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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]

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]

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]

"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]

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]

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]

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 analyzed the feature selection for reducing the number of input variables when developing a predictive model. Boruta Algorithm is using in this paper as a wrapper around a Random Forest classification algorithm. Boruta algorithm is one of the algorithms used to determine the significant variables (feature selection) in a classification model in the machine learning approach, as supervised learning. Our results show that on the German Credit Data from the UCI Machine Learning with 20 variables, feature selection using Boruta Algorithm with Python Programming obtains 4 significant features. [ABSTRACT FROM AUTHOR]

In the present era of Deep learning algorithms whose impact is at a greater extent in identifying and classifying the electronic medical images based on Content Based Image Retrieval (CBIR) systems where a user queries an histopathological images based on the visual content which leads to non accurate results. In this paper after reviewing all the references we propose an image annotation approach which automatically assigns keywords to images based on the visual content by which querying task will be easier one as we classify the images based on supervised learning aspects to perform mapping between features and concepts or class labels called as Bag of Words (BoW). In this paper we compared the remarkable researches from 2006 to till date publications and compared them in terms of methodology adapted. [ABSTRACT FROM AUTHOR]

In the present era heart disease is considered to be one of the major diseases and many people are suffering due to this disease and the foremost challenge is identification and prediction before it causes any consequences or deaths. There are some techniques available for prognosticate heart disease since this disease is increasing rapidly throughout the universe, this prediction process may save life. Time and efficient play important role in identifying heart disease in healthcare industry particularly in the field of cardiology. In this paper we developed a dynamic and accurate system for heart disease prediction using machine learning techniques. There are two phases which can identify and predict heart disease: 1) Feature selection 2) classification stage. Feature selection is one of the methods for selecting attributes and feature subset as it eliminates unwanted data and apply classification algorithms and dataset comprises of patient's attributes like age, gender, blood pressure, glucose level, blood sugar etc... by processing these attributes we can predict the chance of occurring heart disease. This paper proposes a optimization techniques like Grey wolf optimization, Particle swarm optimization combined with Ant colony optimization for performing supervised classification algorithms.PSO is for finding optimum solutions and ACO is for finding good paths and the mixed proposed algorithm is applied and result are estimated to identify the efficiency and robustness. [ABSTRACT FROM AUTHOR]

Recognition of a text is a technique that can convert handwritten text into the machine processable format. Document Analysis and Recognition (DAR) plays a significant role in the automatic extraction of information, which is presented on a piece of paper. The output of DAR is such that computers can quickly process it. Nowadays, many researchers are working on developing software to recognise the text of Indian scripts like Bangla, Devanagari, Gurmukhi, Arabic, Numerals, etcetera. However, exchange data between people and computers continues to be a challenge. Recognition of text is not an easy work due to its complex and challenging structure. There are many reasons, which make text recognition a difficult task, which includes different writing styles of the writers, complexity of the script or text. For example, a Gurmukhi script has different writing zones (upper, middle, lower zone), which make its recognition difficult and also the cursive nature of the writers complicate it further to recognise. A detailed study based on the combination of various feature extraction and classification techniques is presented in this paper for various types of text. However, more stress has been emphasised on Gurmukhi Script. This paper also presents the accuracy results obtained for the recognition of text using various recognition methods. [ABSTRACT FROM AUTHOR]

Waste disposal along with its management is the main concern worldwide. The waste that is produced on a daily basis is impossible to control hence, it is rising constantly. The manually segregated garbage is less efficient, less feasible and is time consuming. The system introduced in this paper is based on machine learning that uses CNN algorithm to segregate the item to be disposed into the bin as plastics and non-plastics like plastic bottles, metal cans, paper cups, tetra-packets, and wrappers. Further, waste is monitored using an ultrasonic sensor and after the limit is reached, the respective authorities are notified using an application. There is a need for responsibility to be taken and awareness to be created amongst the people regarding waste disposal. Due to these reasons, we have implemented a conditional reward system to appreciate the user for their valuable contribution towards cleaner environment. [ABSTRACT FROM AUTHOR]

