used in artificial intelligence in tea crop yield in various states of India and a broad where tea production has contributed in national income. [ABSTRACT FROM AUTHOR]

"Manache-Shlok" (MS) is the set of conventions to follow by every individual in daily life. MS is thoughtfully written by Shree Samarth Ramdas Swamiji, who was the Guru of Shree Shivaji Maharaj of Maharastra. Shri. Ramdas Swamiji was a devotee of Shree Ram and Hanuman, a great poet and saint of Maharashtra. MS shows way of life even today, even after decades of inception. MS is written in Marathi language and is regularly chanted by many for various goals to achieve. By reading the Marathi and English versions of MS, it is clear that these MS are thoughtfully written to cater the needs based on state of mind, importance of exercise, mutual relationship at home or work, believe in God / The lord Rama, or believe in strength of universe etc. In this research paper, we would like to find out the possible hidden patterns among MS, by using Machine Learning and Text Mining concepts. After applying text mining techniques, the various classes of MS are formed, wherein the members of these classes are Shlokas having similar meanings. These classes can be mapped to various personality traits given by psychology. This paper is an attempt to inform readers that chanting all 205 MS is the best option, but it is also beneficial to chant specific Shloka for achieving specific goal or need according to the individual's psychological traits, or even for the improvement of self. [ABSTRACT FROM AUTHOR]

The spread of the Corona Virus epidemic (COVID-19)arouses the human normal life and seen a million of death around the world. Researchers are designing machine learning models to predict and judge the spread of COVID-19 epidemic. The significance of this paper is to predict the number of people who will be infected with COVID19 using machine learning algorithms. In this context, four models such as Autoregressive Integrated Moving Average (ARIMA), Support Vector Machines (SVM), Bayesian Ridge Regression(BRR), and Polynomial Regression (PR) have been employed to anticipate COVID-19 prediction in this paper. All models predict newly infected case's location wise including countries, states, cities etc. for the next 30 days. Furthermore, all models have applied on the time series dataset taken from WHO's official website and build a comparative study on five evaluation metrics such as RMSE, MAE, MSE, MAPE, and R2. The feature selection has been performed on the raw datasets via. Time series properties and demographic attributes. The results showed SVM outperformed the other models, while in the available dataset the Polynomial regression performs poorly in all the predictions. This model also scales on different geography, country and city. [ABSTRACT FROM AUTHOR]

There has been an alarmingly high mortality rate because of cardiovascular diseases since the past few years and some innovative research has been done by scientists and research scholars all over the world to make the pre-emptive detection of these diseases efficient, cheap and accessible to the public. In this paper, we have studied the research and experimentation that has been done on detection and classification of Cardiovascular diseases using Phonocardiogram signals. There are many problems that come with the cheap and democratized process of heart monitoring using Phonocardiogram signals, like environment noise, lung sounds etc. sever a innovative classification and transformation methods that solved these problems and yielded promising results were found in literature. This paper aims at providing a detailed view on such techniques and optimal classification models which yield comparable results and are computationally cheaper. [ABSTRACT FROM AUTHOR]

Quantum machine learning is a new field of study that aims to apply the advantages of quantum computing, such as superposition, which is one of the intriguing properties that could speed up computation, to machine learning. Like classical machine learning models, quantum machine learning models can learn from specific input data and perform specific prediction tasks. However, quantum machine learning models are currently in the form of quantum circuits with parameterizable gates. In this paper, the implementation of a quantum classification model is investigated using the two most well-known quantum machine learning development tools: Cirq with TensorFlow Quantum (TFQ) and Qiskit with PyTorch. The quantum classification circuit is trained to recognize images of handwritten digits using the MNIST dataset. However, because the quantum computer is still in development and is difficult to access and use, quantum simulators are the best alternative for the time being. The performance of the quantum classification model implemented by the two development tools is then compared in this paper experimentally using quantum simulators on two different hardware configurations. In addition, the experimental setups differ in the number of input qubits used to store the image's classical pixel values. Finally, the wall time and CPU time used to run the training process, as well as classification accuracy, are examined. In terms of accuracy, Qiskit with PyTorch outperforms Cirq with TFQ only in the lower number of qubits but the results are similar in the higher number of qubits. The experiment results in terms of run-time are highly different. From the result, we find that Cirq is noticeably faster than Qiskit in every experiment by at least 90 times. [ABSTRACT FROM AUTHOR]

