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Image encryption has become an important issue and a new field in image processing. The Hyperchaotic image encryption scheme has advantages that make it more robust against attacks, faster encryption speed, and easy to implement. In this study, a Novel cryptographic algorithm (a new hyperchaotic map) can be utilized for color image encryption with differing formats presentation through high levels of security and efficiency. By using Hyperchaotic Image Encryption, Hyper Logistic (HIEHL), a secure image transfer method can develop. Due to the wide range of hyperchaos, our 3D-Logistic map is more ergodic and unpredictable than the Lyapunov exponent. An encryption scheme based on 3D-Logistic maps is developing to protect color images by substituting pixels in multiple directions. A chaotic sequence separates adjacent pixels from their correlations, resulting in a change in pixel value. The scheme has been tested on different color images and is effective against various threats, image encryption (HIEHL), and data loss. [ABSTRACT FROM AUTHOR]

An algorithm for detecting active objects from real-time is presented. Security cameras are common devices that especially use motion sensing technology. Nowadays, cameras with motion detectors are preferred for better protection. Motion detection technology is widely used for security purposes. Here with python programming motion detection software is used that detects human, cat and dog movement in front of the camera. Caffe model is used for image processing based motion sensing that are pretrained. The detected movement is notified to the user via the Telegram chatbot along with detected video. Images and videos can also be retrieved in it. [ABSTRACT FROM AUTHOR]

The primary objective of this research project is to utilise a K-neighborhood to locate breast cancer tumours, after which the results will be compared to those obtained through the use of a Novel Gaussian Naive Bayes method for determining the degree to which accuracy is achieved in image processing. The K-neighborhood method is used to ten pictures selected at random from a larger than 300-picture collection. The same goal underlies the process of calculating accuracy values. For the purpose of the research, images of breast cancer were analysed using K-neighborhood machines and a Novel Gaussian Naive Bayes, each with 20 different sample sizes. Following the completion of the statistical analysis, the threshold of significance for establishing the accuracy of the computations was determined to be a p value of 0.05. When it comes to the identification of breast cancer, the mean accuracy of K-neighborhood is 89.3 percent, whereas the best that Gaussian naive bayes can do is 87.5 percent. We get to the conclusion that the K-neighborhood performs much better than the Novel Gaussian Naive Bayes algorithm. [ABSTRACT FROM AUTHOR]

When the Viola-Jones machine learning algorithm is used to forecast a driver's sleepiness behaviour, it may help save people from nodding off behind the wheel and causing accidents that can leave them seriously injured or dead. To determine the predictive accuracy of the Viola-Jones machine learning system for Blink frequency, the sample size was varied from 100 times. The sensitivity of Viola-Jones is much higher. Viols Jones has a higher blink frequency percentage than HOG (86.14 percent) (75.84 percent). The correlation between Voila-Jones and HOGt was significant (p=0.000). The Viola-Jones model improves the sensitivity % for forecasting driver fatigue. If the algorithm determines that the driver is, in fact, sleepy, an alert will sound. [ABSTRACT FROM AUTHOR]

Water, in its many forms, is essential to the survival of all forms of life. The recommended maximum levels for water contaminants are specified in the drinking water recommendations. Taking into account two classes, for example, the cutting-edge Support vector regression technique and the tried-and-true Random forest approach. A dataset of 1993 samples has been used to construct and test the methods. Each group underwent a total of 5 iterations with a larger sample size to ensure precision. About 80% of the time, G-power is utilised. The experiments showed that the support vector regression method improved the accuracy of water quality monitoring by a mean of.9440 percentage points, while the Random forest approach improved accuracy by.8700 percentage points. Using independent samples t-tests, we find a statistically significant difference in accuracy between the two methods, with a p-value of 0.885. The purpose of this research is to apply cutting-edge methods to unique image processing classifiers in order to enhance the precision of water quality monitoring. The novel support vector regression approach outperforms the conventional Random forest technique, as shown by the comparative findings. [ABSTRACT FROM AUTHOR]