Trash classification is an important activity which helps in identification of waste. In this paper, a Classical- Quantum Transfer learning model, namely TrashQNet is proposed to classify trash into two classes, Organic and Recyclable trash. Classical-Quantum Transfer learning is a combination of machine learning and quantum computing. The proposed TrashQNet model uses a pre-trained DenseNet169 network for the feature extraction process and a variational quantum circuit as a classifier. The performance of TrashQNet is compared with classical machine learning models K- Nearest Neighbour & Support Vector Machine, deep learning model - Convolutional Neural Network and transfer learning model. TrashQNet outperforms all these models, it achieves an accuracy of 94% on test dataset. [ABSTRACT FROM AUTHOR]

Clinical laboratory testing is a crucial part of delivering high-quality health care. Lab tests are ordered by a doctor or another clinician to determine, control, or demonstrate a patient's condition. The process starts with the preparation of a pattern of the patient's blood, tissue, or other organic material, which is then sent to a lab to be uniquely identified and checked to make sure it is appropriate for the test. Some exams are graded by hand, but the majority are completed with the aid of technically advanced equipment. The electrocardiogram (ECG)is a crucial tool for identifying cardiovascular issues. The huge number of ECG records were formerly recorded on paper. Examining paper ECG recordings manually can be challenging and time-consuming. The Machine learning algorithms, Support vector machine and other algorithms in particular show promise for systems that have abilities to detect cardiovascular illnesses where the effectiveness differs in terms of accuracy, recall and precision. The proposed research provides a thorough analysis of several data models for predicting patient illness for clinical diagnosis in cardiology. [ABSTRACT FROM AUTHOR]

Various machine learning algorithms are developed with an aim to create precise and trustworthy models and extract knowledge from data sources. Deep expertise in the field of machine learning is required for the challenging task of choosing the right algorithms for a specific dataset. There is no single algorithm that outperforms all others across all applications and different datasets. The difficulty of choosing an appropriate algorithm for a specific task in specific domain is related to the properties of the dataset. Properties of the dataset are measured through meta-features. Meta-features describe task and can provide explanation how one machine learning approach outperforms other algorithms on a given dataset. Learning about the effectiveness of learning algorithms, or meta-learning was developed to deal with this issue. Focus is required because previous research papers have not successfully identified meta-features in particular domains. In this research, we have evaluated various meta-feature characterization methodologies and have concentrated on basic meta-features. Business domain data is in the focus of this paper. We computed basic (general) meta-features and illustrated several use cases for their applications. [ABSTRACT FROM AUTHOR]

Face Emotion Detection Music Player is an innovative idea that combines computer vision and machine learning techniques to create a unique and interactive music player. This Research Paper studies the uses of Convolutional Neural Networks (CNN) to detect and analyze facial emotions in real-time and dynamically create playlists based on detected emotions. The music player is designed to record facial expressions using a webcam or other suitable camera. To reliably identify facial expressions including happiness, sorrow, rage, surprise, and more, a CNN model is trained on a sizable collection of face images. The trained model demonstrated an astounding 74.46% accuracy in identifying emotions when combined with music player software running on a PC or other suitable device. The CNN model analyses the photos to determine the user's emotional state as they interact with the music player in real-time, as captured by the camera. Based on the detected emotions, the player automatically selects and plays songs from a predefined music collection that matches the user's emotional state. For example, if the user looks happy, the player can play happy and energetic songs, while if the user looks angry, it can play more calming and soothing music. Face Detection Music Player offers a personalized and dynamic music listening experience as the music selection is constantly updated according to the user's emotions. This research paper illustrates how computer vision and machine learning techniques may be used to produce interactive programs that can instantly adjust to the moods and preferences of their users. It has uses in user experience research, entertainment, and mental health, among others. [ABSTRACT FROM AUTHOR]

