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IEEE Integrated STEM Education Conference (ISEC)

作者： Sumanth R Moole Thomas Jefferson High School for Science and Technology USA

ISBN: (纸本)9781665413848

In modern warfare, intercepting moving enemy targets such as tanks, aircraft, missiles, and drones plays a crucial role. These targets are either controlled by enemy personnel or by sophisticated electronic systems. Therefore, their movements are best characterized by random motion subject to certain physical laws. Predicting these motions is extremely complex and often requires continuous tracking through sophisticated radar equipment. machine learning algorithms, such as Artificial Neural Networks, have proven to be effective in learning many real world motions of vehicles on the roads and have been extensively used in the autonomous vehicles. Artificial Neural Networks use activation functions to determine the output of a model from the given observations. After training the model with appropriate activation function, the model can be used for predictions. In this process, the activation functions play a crucial role. Selecting the correct activation function is critical to the success of the model. This project simulates the moving enemy target using a BristleBot (a brush-head fitted with vibrating motor which generates vibrations in the bristles thus propelling the BristleBot) which moves on a flat surface. The motion of the BristleBot is digitized by recording the X-Y coordinates on the path it has taken from the beginning of the run to the end of the run. These runs are repeated and data from multiple runs is stored in a database. Using R Programming language, a neural network training algorithm is simulated where the activation function can be changed (slope-intercept linear function y = mx + b with various slopes and intercepts, quadratic function y = a x 2 + bx + c with various a, b, and c values). The resulting models corresponding to each training session are compared with each other to find their similarity to the paths taken by the BristleBot. The effectiveness of these activation functions is then measured by the similarity score. The trained model (

关键词： Training Vibrations Missiles machine learning algorithms Target tracking Artificial neural networks Predictive models

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Music Genre Categorization using machine learning algorithms

Music Genre Categorization using Machine learning Algorithms

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Computational Intelligence and Computing Applications (ICCICA), International Conference on

作者： Vempaty Prashanthi Srinivas Kanakala V. Akila A Harshavardhan Infomation Technology Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

ISBN: (纸本)9781665420419

Music genre prediction is a difficult job in the field in Retrieval of Musical Data. Music group categorization is essential for the music recommending systems, since genre has a high weight in such systems and their recommendations. A machine learning model is designed which automatically classifies the genre of a music clip. Here, we are going to extract acoustic music features with the help of digital signal processing and then classification of music is done with the help of machine learning methods. Librosa, is a tool we will be using for audio feature extraction, which offers a full-featured work-flow situation for low and high-level audio features. In this paper, we are going to utilize k-Nearest Neighbours method for the reason that in many research it is shown that this method gives good outcomes in such scenario. We will be using music dataset GTZAN Genre Collection (1010 clips).

关键词： Measurement machine learning algorithms Music machine learning Predictive models Nearest neighbor methods Feature extraction

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Stock Prediction and analysis Using Supervised machine learning algorithms

Stock Prediction and analysis Using Supervised Machine Learn...

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Computational Intelligence and Computing Applications (ICCICA), International Conference on

作者： Ajinkya Yelne Dipti Theng G. H. Raisoni College of Engineering Nagpur

ISBN: (纸本)9781665420419

Using Supervised machine learning, our project is to analyzed and predict the stock value. As due to pandemic situation stock market trading is the most learned and become important activities to earn money as a second source of income in the people of India. The concept of predicting a stock's future worth is known as stock trading or stock prediction. Stock market is difficult to understand and to predict the value of stock. The majority of stock traders utilize various analytical techniques, as well as time series analysis, when seeking to make stock forecasts. So, we need a better tool to get out of this contemptuous situation and help the common man to make profit. In this research, we discuss a machine learning strategy that will be taught using publicly released stock data to build information, then using that information to make a valid *** accuracy and prediction of stock Classification and Regression algorithms are used with Kaggle dataset a machine learning technique comes under supervised learning that are Random Forest, Decision Tree, and Logistic Regression to predict stock prices for the given company previous year data, employing prices with daily trading prices. Python is the coding language used to anticipate the stock market using machine learning. Result come across that Regression model has more accuracy and can predict more accurate stock price.

关键词： machine learning algorithms Pandemics Time series analysis Supervised learning Predictive models Prediction algorithms Stock markets

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Fraud Detection and Prevention Using machine learning algorithms: A Review

Fraud Detection and Prevention Using Machine Learning Algori...