The purpose of this study is to improve the accuracy of biometric recognition systems by utilising innovative wavelet transform techniques as opposed to the more traditional Fourier Transform Technique. There are going to be two different teams working on this project. The Fourier Transform Technique is used for group 1, and the Novel Wavelet Transform Technique is used for group 2. Both groups have a sample size of 50. A matrix biometric database is combed through to obtain a sample of one hundred different scanned fingerprints. Using the MATLAB (R2017a) software tool, the values of accuracy are calculated to identify the performance of the novel wavelet transform technique. This is done with the help of image processing and also with image processing. Both the false acceptance ratio and the accuracy of the novel wavelet transform technique were found to be 2% and 82%, respectively, while those values for the fourier transform technique were found to be 6% and 76%, respectively. This value, 0.8, is derived from the G power. When compared to the fourier transform technique, it was found that the innovative wavelet transform method performed significantly better. The significance value p is greater than 0.05 but less than 0. In terms of accuracy, it would appear that the recently developed wavelet transform technique is a significant improvement upon its predecessor, the fourier transform technique. [ABSTRACT FROM AUTHOR]

One of the areas of computer vision is superpixel segmentation, and the widespread use of superpixels in a wide range of practical applications shows that it has unique properties. A superpixel is created by combining pixels with similar content, which significant reduce the computational complexity of the algorithm at the next stages of image processing. After the superpixel is built, it is possible to solve problems such as classification, reduction of image dimensions, construction of spectral combination, selection of a certain channel, noise reduction, and change detection based on the application of superpixel to spectral images. This paper presents an analysis of SLIC, SEEDS, LSC, DFIC, AHP, DRW, WSS, BACA, and superpixel segmentation approaches on remote sensing images. The analysis of approaches was considered according to the characteristics of the color model, scale, regularity, connection, smoothness, texture, and boundary strength. Approaches were evaluated using the criteria of Undersegmentation error, Achievable segmentation accuracy, and Boundary Recall. The study revealed that superpixel segmentation techniques that employ all of the spectral image channels have not received enough attention; instead, they are typically applied using PCA to shrink the size or convert them to color models. Additionally, it is important to investigate the issue of applying deep learning techniques to the implementation of superpixel segmentation in remotely sensing spectral images. [ABSTRACT FROM AUTHOR]

The main is one of the main directions and goal of image reconstruction technology a predetermined quality, as in improving the quality of an image and restoring its blurred or degraded parts. Overlapping and blocking some part of the image, image quality improvement basically, image acquisition is a subjective phenomenon, and image restoration is an objective phenomenon. Restoration, i.e. restoration and restoration of the degraded image using existing knowledge about the degradation phenomenon, is important. Therefore, restoration methods focus on modeling the distortion and applying an inverse process to restore the original image. This study examines linear, space-invariant recovery models applied to various recovery situations. It also covers the basic methods of image reconstruction from shadows or projections and their use in medical computed tomography, which is an effective image processing tool, especially commercial and useful in biotechnology and medicine. This research paper presents the analysis of Gaussian, Rayleigh, Erlang, Exponential, Uniform, Salt-and-Pepper, Periodic, Estimating noise and their main parameters. [ABSTRACT FROM AUTHOR]

Given the significant time investment required for manual execution and oversight of various agricultural tasks, the adoption of modern technologies emerges as the most efficient solution to address this issue. Nowadays, the interest in the images taken by unmanned aerial vehicles (drones) in the field of agriculture is also increasing. In contemporary times, there is a growing fascination with images captured by unmanned aerial vehicles (drones) within the agricultural sector. However, in certain instances, the quality of these drone-captured images may not meet the stipulated criteria necessary for performing specific tasks. One of the common problems with images is low image contrast. This situation complicates the identification of objects, distinguishing important elements such as agricultural crops and machinery. This study is devoted to determining the optimal pair of contrast enhancement method or sequence of methods and reference contrast evaluation criteria, and a new approach for automating image processing is proposed. [ABSTRACT FROM AUTHOR]

Road damages are extremely dangerous and unsafe, negatively impacting any type of vehicle, driving safety, business comfort, and countless other factors. The traditional road pothole inspection technique is costly, unsafe, dull, and awkward. Additionally, the road inspection result is good and private; this is fully based on the officer's specific expertise. As a result, if automated pothole detection is required. An investigation report initially considers the most typical kinds of road damage. Define how seeing systems operate for 2-D and 3-D road data access, which is accomplished by any cameras and sensors. An extensive overview of state-of-the-art [SoTA] computer vision techniques, which include first 2-D image processing, second 3-D point modeling and segmentation, and third machine's deep literacy, designed for road pothole detection. These findings indicate that employing unresisting eyesight for this goal is not an issue, since images obtained by standard cameras are used. [ABSTRACT FROM AUTHOR]