There are various models out there, both theoretical and experimental, which are used to predict the fatigue crack growth in metals. Machine learning based computational models have been recently developed to predict such crack growth rate in a more comprehensive manner. This paper presents a supervised machine learning model with a better accuracy than the existing machine learning models. The experimental data obtained from the fatigue test carried out on CT specimen of material SA516Gr70 is used to test and train the model. The machine learning model XGBoost, which is implemented in this paper, is not only more accurate but also less likely to over fit when exposed to unseen data. Three alternative algorithms are investigated and by the process of elimination, best one is chosen. The paper shows the results received from some of those models, which were important in order to select the best one. The metrics used to filter and tune the model's performance are R2 score and mean squared error. The procedure for finding the optimal model and its results have been discussed. [ABSTRACT FROM AUTHOR]

The detection of chest diseases from X-ray images is a crucial aspect of medical diagnostics, playing a vital role in the early identification and treatment of respiratory conditions. This review paper gives a general overview of how machine learning algorithms are used to identify chest diseases from X-ray pictures. It emphasizes the importance of accurate and timely diagnosis of chest diseases, while acknowledging the challenges faced by conventional diagnostic methods. The review highlights the potential of machine learning as a promising approach to enhance diagnostic accuracy and efficiency in this field. It explores the utilization of both handcrafted features and deep learning-based approaches for extracting informative features from chest X-rays. Furthermore, it discusses the adoption of various machine learning algorithms, including support vector machines, random forests, and convolutional neural networks, for effective detection of chest diseases. The review paper also recognizes the significance of training and validation strategies in ensuring the robust development of models. Additionally, it addresses the potential impact of the proposed methodology on chest disease detection and patient outcomes. The review paper discusses challenges and limitations associated with this approach, such as data availability, interpretability, and ethical considerations. In summary, this extensive analysis strives to make a valuable contribution to the field by offering insights into cutting-edge machine learning techniques, recognizing existing challenges, and proposing potential avenues for future research. [ABSTRACT FROM AUTHOR]

This state-of-the-art review paper provides a comprehensive overview of meta-learning techniques for learning to learn in machine learning. Meta-learning called "learning to learn," is an approach that enables models to learn from new tasks learning quickly and efficiently, based on prior knowledge and experience. The paper discusses various meta-learning techniques, such as model-based meta-learning, metric-based meta-learning, optimization-based meta-learning, and memory-based meta-learning. The review also covers the applications of meta-learning in different domains, including natural language processing, computer vision, and reinforcement learning. Additionally, the paper discusses the benchmark datasets, challenges along with the future directions of meta-learning research, highlighting the need for developing more efficient and scalable meta-learning algorithms. Moreover, the proposed Meta-Learn framework incorporates a hierarchical structure to facilitate knowledge transfer across multiple levels of abstraction, and it also includes a memory component that enables the system to store and reuse previous experiences. Overall, this review aims to provide a comprehensive overview of Meta-Learning techniques for learning to learn in Machine Learning and proposes a novel Meta-Learn framework that could potentially enhance the performance and scalability of Meta-Learning algorithms. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak impacted drastically to education and most of educational institutions started preferring online education for students. However, after the settlement of the pandemic there is uncertainty among people about whether they should prefer online education for furthermore or start in offline mode to make it more interactive, so this paper is about an analysis of people's sentiments and emotions through Tweets about COVID-19 Education. This paper aims to study the reaction of people around the world toward online education during COVID-19. This study is conducted on the basis of the responses of students, teachers, parents, college professors, etc. We started with labeling the data into three sentiments namely positive, neutral, and negative and for validation then we used Machine learning (ML) classifiers namely, Logistic regression, Decision tree, Random Forest, Multilayer Perceptron (MLP), Naïve Bayes, Support vector machine (SVM), K-nearest neighbors (KNN), and XG-Boost. Then we performed emotion detection by considering 5 emotions namely happy, surprise, sad, fear, and angry and for validation we used ML classifiers. After applying all these ML approaches, the XG Boost ML classifier achieved the highest accuracy of 94% in classifying the tweets as positive, neutral, or negative, and 96% accuracy in classifying the tweets as happy, surprised, sad, fearful, or angry. [ABSTRACT FROM AUTHOR]

