Google Colab Research Articles

Design of a Saving-Energy Fuzzy Logic Controller for a Differential Drive Robot Based on an Optimization

In the problem of designing a Fuzzy Logic Controller (FLC) for robots in general and a Differential Drive Robot (DDR) in particular, the determination of the parameters of Membership Functions (MFs) and Fuzzy Rules (FRs) is a difficult, complicated, and time-consuming problem because this is mainly based on heuristics and the knowledge of experts. Therefore, this paper provides a new method to efficiently design an FLC for the DDR by using the Genetic Algorithm (GA). Here, the GA is used to generate and optimize both the parameters of MFs and the FRs according to the minimum kinetic energy loss criterion. For this purpose, a program is created in Google Colab® by using the Python language with the help of the “Pymoo” library to not only automatically generate all the suboptimal parameters of MFs and the suboptimal FRs but also the simulate and evaluate different used FLCs. This program is published as an open-source code so that all readers can browse, view, run, and modify the code themselves to design their FLC. The simulation results have shown that the designed FLC is much better than other used FLCs in terms of the minimum kinetic energy loss while other control performances are still good.

Automatic Segmentation of Organs-at-Risk in Thoracic Computed Tomography Images Using Ensembled U-Net InceptionV3 Model.

The objective of this article is to automatically segment organs at risk (OARs) for thoracic radiology in computed tomography (CT) scan images. The OARs in the thoracic anatomical region during the radiotherapy treatment are mainly the neighbouring organs such as the esophagus, heart, trachea, and aorta. The dataset of 40 patients was used in the proposed work by splitting it into three parts: training, validation, and test sets. The implementation was performed on the Google Colab Pro+ framework with 52 GB of RAM and 265 GB of storage space. An ensemble model was evolved for the automatic segmentation of four OARs in thoracic CT images. U-Net with InceptionV3 as the backbone was used, and different hyperparameters were used during the training of the model. The proposed model achieved precise accuracy for OARs segmentation with an average dice coefficient of 0.9413, Hausdorff value of 0.1838, sensitivity of 0.9783, and specificity of 0.9895 on the Test dataset. An ensembled U-Net InceptionV3 model has been proposed, improving the segmentation results compared with the state-of-the-art techniques such as U-Net, ResNet, Vgg16, etc. The results of the experiments revealed that the proposed model effectively improved the performance of the segmentation of the esophagus, heart, trachea, and aorta.

Finding Patterns of Cyber-Attacks and Creating A Detection Model to Detect Cyber-Attacks Using Machine Learning

This research is a finding and detection type of research. That paper focuses on exploratory data analysis and YOLOv3, you only look once at version three, a real-time recognizing model. The hypothesis of this research are, a cyber-attack gives the same pattern in all the different, different IP addresses, and the research model, the YOLOv3 model can detect the cyber-attack by seeing its pattern. First, the researcher collects more than one hundred seventy-eight thousand data from the cyber department of some companies. Then, the researcher does exploratory data analysis of that data in the jupyter notebook. Then, the researcher finds all the important information about cyber-attacks. Then, the researcher finds the patterns of some cyber-attacks from the information of the data collection. Then, the researcher collects pictures of the patterns. Then, with pictures of those patterns, the researcher labeled those pictures by labellmg software and create a zip file of them. Then the researcher use Google colab and trained, "you only look once version three", the YOLOv3 model to detect the name of the pattern. The machine can detect the pattern and can tell us about what cyber-attack it is by only seeing the picture of the pattern. The researcher finds the patterns for eight IP addresses and in all the IP addresses, the attacks give the same patterns. So, the hypothesis is true in all that cases. By this research model, we can easily know about any kind of cyber-attack in detail and also can find the cyber-attacks by their patterns. So, the researcher use jupyter notebook, Google Colab, and pycharm environment. Pattern study always plays an important role to know about anything. By knowing and learning about anything’s pattern, we can easily understand anything. This research does that thing greatly. The researcher first finds the patterns of some cyber-attacks, then make a model which can detect the cyber-attack by just seeing the pattern.

On the Identification of Agroforestry Application Areas Using Object-Oriented Programming

The detection of possible areas for the application of agroforestry is essential and involves the usage of various technics. The recognition of forest types using satellite or aerial imagery is the first step toward this goal. This is a tedious task involving the application of remote sensing techniques and a variety of computer software. The overall performance of this approach is very good and the resulting land use maps can be considered of high accuracy. However, there is also the need for performing high-speed characterization using techniques that can determine forest types automatically and produce quick and acceptable results without the need for specific software. This paper presents a comprehensive methodology that uses Normalized Difference Vegetation Index (NDVI) data derived from the Moderate Resolution Imaging Spectroradiometer instrument (MODIS) aboard the TERRA satellite. The software developed automatically downloads data using Google Earth Engine and processes them using Google Colab, which are both free-access platforms. The results from the analysis were exported to ArcGIS for evaluation and comparison against the CORINE land cover inventory using the latest update (2018).