To predict the accuracy percentage of Short Message Services (SMS) spam detection using machine learning classifiers. Two ensemble learning algorithms named random forest algorithm and naive bayes are applied to data. The algorithms have been implemented and tested over a dataset which consists of 5574 records. Ensemble learning methods combined several models trained with a given learning algorithm to improve accuracy. After performing the experiment as result shows mean accuracy of 89.75 % by using random forest and compared naive bayes algorithm mean accuracy is 88.05% for SMS spam detection. There is a statistically significant difference in accuracy for two algorithms is p<0.05 by performing independent samples t-tests. This paper is intended to implement Innovative Classification for prediction of SMS spam detection. The comparison results shows that the naive bayes algorithm has appeared to be better performance than Random Forest Algorithm. [ABSTRACT FROM AUTHOR]

Stochastic optimization in machine learning adjusts hyperparameters to reduce cost, which shows difference between actual value of estimated parameter and things predicted by machine learning model. Learning rate regulates the amount of alteration to a model in terms of the predicted error. Tuning learning rate is a challenging task. A too large learning rate affects the performance of a model while a too small learning rate may never converge at optimal or near-optimal solution. Adaptive learning rate adjusts learning rates based on performance of a model. This paper presents a survey on first-order stochastic optimization algorithms, which are the main choice for machine learning due to their speed across large datasets and simplicity. Stochastic gradient descent method, its variants and mini-batch algorithms will be elaborated. Adaptive learning rate embedded in the stochastic optimization algorithm can be further improved. This paper discusses learning rate adaptation schemes and how these affect stabilization in the value of learning rate which helps stochastic gradient descent to show fast convergence and a high success rate. This paper aims to offer useful insights towards the development of future stochastic optimization algorithms in machine learning. [ABSTRACT FROM AUTHOR]

According to WHO, a tremendous supply of worldwide deaths are Cardiovascular Diseases (CVD). It is envisioned to take 18 million lives every year. CVDs are a collection of coronary heart problems that encompass however aren't constrained to, coronary heart disease, cerebrovascular disease, and rheumatic coronary heart illness. Approximately 80% of deaths related to cardiovascular disease (CVD) are attributed toeither coronary heart attacks or strokes. Out of those, one-1/3 of those deaths arise in human beings below theage of 70. Researchers have been looking to broaden powerful approaches to are expecting coronary heart sicknesses for a long term however the accuracy of such techniques has continually been less than 89 percent. With the expanded quantity of records that the healthcare enterprise has procured to date, it's miles pretty desired to use Machine Learning strategies to are expecting coronary heart ailments in an effort to be very powerful in helping docs to decide or are expecting if a man or woman is at an expanded threat of coronary heart illnesses. Here, we've labored on a Cardiovascular Disease prediction version that could are expecting coronary heart sicknesses with an accuracy of 99.70%. This is a prediction with excessive reliability. It permitsus to accept as true with our computer systems to decide whether or not one can be laid low with any form of coronary heart disease. Such dedication can assist us take early precautions to reduce, if now no longer eliminate, the contamination. In this paper, we've labored on a dataset and have wiped clean it very well to make certain we attain the very best stage of precision. Following that, we employed classification algorithms,namely KNN and RF Classifier, to train and evaluate the model. They proved to be pretty powerful in figuringout the characteristic factors and indicates an accuracy of 86.43% and 99.70% respectively. The intention of this paper is to discover if a man or woman is at an expanded threat of coronary heart ailments, to assist in early remedy and mitigation of the contamination and thus, reduce the worldwide loss of life toll through a massive margin. [ABSTRACT FROM AUTHOR]