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International Conference on Electrical Energy Systems (ICEES)

作者： G. Jaculine Priya S. Saradha VELS Institute of Science Technology & Advanced Studies(VISTAS) Chennai Tamilnadu India

ISBN: (数字)9781728176123

ISBN: (纸本)9781728176130

Digital Fraud has become a threat across all the sectors. Its pivotal for any organization now to have a dedicated focus to detect and prevent fraud and increase their focus on Security. Digitization has revolutionized the way we perform our day to day transactions with a click of a button. On the flip slide, it has also opened up threats through bad actors who can misuse the missing controls in digital apps and thereby impersonate themselves as real customers and perform costly transactions on their behalf resulting in financial losses. Organizations will have to pay attention as it also impacts its brand value. Organizations have learnt their lessons from the past to detect the fraudulent activities in real time by using multitude of factors like using complex algorithms trying to detect fraud patterns. However, fraudsters are also getting intelligent day by day and it requires continuous focus to prevent frauds and to stay ahead of the fraudsters. It is important to monitor key patterns that might help differentiate a real vs fraud transaction. Capturing Customer information like Geo location, authentication, session, device IP address can be maintained. machine learning and application of Artificial Intelligence will play an important part in learning and detecting fraud patterns automatically.

关键词： machine learning algorithms Standards organizations Organizations Real-time systems Security Proposals Monitoring

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Detecting Fake News using machine learning algorithms

Detecting Fake News using Machine Learning Algorithms

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Advances in Computing, Communication Control and Networking (ICAC3N), International Conference on

作者： Saumya Chaturvedi Ansh Saxena Akash Gupta Farhan Alam School of Computing Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9781665438124

The problem of fake news has become one of the most complicated issues facing society. Nowadays, via social media, false information will spread easily. In this respect, fake news needs to be identified as soon as possible to prevent adverse effects on individuals who may rely on such knowledge when making important decisions (e.g., presidential elections). In this post, we present a groundbreaking approach that utilises machine learning methods for fake news detection. Our studies show that the proposed approach uses the passive aggressive algorithm for correct result classification, such an algorithm remains passive and when there is a miscalculation, it becomes active, updating and adjusting. The aim is to make changes that correct the error, making very little changes to the outcomes and enables us to achieve promising outcomes. As per the studies and test performed the accuracy of this solution is about 96%.

关键词： machine learning algorithms Social networking (online) Databases Current measurement Voting machine learning Feature extraction

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Hand Gesture Recognition using Flex Sensor and machine learning algorithms

Hand Gesture Recognition using Flex Sensor and Machine Learn...

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IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES)

作者： Akash Kumar Panda Rommel Chakravarty Soumen Moulik National Institute of Technology Meghalaya Shillong India

ISBN: (数字)9781728142456

ISBN: (纸本)9781728142463

In this paper, we have proposed a mechanism of hand gesture recognition using flex sensors and Arduino UNO. The data acquired from the sensors corresponding to different hand gestures are analyzed with the help of different traditional machine learning algorithms. We also proposed an adversarial learning approach that performs better classification in comparison with these traditional learning models. The results of the proposed approach records a commendable accuracy of 88.32% with a precision of 81.77%, a recall of 84.37% and F1-score of 82.78%. The proposed model yields better results in terms of all these four performance metrics.

关键词： Measurement machine learning algorithms Flexible printed circuits Gesture recognition Speech recognition Medical services Sensors

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Comparative analysis of machine learning algorithms for prediction of smart grid stability^†

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2021年第9期31卷

作者： Bashir, Ali Kashif Khan, Suleman Prabadevi, B. Deepa, N. Alnumay, Waleed S. Gadekallu, Thippa Reddy Maddikunta, Praveen Kumar Reddy Manchester Metropolitan Univ Manchester Dept Comp & Math Manchester Lancs England Air Univ Islamabad Islamabad Pakistan Vellore Inst Technol Sch Informat Technol & Engn Vellore Tamil Nadu India King Saud Univ Comp Sci Dept Riyadh Saudi Arabia

The global demand for electricity has visualized high growth with the rapid growth in population and economy. It thus becomes necessary to efficiently distribute electricity to households and industries in order to reduce power loss. Smart Grids (SG) have the potential to reduce such power losses during power distribution. machine learning and artificial intelligence techniques have been successfully implemented on SGs to achieve enhanced accuracy in customer demand prediction. There exists a dire need to analyze and evaluate the various machine learning algorithms, thereby identify the most suitable one to be applied to SGs. In the present work, several state-of-the-art machine learning algorithms, namely Support Vector machines (SVM), K-Nearest Neighbor (KNN), Logistic Regression, Naive Bayes, Neural Networks, and Decision Tree classifier, have been deployed for predicting the stability of the SG. The SG dataset used in the study is publicly available collected from UC Irvine (UCI) machine learning repository. The experimentation results highlighted the superiority of the Decision Tree classification algorithm, which outperformed the other state of the art algorithms yielding 100% precision, 99.9% recall, 100% F1 score, and 99.96% accuracy.

关键词： machine learning algorithms smart grid stability

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Anomaly Detection Technique for Intrusion Detection in SDN Environment using Continuous Data Stream machine learning algorithms

Anomaly Detection Technique for Intrusion Detection in SDN E...