One of the main forces advancing Industry 4.0, also known as the fourth industrial revolution, is machine learning (ML). This paper examines the machine learning (ML) models application for biogas production prediction in anaerobic digestion (AD). This study's primary objective is to determine which ML techniques and models are utilised in the AD process. In addition, this study identifies the type of ML techniques, input and output parameters, and software used. Researchers have widely employed a couple of ML models in biogas production. After reviewing the 15 most recent papers, it was discovered that the Artificial Neural Network (ANN) and Adaptive Network-Based Fuzzy Inference System (ANFIS) are the most commonly used types of ML. The most commonly used operating parameters in predicting biogas production were reaction time, temperature, pH, total solids (TS), volatile solids, volatile fatty acids (VFAs), and fixed solids. In conclusion, the review discusses the challenges and prospects of using machine learning in the AD process and provides recommendations for future implementation. [ABSTRACT FROM AUTHOR]

The sine and cosine algorithm has become a widely researched swarm optimization method in recent years due to its simplicity and effectiveness. Based on the advantages, the study in this paper delves deeper into the key parameters that influence the performance of the algorithm, and has implemented modifications such as integrating the reverse learning algorithm and adding elite opposition solution to create the modified Sine and Cosine Algorithm (the modified SCA). Furthermore, by combining the fuzzy k-nearest neighbor method with the modified SCA, the study simulates numeric datasets with two or multiple classes, and analyzes the results. The accuracy rate (ACC) achieved by the modified SCA FKNN in this paper is compared to other models, with data comparison results and tables presented for each. The modified SCA FKNN proposed in this paper has obvious advantages on accuracy rate(ACC). [ABSTRACT FROM AUTHOR]

Because the technology is growing exponentially and the usage of laptop systems is improving compared to auld langsyne, cheating that the progress is increasing day by day. Improper distribution of someone else's work is considered cheating. Manually cheating is difficult so this method should be automatic. There are varied tools which may be used for plagiarism detection. n changed format. In each field of education, it becomes a significant issue. Varied techniques and tools are derived of late to sight plagiarism. Varied techniques of plagiarism are there like text matching, copy paste, descriptive linguistic primarily based technique etc. This paper proposes a replacement technique enforced during a program, whenever we tend to utilize a text set to spot the derived half by comparison with some existing multiple files. Here we tend to place the idea of a machine learning language i.e. k-NN. It helps nation of North American to spot whether or not a paper is plagiarized or not. [ABSTRACT FROM AUTHOR]

The extensive existence about COVID-19 diseases has led to global initiatives to regulate and manage the virus with the goal of eradicating it. Machine Learning (ML) is one key for analyzing and combating COVID-19 in line with research. This is a hotly debated topic right now. Even though numerous studies are in line with medical literature, there is a requirement to follow maintain with fast-increasing quantity of papers on ML applications connected to COVID-19. Day-to-day information on the COVID-19 virus's transmission is critical for assessing the virus's global behavior. As a result, in the state of COVID-19, this paper examines forecasting methods for COVID-19 affected instances utilizing existing machine learning methods. Most of the ML algorithms used in the early detection and diagnosis of contagion are guided learning methods. The prognosis features reported by ML models are in line with medical literature findings. Many of the relevant studies are still in their early phases. One of the limitations of machine learning approaches is the use of unbalanced datasets exposed to bias in selection. [ABSTRACT FROM AUTHOR]

Due to the advanced and hasty development of technology nowadays, researches in relation to Artificial Intelligence (AI) such as Machine Learning (ML), Genetic Algorithm (GA), Neural Network (NN) and Expert System (ES) are progressively introduced. These AI types are also frequently presented in building a new and profitable Algorithmic Trading System (ATS). Apart from Fundamental Analysis (FA), Technical Analysis (TA) based on Technical Indicators (TIs) is one of the most AI used in the development of ATS. However, there is a lack of research despite the available knowledge on TIs in ATS where AI is implemented. Therefore, this study aims to review the ATS current work comprehensively using TIs in AI. A systematic literature review (SLR) has been performed to achieve the objective of this study. This article analyses the rough and systematic literature search process for 17 selected papers from 2010 to 2020. The analysis depicts that in most published studies, GA with multiple TIs is applied. The result of this study leads to a pertinent suggestion that is aimed for further researches on ATS to be conducted by future researchers and traders. [ABSTRACT FROM AUTHOR]