The aim of this paper is mining serendipitous drug usage to validate and generate drug repositioning hypotheses from social media. Materials and Methods: Two machine learning algorithms svm with sample size=12 and adaboost algorithm with sample size=12. Results: The support vector machine algorithm has shown more accuracy of (96. 66%) in reducing the false positive rates when compared with Adaboost algorithm accuracy(84.6%). The pre-test was calculated with a g-power value = 80% and threshold 0. 05% confidence interval of 95% mean and standard deviation by using the G-power tool. t is found that the svm algorithm has more accuracy percentage when compared with the Adaboost algorithm. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to improve the accuracy for automatic classification of meal waste from innovative trash data with the help of image processing. There are 2572 images for the classification of meal waste were used for this paper. The images are labeled as "Cardboard", "Plastic", "Paper", "Metal", "Glass", "Trash" and there are 20 number of images have been used for RF classifier taken as first set of machine learning algorithm and is compared with SVM algorithm taken as second set of machine learning algorithm With a g-power value of 80%, the revolutionary garbage data images, a threshold of 0.05%, a confidence interval of 95%, and a standard deviation, these photographs were gathered from various web sources. When compared to the SVM method, which had an accuracy of 61.45%, the proposed system's accuracy was enhanced to 84.81%, with a significant value of 0.001 (p 0.05) with a 95% confidence interval. This study found the meal waste from trash using the RF is significantly better than SVM algorithm. [ABSTRACT FROM AUTHOR]

Online transactions and shopping make the life of urban cities more comfortable. But this system is not full proof there are various flaws due to which hacker can steal all the hard-earned money is very less amount of time. The need for earlier detection of online frauds is increasing day by day. Online fraud refers to frauds when product bought online is of cheap quality or the money from one account is robbed. In this paper, we are going to discuss various types of online-based frauds and their detection using machine learning. Accuracy is also an important factor while discussing online frauds in this paper we are also going to discuss various methods used to calculate the accuracy. [ABSTRACT FROM AUTHOR]

The main idea of this research paper is to avoid food wastage by making an ordinary refrigerator smart cost-effectively instead of designing a brand new refrigerator which is a very costly affair. This paper deals with the complete design of an intelligent module based on Machine Learning (ML) and Internet of Things (IoT) which can be fitted to any refrigerator with enough features thereby making it smart and modular. The module comprises an adjustable frame containing the processing unit at the top side, a display screen in front, and a weight measurement tray with camera sensing attached on one side of the refrigerator respectively. This design helps to remove the mess of wires inside the fridge which is a common issue with other smart refrigerators as evident from the literature review. At the end, results of system testing using ImageNet dataset are shown where an accuracy of 85% is achieved in item classification. [ABSTRACT FROM AUTHOR]

In software engineering, it is now necessary to divide needs into functional and non-functional categories to ensure proper operation and overall performance. This study aims to distinguish between functional (F) and non-functional (NF) requirements for application software and classify them accordingly. A portion of the data has been obtained from online resources. In this study, data is cleaned using normalization steps and then proceeded with subsequent steps such as text preprocessing and vectorization. Bag of Words, Term Frequency-Inverse Document, Featurization and Machine Learning Models, ROC and AUC curves, Bi-Grams and n-Grams in Python, Word2Vec, and confusion matrix were covered. Early detection of NFRs allows us to make initial design decisions. Machine Learning algorithms were applied to identify and classify functional and non-functional requirements for application software development based on user requirements. This paper aims to find out which ML algorithm and which model of NLP gives better accuracy and best performance for binary classification. In this study, the combination of BoW and Multinomial Naive Bayes provides the best performance for binary classification. This paper classifies functional Requirement (FR) and non-functional requirements (NFRs) and assists the software developer, software designer, testers, etc., in developing applications. This paper is also helpful for faster software development and delivery. This step facilitates the creation of an SRS document and avoids unnecessary work and complexity during the requirement analysis phase. [ABSTRACT FROM AUTHOR]

Pulmonary carcinoma affects both men and women caused by the uncontrollable growth of cells. The majority of pulmonary carcinoma is discovered at an advanced stage. Pulmonary carcinoma is divided into 2 types: „small cell pulmonary carcinoma (SCPC)' and „non-small cell pulmonary carcinoma (NSCPC)'. Based on routine clinical data, machine learning (ML) is used to detect the upcoming diagnosis of pulmonary carcinoma. In this paper, we discuss image preprocessing, image enhancement for preprocessing techniques, and a few feature extraction and segmentation concepts and classification techniques. It implements different image processing and ML techniques, and by comparing different classifiers, this will aid in increasing the accuracy of the pulmonary carcinoma prediction system through segmentation and classification. [ABSTRACT FROM AUTHOR]