Penajaman Kualitas Citra Digital Menggunakan Histogram Equalization

CCTV (Closed-circuit Television) is a surveillance camera that is installed on roads and in various rooms around the world with the aim of reducing crime and increasing public safety. Often, the image quality of CCTV has poor results, one of the causes is noise. This condition causes the information contained is sometimes not well conveyed. Image enhancement is a part of image processing which has the aim of improving image quality by manipulating image parameters. The application of this research is to sharpen the quality of screen capture on CCTV. The method applied is histogram equalization which can improve image quality. Histogram equalization method will be applied using Google Colab because its use has good portability.

Improving classification of correct and incorrect protein-protein docking models by augmenting the training set.

Protein-protein interactions drive many relevant biological events, such as infection, replication and recognition. To control or engineer such events, we need to access the molecular details of the interaction provided by experimental 3D structures. However, such experiments take time and are expensive; moreover, the current technology cannot keep up with the high discovery rate of new interactions. Computational modeling, like protein-protein docking, can help to fill this gap by generating docking poses. Protein-protein docking generally consists of two parts, sampling and scoring. The sampling is an exhaustive search of the tridimensional space. The caveat of the sampling is that it generates a large number of incorrect poses, producing a highly unbalanced dataset. This limits the utility of the data to train machine learning classifiers. Using weak supervision, we developed a data augmentation method that we named hAIkal. Using hAIkal, we increased the labeled training data to train several algorithms. We trained and obtained different classifiers; the best classifier has 81% accuracy and 0.51 Matthews' correlation coefficient on the test set, surpassing the state-of-the-art scoring functions. Docking models from Benchmark 5 are available at https://doi.org/10.5281/zenodo.4012018. Processed tabular data are available at https://repository.kaust.edu.sa/handle/10754/666961. Google colab is available at https://colab.research.google.com/drive/1vbVrJcQSf6\_C3jOAmZzgQbTpuJ5zC1RP?usp=sharing. Supplementary data are available at Bioinformatics Advances online.

MBDBMetrics: an online metrics tool to measure the impact of biological data resources.

There now exist thousands of molecular biology databases covering every aspect of biological data. This database infrastructure takes significant effort and funding to develop and maintain. The creators of these databases need to make strong justifications to funders to prove their impact or importance. There are many publication metrics and tools available such as Google Scholar to measure citation impact or AltMetrics covering multiple measures including social media coverage. In this article, we describe a series of novel impact metrics that have been applied initially to the UniProt database, and now made available via a Google Colab to enable any molecular biology resource to gain several additional metrics. These metrics, powered by freely available APIs from Europe PubMedCentral and SureCHEMBL cover mentions of the resource in full text articles, including which section of the paper the mention occurs in, grant acknowledgements and mentions in patent applications. This tool, that we call MBDBMetrics, is a useful adjunct to existing tools. The MBDBMetrics tool is available at the following locations: https://colab.research.google.com/drive/1aEmSQR9DGQIZmHAIuQV9mLv7Mw9Ppkin and https://github.com/g-insana/MBDBMetrics.

Image Recognition Based on Deep Learning with Thermal Camera Sensing

As the COVID-19 epidemic spread across the globe, people around the world were advised or mandated to wear masks in public places to prevent its spreading further. In some cases, not wearing a mask could result in a fine. To monitor mask wearing, and to prevent the spread of future epidemics, this study proposes an image recognition system consisting of a camera, an infrared thermal array sensor, and a convolutional neural network trained in mask recognition. The infrared sensor monitors body temperature and displays the results in real-time on a liquid crystal display screen. The proposed system reduces the inefficiency of traditional object detection by providing training data according to the specific needs of the user and by applying You Only Look Once Version 4 (YOLOv4) object detection technology, which experiments show has more efficient training parameters and a higher level of accuracy in object recognition. All datasets are uploaded to the cloud for storage using Google Colaboratory, saving human resources and achieving a high level of efficiency at a low cost.