The performance of university students during their academic session are vital to their overall grade throughout their term in the university. There are multiple factors that could lead to the loss of performance but the foremost factor is their level of emotions. Previous research has shown that to determine the performance of the students, the best way to do so is by analysing their attention levels. With the development of portable Electroencephalogram (EEG) devices and machine learning algorithms, it is easy to obtain the students attention and emotion level during their academic sessions. This paper aims to present a method of obtaining the EEG signals using a portable EEG device and classifying it into the type of emotions that are present in the human brain. The EEG device will obtain the attention level and EEG signals during two scenarios which are lectures/tutorials and exams/quizzes. The signals are then compiled and analysed to determine the emotion labels based on a normalization process that categories the signals into positive or negative emotions. The dataset and labels are then used to train and evaluate multiple machine learning models and a deep learning model in order to determine which model has the best accuracy and performance. The chosen model is then used to predict the emotions of several students during both scenarios and the average emotions are then compared with their average attention to determine the effect of emotions on the students' performance. Hence, this paper will first provide a method on obtaining the emotion labels, followed by the models' development and finally correlating the predicted emotions with the students' performance during their academic sessions. [ABSTRACT FROM AUTHOR]

The enormous benefits and applications of Image classification and recognition are umpteen. Machine learning algorithms and Deep Neural Networks are like windfall to fathom the objective proficiently in streamlined manner. The prevalent improvement in this technology is that these networks do not call for any prior blueprints in terms of algorithms as prerequisites. The presented paper is an attempt to create a Convolutional Neural Network from scratch to classify the images from the well-known dataset – Cats and Dogs into their relevant baskets. Manifold open source accessible approaches to amplify the efficiency of the network are no more onerous. Further, data augmentation technique boosts the efficiency tremendously by extending the dataset with reoriented features from the same images. To untangle the same problem, Transfer Learning is also a compelling technique in which all the layers, neurons in each layer, weights of each neuron and all other parameters are predefined and we can amend the output layer as per the classes in the respective problem statement. In the present paper, we have tried to obtain a comparable efficiency with a significant reduction in parameters. [ABSTRACT FROM AUTHOR]

The manual detection of exoplanets is subject to human error and very labor-intensive. This paper introduces, classical machine learning techniques for the detection of exoplanets. For the testing, three different machine learning algorithms, i.e., Logistic Regression, SVM, and Decision Tree, with and without filter are used. The model is designed to detect transiting planets, with the help of light curves by searching a regular pattern in the flux of the host star. The finding demonstrates that these algorithms can also perform well as compared to the deep learning models and provide a reliable system for detection. The final model after implementation of filter called SMOTE achieves an accuracy of 94.21%, recall of 98.54%, and f1-score of 94.02%. [ABSTRACT FROM AUTHOR]

Fake news is a fabrication of the original news intentionally to deceive readers. Internet and social media help such news to spread widely and affect individuals and society negatively. Because of the lack of control over writing the posts on social media. The spread of this type of news has become much more than before. We present one of the most societal severe affairs for misinformation, especially in the presidential elections and fake news related to health like COVID-19. Therefore, there is a need for machine learning algorithms to detect and classify all types of fake news that is difficult to be detected by a human and experts. In this paper, Covid-19 FNs are detected using the Term Frequency-Inverse Document Frequency (TF-IDF) as features extraction and two machine learning algorithms (SVM, Multinomial Naive Bayes) as a classifier. The results show that the accuracy of the proposed algorithms is equal to 94.83% and 91.38%, respectively. We conclude that using machine learning algorithms can help detect such fake news based on good achieved accuracy. [ABSTRACT FROM AUTHOR]