The scope of the paper is to leverage the power of Machine Learning and Deep Learning to detect diseases in crops and to provide solutions for the diseases detected. Farmers usually detect diseases in the crops using their naked eyes, eventually leading them to make wrong predictions regarding diseases in the crops. They might use the wrong fertilizers or more than the usual dose, which could damage the plants, soil, and fields. Thus, this paper focuses on providing solutions for detecting diseases in crops and solutions for the diseases identified. Convolutional Neural Networks are used to identify various diseases in crops since it limits the number of parameters and thereby make the model more efficient. The said model is implemented by making a cross-platform code using Flutter with Dart as the programming language. [ABSTRACT FROM AUTHOR]

Object Detection has made fathomless developments in terms of deep learning with varied applications. Detection of fruits is one of the commonly used application which still has some major leaks in it. In this paper, we discuss an approach to detect the fruits on trees. The problem has a broad room for development and along with a proposed system this paper is inclined towards review for discussing all the state-of-the-art object detection algorithms in deep learning that can be used. The paper discusses ideology and approach for the fruit detection in a very concise manner. [ABSTRACT FROM AUTHOR]

Heart disease is a major killer in the world. Nature-inspired algorithms have been used as optimization techniques to predict the disease. The prediction of the disease can be further enhanced by using classification models of machine learning algorithms. The accuracy and other aspects of prediction varies with classification models. This paper presents the research conducted by combining nature-inspired techniques with machine learning algorithms to make intelligent decisions for prediction of heart diseases. As part of the research conducted, the prediction of the heart disease has been assessed by the combination of nature-inspired techniques and machine learning algorithms. This paper presents the outcome of the research conducted with four nature-inspired algorithms, namely Ant, Bat, Bee and Genetic algorithms and two machine learning classification models, Random Forest and SVM to predict the heart disease. Feature variables are extracted from heart data obtained from the UCI Machine Learning Repository. The output of the optimization techniques is used to train models based on the two classification algorithms. The measures of prediction were compared. [ABSTRACT FROM AUTHOR]

Drowsiness and Fatigue of drivers square measure among the numerous reasons for street mishaps. Street crashes square measure the chief normal styles of mishaps and passings around the world, and furthermore the significant explanations behind these mishaps square for the most part measure regularly intoxicated, laziness and crazy conduct of the main impetus once per year, they increment the measures of passings and fatalities wounds worldwide. in accordance with the globe Health Organization, street traffic wounds have up to 1.25 billion across the globe, that makes driver languor identification an intense expected space to deflect different rest prompted street mishaps during this paper, a module for Advanced Driver help System (ADAS) is presented to downsize the amount of mishaps because of drivers exhaustion and thusly increment the transportation wellbeing. This technique significantly manages the programmed driver tiredness identification upheld visual information and Machine learning. The model assesses each edge of the video, that assists with recognizing the condition of the main impetus. we tend to blessing each emotional and quantitative results to approve the cases made inside the paper. Communication is the best way of expressing your thoughts, ideas, and feelings. Among all the types of communication technologies, a audio, or a speech, it is the preferred, advanced, and professional means of communication between people. In the era of the Internet of Things is growing, and evolving, with more and more intelligent systems that will be available for day to day use. These types of applications, ranging from simple handheld devices, the structures of a complex of autonomous vehicles and automated systems that are used in various fields. [ABSTRACT FROM AUTHOR]

The SVM is a widely known machine learning, which is very useful for regression applications and pattern classification. These machines have been used successfully in several domains to address numerous real-world challenges. In this context, parameter optimisation for an SVM is a widely researched topic, which has attracted attention from several research domains. Algorithms facilitating optimisation have been of greater interest compared to other algorithms. Algorithmic approaches allow the optimal parameters for an SVM to be determined, after which the model can be adapted for several other applications. During the last two decades, several enhancements have been brought about to facilitate better optimisation of SVM models to offer enhanced performance. This paper focuses on the several algorithms currently employed to optimise support vector machines in their basic and modified forms. This paper comprises a comprehensive analysis of algorithms and aims to ascertain the present challenges relating to algorithms used for SVM parameter optimisation. This study cannot evaluate all the details; however, the significant theoretical aspects are covered using references to existing literature. [ABSTRACT FROM AUTHOR]

