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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]

The paper proposes a novel approach for recognizing and classifying gym exercises using human pose detection with Mediapipe and LSTM, followed by matching the accuracy of the exercises using the Hungarian algorithm and cosine similarity. The proposed approach involves the use of a combination of cutting-edge technologies, including computer vision and deep learning, to recognize and classify a range of gym exercises accurately and efficiently. This study presents several advantages of this approach, including the ability to leverage temporal information, robustness to variations in lighting, camera angle, and occlusions, and the ability to handle a wide variety of gym exercises. Experimental results demonstrate that the proposed approach achieves high accuracy and outperforms existing methods for gym exercise recognition and classification. The paper also compares the proposed approach with existing techniques, highlighting its strengths and cost-effectiveness. It concludes by outlining the methodology, including dataset collection and pre-processing, landmark extraction, LSTM model training, exercise classification, and similarity analysis. Finally, the paper discusses potential future research areas in this field. [ABSTRACT FROM AUTHOR]

Natural disasters cause devastation, chaos, destruction, death, displacement, and much more when they occur. With the advancement of recent technologies especially in artificial intelligence and deep learning, the severity and impact of natural disasters could be reduced by predicting their occurrence. In this research, the flood detection (using images) system is developed using the proposed architecture of the Deep Convolutional Neural Network (CNN). In this paper, the main aim is to propose an automated System for precision disaster detection, in particular flood detection. The intelligent system will monitor vast problematic areas that are dubbed flood risk areas during the season. The intelligent automated system will systematically take images with real-time processing to detect floods. If a flood is detected, the system will automatically communicate to the ground station with the early warning to issue an early warning to all the inhabitants in the path of the flood. Since this paper will prove a concept, the other main contribution of this paper is a new dataset consisting of 9000 images collected from various sources on the Internet, labeled, and preprocessed. The Deep CNN architectures were trained and tested by constructing a new dataset of more than 9000 labeled images with flood and non-flood collected from different sources. Several experiments are performed with changing CNN architectural design parameters in order to get the best recognition rates. Experimental results show that 92.50% accuracy was achieved using GoogleNet. The second highest performance was achieved using AlexNet with an accuracy of 92.20%. [ABSTRACT FROM AUTHOR]

Road traffic by vehicles is continuously increasing through the world and they lead to terrible traffic jamming and significant delay in emergency services at cross roads. The vehicles stuck in that traffic jam may reach their destination with more delay which was lead to loss of lives or a property in some cases for example, emergency vehicles when stuck in the traffic jam like ambulance, fire engine vehicles, police jeeps, etc. Therefore it becomes most significant to control the vehicle traffic on the roads so as to acquire the effective control over the vehicular traffic based on the priority that may leads to saving the life of people in emergency situations by providing delay less services. This paper presents a survey on various methods and systems developed for the traffic classification according to the priority of emergency vehicle and for scheduling the vehicles accordingly in traffic. In this paper, different deep learning methods explored by various works are presented for effective increasing of vehicles creating traffic on the route of emergency vehicle. Finally, the works presented in this paper are compared and result analysis can be illustrated. [ABSTRACT FROM AUTHOR]

The paper presented the various techniques that can be applied for vehicle number plate identification. The extensive literature survey has been carried out on already presented technologies. Out of these technologies, three technologies have been selected and explained in this paper, the technologies are: OCR Method, Deep Learning Method and OpenCV Method. The comparison of three different technologies also available for vehicle number plates identification has been carried out on the basis of accuracy, complexity and cost. [ABSTRACT FROM AUTHOR]

Physical disabilities cause significant distress in people's daily routine, especially for leg amputees as their movement is restricted. The civilized society has the responsibility to provide them with better prosthetics so they can live a normal life. Previously, prosthetic legs were developed for lower leg amputees. Today's electronic prosthesis provides a wide range of mobility compared to a mechanical prosthesis. Powered prosthetics are quite expensive and hence not widely used. We have proposed a low-cost control system above-knee powered prosthetics leg that is reliable, and the complexity of the model is less. The prostheses works by taking input from a sensor placed in the model and six FSR402 sensor and an IMU sensor that record force muscle activity of the thigh during variation of leg movement. The value of motion of the joint knee angle is determined by sensors. In this paper, we are discussing and comparing potential deep learning control system namely artificial neural network (ANN) as baseline and recurrent neural network-long short term memory (RNN-LSTM) to predict human gait cycle. A multi-layer perceptron (MLP) method is implemented. In general, network architecture consists of input layer which is represents by six inputs of the FSR402 sensor and an IMU sensor, fully connected layers, and an output layer as joint knee angle. As the result model of ANN as baseline has metric value MAE and R squared are 6.21 and 68.7% respectively whereas model of RNN-LSTM has metric value MAE and R squared are 4.99 and 96.9% respectively. The result indicates the RNN-LSTM model is most likely to perform fewer errors based on the dataset. RNN-LSTM outperforms the task with a greater value compared to the ANN model as the baseline. This present study suggests that deep learning with the recurrent neural network-long short term memory (RNN-LSTM) is suitable for the prediction of human gait cycle movement from the FSR402 and an IMU signal in the overall walking task. [ABSTRACT FROM AUTHOR]