A large segment of revenue in many developing countries comes from agriculture. But the perennial incline of diseases in crops is much severe issues and seeks attention of researchers during the past few years. Agriculturists are unable to make timely prediction of diseases due to lack of knowledge and large cultivation areas. Presently, Machine learning (ML) and its sub branch Deep learning (DL) brings a sea level change in conventional disease prediction methods and incline accuracy also. Using visualization techniques many DL architectures are implemented for identification and classification of plant diseases. This paper provides a systematic review of various existing DL and ML models for content-based image retrieval for data sets related to agriculture and investigate the future scope which helps to learn the potential of DL along with multiple visualization methods for recognizing plant diseases which is fruitful for farmers. Prediction and analysis of diseases instead of leaves at other parts of plants is not done yet, DL can help to work from this perspective also. The main motive of this study is to prediction of diseases at various parts of plant to overcome the issues in agriculture field to incline the profit of Farmers. [ABSTRACT FROM AUTHOR]

Cricket is an outdoor game which consists of 2 teams, each having 11 players, out of the two teams, one bats and the other one bowls and fields. There are various formats of cricket like Test cricket, ODI (One Day Internationals) cricket, and T-20 cricket. Test cricket format takes 5 days to reach to a decision which can be in Win, lose or Draw. Due to reduced playtime and speedy decision, nowadays T20 cricket is widely popular. Due to the popularity of T-20 across cricketing nations; many countries have started their own leagues. In India, IPL (Indian Premier League) is very much popular. Now-a-days team's management is using various technologies to improve their team's performance and also takes the help of various technologies to analyze the opposite team's performance. Prediction of the score is one of the most important events in cricket. Once the score prediction is done, the chasing team prepares itself accordingly. We use deep learning and ML algorithms for finding the first inning's predicted score. In the current work, we have used Linear Regression, Ridge Regression for model building and plot various graphs and find the errors to evaluate our model. In this model, we have used the IPL dataset taken from Kaggle and cleaned it using various available techniques. After building the model, we have analysed it by finding errors. Then we have deployed it in the graphical userinterface that was built with the help of HTML and Flask. [ABSTRACT FROM AUTHOR]

In the machine learning era, aspect-based sentiment analysis is a very popular idea that is still in the research realm. The main objective of this paper is to derive new pattern-based rules for Aspect Extraction. This focuses on the Explicit aspects where the aspect is precisely defined, unlike the previously researched papers on Aspect Extraction, this model does not limit itself to the noun/noun phrases but also focuses on the other words like verbs in a review. Newly extracted pattern rules for aspect extraction would be proved beneficial in terms of sentiment analysis of online customers. [ABSTRACT FROM AUTHOR]

Today, the world is exposed to many widespread health crises. One of these crises is the emergence and spread of the covid-19 virus, where any person becomes infected with this disease through the rapid transmission of infection between people, either by palpation or by inhaling droplets of carriers of this virus. Hence, there is a need to build a tracking system to respond quickly to problems. This paper proposes using one of the unsupervised machine learning algorithms, the K-Means Clustering Data, to determine the areas with the most significant percentage of infections by dividing them into infected and uninfected human groups. Where the infected persons are divided into three high groups (C1), medium (C2), and low (C3), and follow the path of the infected persons using Google Maps. The contribution to this paper is to support appropriate decision-making by public health authorities by predicting endemic areas and how to address problems that occur as they can use this system to reduce other infectious diseases. [ABSTRACT FROM AUTHOR]