This paper presents the systematic literature review of the application machine learning method in detecting a distributed of service (DDoS) attack. Several relevant research papers were selected and they were reviewed based on the method using to provide the best performances and evidence in machine learning technique applications. The researchers are dedicating their efforts to analyzing, summarizing, and evaluating various machine learning methods for detecting DDoS attacks. Therefore, the purpose of this study is to evaluate several machine learning approaches for detecting DDoS attacks in computer networks. These mechanisms are characterized into five categories, the Multiple Linear Regression method, Deep Neural Network (DNN) and Long Short-Term Memory (LSTM) method, Recurrent Neural Network (RNN) with Autoencoder, Deep learning-based method, and LSTM with Singular Value Decomposition (SVD). The paper also debates several open research questions and the research technique, parameters, and metrics. Also reviewed and contrasted were summaries of analyses and gaps in deploying a predictable machine learning model. Thus, the paper is expected to benefit academicians and researchers in developing an efficient solution for the machine learning mentioned above in detecting DDoS attacks. [ABSTRACT FROM AUTHOR]

The time people spend online has been increasing dramatically recently, and people can become anonymous when posting, share their own opinion and participate in online chat. Because of this, more and more people are sexually harassed online on various social media, especially with children. This paper aims to detect sexual harassment at an early phase using three types of machine learning (SVM, Logistic regression, XGBoost) with a bag of words to represent the text. This paper deal with two kinds of data sets Chats sexual predators (CSP) and comments sexual harassment (CSH). In the logistic regression for the datasets (CSH, CSP), the accuracy obtained was 98.44%, 93.71%, while the accuracy of XGBoost was 96.57%, 90.65%, respectively. XGBoost use to avoiding overfitting and shows promising results in both data sets. [ABSTRACT FROM AUTHOR]

The paper proposes energy prediction techniques on Electric energy consumption from existing electricity supply using machine learning. Machine learning and its techniques are vastly used in factories for maintenance, in industries for optimization and to calculate the fault tolerance to produce an efficient monitoring system. This paper proposes the best model to monitor the usage of power for predicting the energy Raspberry Pi 3Model B+ is the core controller used for this energy prediction and modelling. AC-current, AC-voltage and power are the input parameters measured using current sensor (ACS712) and AC-Voltage sensor (ZMPT101B). MCP3008 ADC is interfaced with the controller to read analogue current and voltage values. Linear regression and logistic regression are used for both prediction and optimization. The two algorithms are implemented on the Spyder IDE which is installed in the Raspberry pi 3Model B+ controller. After reading the input parameter it predicts the energy consumption and displays whether it is high or low for that day. Experimental Results suggests that the linear regression model is the best among the two because the accuracy metric values viz., Mean Absolute Error, R2 score, Execution time, Residual Sum of Square are comparatively less when compared with logistic regression model. [ABSTRACT FROM AUTHOR]

In this paper the focus is to study the use of artificial intelligence and machine learning in acquisition of knowledge for students. industry which can be employed to improve the teaching learning process and make it more flexible for all students resulting in the mapping with the future skills required by the students. The education is undergoing a fast change keeping in view the global trends the new teaching and learning approaches have being examined in a variety of settings. The paper explains AI and machine learning as the future of education in both developed and developing countries. The paper focuses on artificial intelligence and machine learning in future of education. The finding indicates that AI and machine instruction help in instructional method, examination, evaluation and training to staff in education sector although it is still having some challenges in the education system. The study suggests the research to be done about the effectiveness of AI and machine systems in student's evaluation and examination. [ABSTRACT FROM AUTHOR]