Addressing the selection bias in voice assistance: training voice assistance model in python with equal data selection

In recent times, voice assistants have become a part of our day-to-day lives, allowing information retrieval by voice synthesis, voice recognition, and natural language processing. These voice assistants can be found in many modern-day devices such as Apple, Amazon, Google, and Samsung. This project is primarily focused on Virtual Assistance in Natural Language Processing. Natural Language Processing is a form of AI that helps machines understand people and create feedback loops. This project will use deep learning to create a Voice Recognizer and use Commonvoice and data collected from the local community for model training using Google Colaboratory. After recognizing a command, the AI assistant will be able to perform the most suitable actions and then give a response. The motivation for this project comes from the race and gender bias that exists in many virtual assistants. The computer industry is primarily dominated by the male gender, and because of this, many of the products produced do not regard women. This bias has an impact on natural language processing. This project will be utilizing various open-source projects to implement machine learning algorithms and train the assistant algorithm to recognize different types of voices, accents, and dialects. Through this project, the goal to use voice data from underrepresented groups to build a voice assistant that can recognize voices regardless of gender, race, or accent. Increasing the representation of women in the computer industry is important for the future of the industry. By representing women in the initial study of voice assistants, it can be shown that females play a vital role in the development of this technology. In line with related work, this project will use first-hand data from the college population and middle-aged adults to train voice assistant to combat gender bias.

Object detection and ship classification using YOLO and Amazon Rekognition

The need for reliable automated object detection and classification in the maritime sphere has increased significantly over the last decade. The increased use of unmanned marine vehicles necessitates the development and use of an autonomous object detection and classification systems that utilize onboard cameras and operate reliably, efficiently, and accurately without the need for human intervention. As the autonomous vehicle realm moves further away from active human supervision, the requirements for a detection and classification suite call for a higher level of accuracy in the classification models. This paper presents a comparative study using different classification models on a large maritime vessel image dataset. For this study, additional images were annotated using Roboflow, focusing on the types of subclasses that had the lowest detection performance, a previous work by Brown et al. (1). The present study uses a dataset of over 5,000 images. Using the enhanced set of image annotations, models were created in the cloud using Google Colab using YOLOv5, YOLOv7, and YOLOv8 as well as in AmazonWeb Services using Amazon Rekognition. The models were tuned and run for five runs of 150 epochs each to collect efficiency and performance metrics. Comparison of these metrics from the YOLO models yielded interesting improvements in classification accuracies for the subclasses that previously had lower scores, but at the expense of the overall model accuracy. Furthermore, training time efficiency was improved drastically using the newer YOLO APIs. In contrast, using Amazon Rekognition yielded superior accuracy results across the board, but at the expense of the lowest training efficiency.

Analysis and Classification of Bone Fractures Using Machine Learning Techniques

Human bones are the hard organs that protect vital organs such as the heart, lungs, and other internal organs. Fractures of the bones are a prevalent issue among humans. Bone fractures may develop from an accident or another circumstance when there is great pressure on the bones. It may be difficult and time-consuming to determine the site of a fracture in a patient who is suffering discomfort. The manual examination of fractures during radiological interpretation is a time-consuming and error-prone process. This may result in erroneous detection, poor fracture healing, and an extensive procedure. So, this research proposed an effective approach to rectifying bone fractures with the inclusion of the latest technologies. The solution is proposed by employing a Deep learning model. Moreover, a novel concept of classification is also incorporated. Firstly; the MURA dataset was collected from Stanford. Secondly; The proposed model used techniques like DCNN (Deep Convolution Neural Network) and use Alex Net model. Bones are classified into fractured or non-fractured through a classification approach. The proposed model was created using Google Colab. The proposed model was trained by repeating several experiments. The performance was evaluated based on accuracy. The suggested model results were compared with baseline algorithms as well. Consequently, the findings of this work will be useful for the medical industry.

Object detection and ship classification using YOLOv5

Using a public dataset of images of maritime vessels provided by Analytics Vidhya, manual annotations were madeon a subsample of images with Roboflow using the ground truth classifications provided by the dataset. YOLOv5,a prominent open source family of object detection models that comes with an out-of-the-box pre-training on theCommon Objects in Context (COCO) dataset, was used to train on annotations of sub-classifications of maritimevessels. YOLOv5 provides significant results in detecting a boat. The training, validation, and test set of imagestrained YOLOv5 in the cloud using Google Colab. Three of our five subclasses, namely, cruise ships, ROROs (RollOn Roll Off, typically car carriers), and military ships, have very distinct shapes and features and yielded positiveresults. Two of our subclasses, namely, the tanker and cargo ship, have similar characteristics when the cargoship is unloaded and not carrying any cargo containers. This yielded interesting misclassifications that could beimproved in future work. Our trained model resulted in the validation metric of mean Average Precision (mAP@0.5)of 0.932 across all subclassification of ships.