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Annual IEEE Systems Conference

作者： Admilson de Ribamar Lima Ribeiro Reneilson Yves Carvalho Santos Anderson Clayton Alves Nascimento Federal University of Sergipe UFS São Cristóvão Brazil Institute of Technology University of Washington Tacoma Tacoma Washington

Software Defined Networks (SDN) present some security weakness due to the separation between control and data planes. Thus, some operational security mechanisms have been designed to deal with malicious code in SDN. However, most of those approaches require a signature basis and present the inability to anticipate novel malicious activity. Other anomaly based approaches are inefficient due to the possibility of an attacker simulates legitimate traffic, which causes lots of false alarms. Thus, in this paper, we present an anomaly based approaches that uses machine learning algorithms over continuous data stream for intrusion detection in a SDN environment. Our approach is to overcome the main challenges that happen when developing an anomaly based system using machine learning algorithms. For characterising the anomalies, we have analysed a type of DDoS attack classified as infrastructure attack that considers the impact of both bandwidth and resource depletions. This type of attack imposes a high affect to the whole SDN. In fact, there are two types of attacks. The bandwidth depletion attack targets the channel between the switches and the controller through either UDP or HTTP flooding. Another way to exhaust outgoing and ingoing bandwidths is through ICMP flooding. The resource depletion attack attempts to exhaust the flow table of switches through SYN flooding. From experiments, we notice that the solution obtains 97.83% accuracy, 99% recall, 80% precision and 2.3% FPR for 10% DDoS attacks on the normal traffic. These results show the effectiveness of the proposed technique.

关键词： machine learning algorithms Conferences Intrusion detection Bandwidth Denial-of-service attack Control systems Malware

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A Comprehensive Study of machine learning algorithms for Predicting Car Purchase Based on Customers Demands

A Comprehensive Study of Machine Learning algorithms for Pre...

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Information and Communication Technology for Sustainable Development (ICICT4SD), International Conference on

作者： Anamika Das Mou Protap Kumar Saha Sumiya Akter Nisher Anirban Saha East West University Dhaka Bangladesh Florida International University Florida USA

ISBN: (数字)9781665414609

ISBN: (纸本)9781665447522

The automobile industry is one of the prominent industries for the national economy. Day by day car is getting popular for the private transport system. The customer needs review when he wants to buy the right vehicle, especially the car. Because it is a very costly vehicle. There are many conditions and factors matter before buying a new car like spare parts, cylinder volume, headlight and especially price. So deciding everything, it is important for the customer to make the right choice of purchase which can satisfy all the criteria. Our goal is to help the customer to make the right decision whether he will buy a car or not. Therefore we wanted to build a technique for decision making in-car buy system. That's why we propose some well-known algorithms to get better accuracy for a car purchase in our paper. We applied those algorithms in our dataset which contains 50 data. Among them, Support Vector machine(SVM) gives the best result with 86.7% accuracy of prediction. In this paper, we have also revealed the comparative results using different algorithms precision, recall and F1 score for all data samples.

关键词： Support vector machines Industries machine learning algorithms Prediction algorithms Information and communication technology Automobiles Sustainable development

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OptABC: an Optimal Hyperparameter Tuning Approach for machine learning algorithms

OptABC: an Optimal Hyperparameter Tuning Approach for Machin...

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International Conference on machine learning and Applications (ICMLA)

作者： Leila Zahedi Farid Ghareh Mohammadi M. Hadi Amini Knight Foundation School of Computing and Information Sciences Florida International University Miami FL USA Sustainability Optimization and Learning for InterDependent networks (solid) laboratory FIU Miami FL USA University of Georgia Athens GA USA

ISBN: (纸本)9781665443388

Hyperparameter tuning in machine learning algorithms is a computationally challenging task due to the large-scale nature of the problem. In order to develop an efficient strategy for hyper-parameter tuning, one promising solution is to use swarm intelligence algorithms. Artificial Bee Colony (ABC) optimization lends itself as a promising and efficient optimization algorithm for this purpose. However, in some cases, ABC can suffer from a slow convergence rate or execution time due to the poor initial population of solutions and expensive objective functions. To address these concerns, a novel algorithm, OptABC, is proposed to help ABC algorithm in faster convergence toward a near-optimum solution. OptABC integrates artificial bee colony algorithm, K-Means clustering, greedy algorithm, and opposition-based learning strategy for tuning the hyper-parameters of different machine learning models. OptABC employs these techniques in an attempt to diversify the initial population, and hence enhance the convergence ability without significantly decreasing the accuracy. In order to validate the performance of the proposed method, we compare the results with previous state-of-the-art approaches. Experimental results demonstrate the effectiveness of the OptABC compared to existing approaches in the literature.

关键词： Greedy algorithms machine learning algorithms Sociology Clustering algorithms machine learning Artificial bee colony algorithm Statistics

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