This paper aims to develop a student worksheet with a discovery learning-STEM model on dynamic electric material to build on students' problem-solving skills in YAPPI Wonosari Vocational High school. The method used is R&D with a 4-D model that includes defining, designing, developing, and disseminating. The instruments used include validation sheets and tests—data in a physics worksheet to improve students' problem-solving skills. This study's data collection methods contain expert validation, observation, pre-test, and post-test—the data analysis using the sign test. The results showed that 1) the development of student worksheets with discovery learning-STEM on dynamic electric material was feasible in terms of the validation results. This can be seen from the results of validation that show the feasibility of physics worksheets for improving problem-solving skills is in the excellent category, and 2) the effectiveness of using physics worksheet with discovery learning-STEM based on data obtained from the pre-test and post-test results can improve problem-solving skills as a problem in the implementation class. Thus, student worksheet developed with discovery learning STEM is compatible with improving problem-solving skills. [ABSTRACT FROM AUTHOR]

The rapid developments of deep learning techniques are the process that can easily create and use a fake media known as Deepfakes. Deep learning techniques can generate facial motion transferring, gender-changing, and face swapping with others. Currently, it is possible to create hyper-realistic digital media like images and videos through deep learning techniques that are easily accessible. However, that becomes a threat for everyone, if used for harmful purposes. This work presents how deep learning is used for Deepfake generation and detection. There are two methods that are most popular for Deepfake generation: Generative Adversarial Networks (GANs) and autoencoder techniques that is presented in this work. It also, introduces a survey of some researchers' works and methods that used for Deepfake detection during the recent years. This paper proposed a method to discriminate if that video is real or fake by using a Support Vector Machine (SVM) classifier, the method presented in this work for deepfake detection is based on Gray Level Co-occurrence Matrix (GLCM) as a feature extraction method applying on 4 bands to obtain 13 feature form each one to achieve good accuracy of the classifier with a score 70.47% that batter than previous work that didn't exceed 60% of accuracy. [ABSTRACT FROM AUTHOR]

Coronavirus Disease (COVID-19) pandemic has emerged as one of the biggest challenge faced by researchers, especially in public health system. Since the virus spread and mutates quickly, strategies are needed so that the public health system doesn't collapse due to overcapacity. Modeling and forecasting accurately the COVID-19 daily new cases is very important to understand and help carry out risk management for the outbreak control. To obtain the estimation of the Indonesian daily cases of COVID-19 based on daily confirmed cases, we present a comparative study between three deep learning methods in this paper: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Long Short-Term Memory (Conv-LSTM). The data used is daily confirmed cases from September 1st to September 7th, 2021. The result is Conv-LSTM model produces better performance than other methods. Here we also provide 7 day out-of-sample forecast for daily cases from September 1st to September 7th, 2021 using each model. [ABSTRACT FROM AUTHOR]

Timely identification of diseases in crops and taking immediate actions to prevent them from being completely destroyed is a key part of agricultural production. Environmental challenges like unfavorable weather conditions and climate changes directly affect the plant growth and it causes viral infections that can lead to the eradication of the crops. Microorganisms such as bacteria and fungi can also cause various diseases in plants. In order to provide the accurate and better solution for a plant problem, the farmers first need to identify the exact kind of illness that the plant is having. The existing method of manual diagnosis of plant diseases by a field expert is time consuming as well as costly. Farmers cannot afford the cost of frequent visits to remote laboratories for diagnosis. The use of advanced and effective deep learning models can solve this problem and predict plant diseases at an early stage. IoT has become very popular in the agricultural industry as it provides state of the art solutions to plant problems. This paper aims to study the recent related works on this area and to analyze the advanced methods and models used in the implementation and to allow future research to learn larger capabilities of IoT and Deep Learning in proactively detecting the plant diseases with improved system performance and accuracy. [ABSTRACT FROM AUTHOR]