A Comparative Study of Breadth First Search and Depth First Search Algorithms in Solving the Water Jug Problem on Google Colab

A Comparative Study of Breadth First Search and Depth First Search Algorithms in Solving the Water Jug Problem on Google Colab

Identification of bearing fault in induction motor using random forest algorithm

In day-to-day life 90% of industries use induction motors due toless maintenance, high efficiency, good Power factor and low cost. Maintenance of the induction motor is important for continuous operation in industries.40-60% of the fault in Induction motors is due to bearing failure. Unexpected bearing failures could cause industries to spend money on repairing and replacing the bearing, along with that other nearby components might damaged. Failure in bearing, decrease the plant’s operating efficiency, increases downtime, raises operating costs and in the worst case, it may cause injuries to workers. The proposed method detects and diagnoses the bearing fault using vibration signals. The fault gets detected by using the Machine learning classifier. The proposed method achieves high accuracy in detecting and diagnosing the bearing fault. The proposed work is implemented using Google Colab (colaboratory) software. The result demonstrates the usefulness of the suggested of strategy enhancing the maintenance of bearing in good condition and safe operation in the induction motor.

Object detection and ship classification using YOLOv5

Using a public dataset of images of maritime vessels provided by Analytics Vidhya, manual annotations were madeon a subsample of images with Roboflow using the ground truth classifications provided by the dataset. YOLOv5,a prominent open source family of object detection models that comes with an out-of-the-box pre-training on theCommon Objects in Context (COCO) dataset, was used to train on annotations of sub-classifications of maritimevessels. YOLOv5 provides significant results in detecting a boat. The training, validation, and test set of imagestrained YOLOv5 in the cloud using Google Colab. Three of our five subclasses, namely, cruise ships, ROROs (RollOn Roll Off, typically car carriers), and military ships, have very distinct shapes and features and yielded positiveresults. Two of our subclasses, namely, the tanker and cargo ship, have similar characteristics when the cargoship is unloaded and not carrying any cargo containers. This yielded interesting misclassifications that could beimproved in future work. Our trained model resulted in the validation metric of mean Average Precision (mAP@0.5)of 0.932 across all subclassification of ships.

Object detection and ship classification using YOLO and Amazon Rekognition

The need for reliable automated object detection and classification in the maritime sphere has increased significantly over the last decade. The increased use of unmanned marine vehicles necessitates the development and use of an autonomous object detection and classification systems that utilize onboard cameras and operate reliably, efficiently, and accurately without the need for human intervention. As the autonomous vehicle realm moves further away from active human supervision, the requirements for a detection and classification suite call for a higher level of accuracy in the classification models. This paper presents a comparative study using different classification models on a large maritime vessel image dataset. For this study, additional images were annotated using Roboflow, focusing on the types of subclasses that had the lowest detection performance, a previous work by Brown et al. (1). The present study uses a dataset of over 5,000 images. Using the enhanced set of image annotations, models were created in the cloud using Google Colab using YOLOv5, YOLOv7, and YOLOv8 as well as in AmazonWeb Services using Amazon Rekognition. The models were tuned and run for five runs of 150 epochs each to collect efficiency and performance metrics. Comparison of these metrics from the YOLO models yielded interesting improvements in classification accuracies for the subclasses that previously had lower scores, but at the expense of the overall model accuracy. Furthermore, training time efficiency was improved drastically using the newer YOLO APIs. In contrast, using Amazon Rekognition yielded superior accuracy results across the board, but at the expense of the lowest training efficiency.

Performance analysis of pretrained convolutional neural network models for ophthalmological disease classification.

This study aimed to evaluate the classification performance of pretrained convolutional neural network models or architectures using fundus image dataset containing eight disease labels. A publicly available ocular disease intelligent recognition database has been used for the diagnosis of eight diseases. This ocular disease intelligent recognition database has a total of 10,000 fundus images from both eyes of 5,000 patients for the following eight diseases: healthy, diabetic retinopathy, glaucoma, cataract, age-related macular degeneration, hypertension, myopia, and others. Ocular disease classification performances were investigated by constructing three pretrained convolutional neural network architectures including VGG16, Inceptionv3, and ResNet50 models with adaptive moment optimizer. These models were implemented in Google Colab, which made the task straight-forward without spending hours installing the environment and supporting libraries. To evaluate the effectiveness of the models, the dataset was divided into 70%, 10%, and 20% for training, validation, and testing, respectively. For each classification, the training images were augmented to 10,000 fundus images. ResNet50 achieved an accuracy of 97.1%; sensitivity, 78.5%; specificity, 98.5%; and precision, 79.7%, and had the best area under the curve and final score to classify cataract (area under the curve = 0.964, final score = 0.903). By contrast, VGG16 achieved an accuracy of 96.2%; sensitivity, 56.9%; specificity, 99.2%; precision, 84.1%; area under the curve, 0.949; and final score, 0.857. These results demonstrate the ability of the pretrained convolutional neural network architectures to identify ophthalmological diseases from fundus images. ResNet50 can be a good architecture to solve problems in disease detection and classification of glaucoma, cataract, hypertension, and myopia; Inceptionv3 for age-related macular degeneration, and other disease; and VGG16 for normal and diabetic retinopathy.