Health and medicine has gained a lot of importance in today's digital world, where evolving technology is being used to fight against almost all the known diseases thoroughly. But, according to reports, more than two lakh people in China and one lakh in United States of America are dead every year due to the mistakes made while prescribing errors. This paper aims to propose a system which takes as input the symptoms from the patient to predict the disease, which is followed by recommending the correct medicine. This system consists of database system module where disease dataset contains attributes like "DiseaseID", "Disease", "Symptoms" and "count of occurrences" and drug dataset has additional attributes such as "drug" and "drug_rating". In the data preparation module, a new data frame is created where each disease with its associated symptom is given "1" and "0" if it is not the symptom related. After all these steps of preprocessing are performed on the raw dataset, the dataset is now ready to be acted upon by a machine learning algorithm. Models like Decision Tree, Random Forest and Naive Bayes were applied on the training-dataset. Entire knowledge database was fitted in models and it was seen if not much dataset is used Random Forest And Naïve Bayes classifiers showed 84% accuracy whereas Decision Tree gives 92%. But Random Forest also gives accuracy similar to Decision Tree for large dataset. Hence for disease prediction we use Decision Tree. The recommendation is done on the basis of the rating of the drug. After this, only the drug with the highest rating is included in a newly formed dataset and the rest of the drugs are ignored. The system is implemented for user access by making use of Tkinter a python GUI Library for constructing the desktop end user interface. This paper deals with the design and implementation of a system which performs the function of prediction of diseases and the recommendation of medicines, which adds to the capabilities of the present systems in health infrastructure. [ABSTRACT FROM AUTHOR]

This paper discusses a keen way to manage and distribute water from a water tank or reservoir to consumers with minimal wastage. The aim of this paper is to discuss about the implementation of an IoT (Internet of Things) based water distribution system that will diminish the water wastage by consumers. The setup can be extended from a small society to the entire urban infrastructure. The proposed system consists of a waterflow sensor, actuator, microcontroller (ESP8266), and a web-application which can remotely control, cut, monitor, and analyze the water usage. Administrators will have access to restrict the water flow and upon consumers' requests ancillary water can be supplied. Also, this system is equipped with machine learning techniques for predicting the usage and learns the flow patterns of water among different user spaces, which will be useful to estimate the water requirements. Also, classify consumers based on their usage. The application is equipped with a reward system where consumers are rewarded with ancillary water when their consumption is minimal. [ABSTRACT FROM AUTHOR]

Twitter is known to be one of the familiar social networking platform these days, among many others, with a lot of user engagement. This microblogging site encourages social interactions, allowing users to stay up to date on the latest news and events and share them with others in real time. Tweets are limited to 280 characters and is allowed to include links to related websites and tools. With a platform having such wide reach, it is prone to be targeted negatively and spams are one way to do it. Spammers use this platform to display malicious content that is inappropriate and harmful to users worldwide. Machine Learning uses various approaches that can be used to detect spam and overcome it. However, with the advent of recent technologies it has been observed that the properties of tweets vary overtime making it difficult to detect spam leading to the "Twitter Spam Drift" problem. This paper reviews the papers published since 2018 that have focused on the spam drift problem and gives a comparative analysis of the different algorithms that are utilized on the various data sets to tackle such a problem. [ABSTRACT FROM AUTHOR]

The aim of this paper is to carry out work those results in a good friend to farmers in every way. The work primarily recommends a high-yielding crop to the farmer based on regional soil data and the farmer's requirements. The job entails analysing a database of different types of soil from a specific area. To improve the analysis' efficiency, a machine learning method called linear regression is used. The study also implements disease detection in crop leaves using image processing techniques and provides remedy/solutions to farmers with diseased leaves, which promises to be a preventative measure in the future. The study carried out has an effect on various cultivation methods for various crops, assisting farmers as a "SAHA PATI" for this work. This service assists farmers in determining the best method of cultivation based on climatic and geographical factors. The final section of the Paper introduces a programme that provides current market prices for various crops by connecting to a government website that is updated on a regular basis. This service assists farmers in avoiding being duped by business intermediaries. The overall work done with the agriculture-based component turns out to be the best application for the current situation. [ABSTRACT FROM AUTHOR]

This paper proposed to analyze various DDOS ATTACK protection mechanisms for cloud environment. DDOS ATTACK is very big threat for networks and communication. Now a day every sensitive information stored in cloud environment and other types of dual services such as PAAS, SAAS, IAAS were also attacked by DDOS ATTACKS. So cloud environment must be protected from these types of DDOS ATTACKS. In this paper we provide complete outlook for various DDOS protection, such as machine learning methods with various other methods. Different types of methods tested with test data downloaded from various authentic sources in this paper shows structured ATTACK and prevention. Architecture such as test website ATTACKED by various DDOS ATTACK tools and prevention method executed by separate module or integrated with ASP.NET website. [ABSTRACT FROM AUTHOR]