This paper mostly focuses on real-time detection and classification of biomedical waste objects. Traditional methods for detecting the objects like biomedical waste objects in machine learning are substituted by recent technology of object detection methods in deep learning by building Convolutional Neural Network (CNN) which is nothing but the components of deep learning. This paper proposes a deep learning approach for object detection of biomedical waste material by evaluating the TensorFlow object detection API, which can used for real time application, this biomedical object detection API further we can used for automate the biomedical waste segregation system, this proposed system has been trained using more than 500 images for each type of biomedical waste object. Tensorflow is useful and easy to object detection that can form powerful image recognition software, in this proposed work we are using Faster R-CNN algorithm for developing this object detection process. [ABSTRACT FROM AUTHOR]

In India, climate change has a huge impact on the agricultural sector. It directly affects crop yield, nutritional quality and productivity. In this paper, a model is devised to correctly predict the crop yield only from past data including factors like rainfall so that appropriate measures can be taken in the future. Machine Learning is one such tool which can be used to solve this issue. Random Forest Algorithm, XGBoost and K-Nearest Neighbor (KNN) Algorithm are the machine learning algorithms used for crop yield prediction in this paper and the one with higher accuracy can be used for future work. Further discussion includes how much impact each factor related to crop yield has on the predicted value so an ordinary farmer can plant and take care of his crops according to it and use this model for his daily work. In doing this, modernization of the agricultural sector is made possible and a boost is given to crop yield in India. [ABSTRACT FROM AUTHOR]

This paper presents a novel idea of implementing & creating a model to determine the fake news services Vs the real news. This is done by implementing the latest Machine Learning algorithms to create models. The model that is being created deploys the latest data sets that have been trained and manipulated and detects, classifies and to recognise and to provide the information that has been collected across various channels to be real or fake news. Data Validation is an ever-growing field, which with the emerging era of data science has increased its manifold tenfold. Thus are the implications of detection and classification of the data. Whomsoever it may be, whether companies, businesses or authorities from the government, it is essential to formulate an application to detect and prevent Fake News spread or misinformation from reaching the mass. This paper implements the latest growing pace of Machine Learning to implement the same. [ABSTRACT FROM AUTHOR]

In this paper a machine learning based solution for detection of public transport the visually impaired people have been proposed. The proposed solution helps visually challenged people to detect cabs and buses. One of the major challenges faced by individuals with complete loss of vision is to navigate around places. Lack of awareness about the public transport facilities available for them in their vicinity is a challenge for the visually impaired. Existing assistive aids are not directed towards solving this problem. The solution proposed in this paper employs five machine learning classifiers namely Decision tree, Random Forest, SVM, Gaussian Naïve Bayes and KNN which are trained on a custom dataset. SIFT is employed for feature detection and PCA is used for dimensionality reduction. The trained model precisely detects cabs and buses, and then provides audio and haptic feedback to the user. The distinct points to note about the solution are the low power requirements and reduced latency both of which are essential for its purpose. In this approach maximum accuracy achieved is 98.1% with Random Forest classifier model. [ABSTRACT FROM AUTHOR]

Now a day there is exponential growth in music files, so the need for automatic music genre classification approach is also increasing. Current classification systems of music genres developed using machine learning algorithms and some of the limitations of existing music genre systems are doesn't work with large database, low accuracy etc. In this paper, presented a large music dataset namely GTZAN with 10 distinct music genres like jazz, rock, hip-hop etc. In order train the classification model of music genre system, a deep learning based technique called CNN (Convolution Neural network) is applied. The proposed method performs the classification of music genre into innumerable genre with the help of feature vector extraction, which plays a vital role in training the model. The feature vector called MFCC (Mel Frequency Cepstral Coefficient) considered in classifying the raw files which are converted to respective music genres or types. The performance of various classification techniques are analyzed compared with respect to evaluation metrics. [ABSTRACT FROM AUTHOR]

This paper reviews the application of big data in smart manufacturing. Currently, the application of big data into the manufacturing operation is still a premature undertaking, with cost, compatibility, and lack of expertise hindering decision-making for many organizations. The selected papers discuss a variety of situations in which big data analytics and its applications have been used to enhance decision-making in the manufacturing operations, productions, scheduling, quality assurance, maintenance, and sustainability with significant data analytics considerations, obstacles to extensive data analytics adoption, machine learning, and use of sensors for data extraction. This article presents the discussions in the manufacturing industry surrounding the use of big data analytics. [ABSTRACT FROM AUTHOR]