JBrowse Jupyter: a Python interface to JBrowse 2.

JBrowse Jupyter is a package that aims to close the gap between Python programming and genomic visualization. Web-based genome browsers are routinely used for publishing and inspecting genome annotations. Historically they have been deployed at the end of bioinformatics pipelines, typically decoupled from the analysis itself. However, emerging technologies such as Jupyter notebooks enable a more rapid iterative cycle of development, analysis and visualization. We have developed a package that provides a Python interface to JBrowse 2's suite of embeddable components, including the primary Linear Genome View. The package enables users to quickly set up, launch and customize JBrowse views from Jupyter notebooks. In addition, users can share their data via Google's Colab notebooks, providing reproducible interactive views. JBrowse Jupyter is released under the Apache License and is available for download on PyPI. Source code and demos are available on GitHub at https://github.com/GMOD/jbrowse-jupyter.

Efficient Design Method for a Forward-Converter Transformer Based on a KNN–GRU–DNN Model

This letter proposes an efficient design method for a forward-converter transformer (FCT) with artificial intelligence (AI). Conventional FCT design is inefficient because it requires numerous repeated design processes. To solve this problem, this letter proposes FCT design by applying a K-nearest neighbors, gated recurrent unit, and deep neural network model. The design estimation accuracy of the proposed AI model was over 91% based on Google colaboratory validation. The proposed transformer design also satisfied the design requirements with less than 1,450 epochs. Once the learning process is completed, the proposed AI-based transformer design can obtain various FCT designs without further repeated training procedures. To verify the proposed design results, this letter conducted finite-element method (FEM) simulations using ANSYS Electronics Desktop 2018.2 and hardware-in-the-loop (HIL) experiments using OPAL-RT with the transformer design values resulting from the AI-based design model. According to the FEM simulations and HIL experiments, it is verified that the secondary winding induced voltage of the transformer designed by the AI-based model satisfies the design requirements.

PREDICTION OF NON-SINUSOIDAL VOLTAGE CHANGES BASED ON MACHINE LEARNING ALGORITHMS

The analysis of the harmonic composition of the voltage at the substation of the Irkutsk region shows that for all three phases, the average values of the coefficients of the harmonic component of the voltage for the harmonic spectrum from 2 to 9 exceed the corresponding GOST normally permissible values. The results of the measured values of the currents demonstrated that the harmonic level depends on the magnitude of the load. (Research purpose) The research purpose is studying the models for predicting non-sinusoidal parameters at medium-voltage power substations and nodal voltages at 0.4 kilovolt substations connected by a single grid and determining the smoothing functional dependencies necessary for calculating the grid mode. (Materials and methods) Presented data on measurements of electricity quality indicators at the Novozhilkino substation and its connections, conducted an analysis of measurement emissions. The simulation of operating modes was carried out using the Google Colaboratory package. (Results and discussion) We tested the hypothesis that the non-sinusoidal voltage coefficient at one of the connected substations can be predicted with acceptable accuracy at known voltage indicators and non-sinusoidal voltage coefficients at the head and other connected substations. Emissions were analyzed and visualizations were constructed based on measurement data at the supply substation and outgoing connections, and a correlation matrix was constructed. The predicted data of the non-sinusoidal coefficient with a scattering characteristic, which is about 0.7 for all substations of the system, were obtained experimentally. It was determined that the results of the forecast based on machine learning models correlate with the data of measurements in electrical networks. (Conclusions) Proposed a methodology for the effectiveness of forecasting non-sinusoidality based on existing machine learning models, which allow to implement automatic calculation to solve the problem, to correct models, to increase the accuracy of forecasting under uncertainty. The effectiveness of the proposed approach was shown by the example of real experimental data at substations of the central networks of the Irkutsk region.