This paper presents the detailed overview on the artifacts related to Computed Tomography (CT) specifically Cardiac CT (CCT), their classification and various reduction techniques. In CCT, the motion artifacts occurs mainly due to the pumping of the heart which has to be reduced during and post CT reconstruction. The main drawback of CT machine is that it emits high radiation during scanning. Therefore, Low Dose CT (LDCT) and Limited Angle CT (LACT) have been proposed by various researchers to overcome the problem of emission of high radiation during scanning which provides better image quality with minimum radiation. However, the artifacts generated during scanning and in image reconstruction phase also affects the quality of the image which makes it a challenging task to medical experts during their interpretation. In view of that, the extensive research have been carried out by various researchers to reduce the noises or artifacts using traditional and machine learning techniques in recent past. Finally, the paper is concluded by presenting the current state of art deep learning approaches to enhance the performance of the existing methods. [ABSTRACT FROM AUTHOR]

The first, and at the same time one of the most important tasks in building a predictive system is the task of analyzing the original data set. The next task is to process the data according to the purpose and objectives of the predictive system. These two tasks are often not given enough attention and, as a consequence, the developer does not get the desired result as a result of building the system. This paper discusses methods and algorithms for data analysis and processing that can help in building a predictive system. The data set from the machine learning competition "ASHRAE - Great Energy Predictor III" is used as input data. The analysis phase focuses on constructing graphs and drawing conclusions from them. The paper shows how to use graphical information to find anomalies in the data and eliminate them. Examples of graphs that provide information useful for building machine learning models are given. The data processing phase describes the transformations performed on the data. It considers the transformations that lead to positive results as well as those that lead to negative results. [ABSTRACT FROM AUTHOR]

Machine learning is an artificial intelligence (AI) technique for automation and prediction that is widely utilized today. This paper looks at the fledgling usage of machine learning in the legal field. The first portion focuses on the fundamentals of machine learning. The relationship between machine learning and law is discussed in the next section. Many parts of the law can be interpreted in terms of data, and machine learning works by recognizing patterns in data. Machine learning technologies can be used to learn about data-driven aspects of the law. After that, this paper looks at how machine learning is used in law while also recognizing the technology's strengths and limitations. It finishes by examining some current social debates around the use of machine learning in legal contexts. [ABSTRACT FROM AUTHOR]

Choosing a tourist place for vacation is very important, as a user spends a lot of money and efforts to choose a location wisely. To alleviate this effort, a travel recommendation system is introduced. However, the current recommendation system falls short of providing a perfect tourist location because there are different types of users, each with their own preferences. So here, a user needs a personalized recommendation system. This paper focuses mainly on creating a machine-learning system that is a travel recommender system. It recommends a particular tourist place in two different ways. The first is based on ratings given by other users, and the second is based on previous images of places that users have previously visited. This machine learning model can be further linked to a website using Flask to make it easier for people to use it and search for their favorite destination. This system recommends a tourist attraction based on two factors: first, the ratings of each location given by other users, and second, the images users prefer. This paper mainly focuses on using SVD, LDA, and OpenCV. [ABSTRACT FROM AUTHOR]

In the present time, the demand of energy is increasing rapidly. The heavy demand of energy is in the smart cities domain as there are number of sub domains like Smart Homes, Smart Transportation, Smart Healthcare, etc. So, as the result the domain is opening the new research direction for the industrialist, researchers, and scientists. They are attracting towards the domain of smart city for different kinds of research projects. The primary concern of this paper is to focus on the energy consumption of smart homes. In this paper different Machine Learning (ML) and Deep Learning (DL) models are being implemented and the results are analyzed to check the performance of the different models on the dataset of smart home energy consumption. This study found that the Random Forest is predicting the energy consumption with lowest error with 0.6616, 0.4377, 0.3171 RMSE, MSE, and MAE respectively. The performance of the models is being evaluated in the terms of Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE). This study found that the most suited model for energy consumption data in smart city is Random Forest. [ABSTRACT FROM AUTHOR]