The paper presents the results of studies to determine the dependence of porosity of gas-thermal coatings on laser radiation parameters. On the basis of analytical dependences the effect of laser radiation on the surface layers of gas-thermal coating is described. Its discontinuity caused by the presence of open and closed pores was taken into account. It is shown that to reduce the porosity of the coating under the influence of laser radiation it is necessary to create on the surface of the coating the temperature in the interval "melting temperature - boiling temperature". Increase of temperature caused by increase of radiation intensity above the specified range leads to evaporation of surface layers of the coating up to its complete removal in the zone of thermal influence. Laser treatment of pre-sprayed gas-thermal coatings based on and non-metallic (Al2O3) components was carried out in the course of experimental studies. Quantitative estimation of porosity change was performed with the help of image processing program developed in Visual Studio 2008 environment by means of pixel-by-pixel comparison of areas occupied by pores and coating material in the microstructure image. It was found that the laser radiation exposure leads to a decrease in the average porosity of gas-thermal coatings from 24.5 % in the initial state to 15-18 % after laser treatment. [ABSTRACT FROM AUTHOR]

Skin Cancer is one of the most common types of disease in the world, including Indonesia. This disease is caused by food that can be toxic to the human body and the effects of global warming. Especially melanoma, this type of skin cancer that occurs in melanocyte cells is a very serious threat. In the United States, although melanoma is only 5% of all cases of skin cancer that occur, melanoma has been the cause of death in 75% cases of death from skin cancer in the country. Melanoma has a similar shape as mole, birthmark or beauty sign that is often ignored. To recognize the presence of melanoma early on, this study uses the method of deep convolutional neural network to classify skin cancers that occur in melanocytes, where melanoma grows. The deep learning method used has good accuracy and good use in image recognition. Dermoscopy image of skin cancer is used as input for image processing. Before entering the classification stage, the image is processed with the image processing Scaling and contrast enhancement. After testing phase in this study, it concludes that the proposed method has the ability to classify skin cancer that occurs in melanocytes quite well with an accuracy of 93.3%. [ABSTRACT FROM AUTHOR]

Gastritis is a disease that occurs in the stomach and can be dangerous if not treated quickly. Gastritis can affect anyone, young or old. Therefore, a system is needed to identify early gastritis identification so that it can help the community to find out early on their gastritis. The identification method used in this study is the Radial Basis Function Neural Network with several image processing techniques. Tongue image is used as input image for image processing. Prior to the initial identification, the image preprocessing process is carried out, namely cropping, resizing, brightness, image feature extraction using color extraction using Hue Saturation Value (HSV). The dataset used in this study are limited to 150 tongue images and resulted in the ability to identify early gastritis with an accuracy of 93.3% for 50 testing images. [ABSTRACT FROM AUTHOR]

This study investigates the statistical characteristics of liquid film thickness fluctuations during countercurrent flow limitation on a complex geometry containing a horizontal and inclined pipe which are connected by an elbow. The fluctuation data are obtained by image processing technique which was applied towards high-quality images captured by a high-speed video recording. The result indicates that the thickening of the liquid triggers the liquid blockage that initiates flooding at the upper edge of the hydraulic jump. Moreover, the stochastic analysis reveals that the obtained data imply consistence trends across whole water discharge. Furthermore, it also shows a good agreement to the visual observations. Meanwhile, the wavelet analysis can distinguish energy fluctuations under different water discharges. [ABSTRACT FROM AUTHOR]

The solution of the problem of text recognition on raster navigation maps is considered. The implementation of image processing by the method of colorimetric conversion to grayscale based on the Otzu algorithm for text recognition on navigation maps is described. The input data are files containing training data for the neural network (a set of images of individual symbols and a text document containing training data labels) and files of raster images of navigation maps without tilt. The output data is a vector image containing the results of recognition of text labels and a text file containing all recognized labels. The experimental part of the work was carried out for three types of neural networks (three-layer, five-layer and six-layer) trained on three data sets. The results of the study allow us to conclude about the combination of the number of neural network layers used and the number of data types in the training set. [ABSTRACT FROM AUTHOR]

Corona virus disease (COVID-19) was responsible for inevitable chaos in the human life due to its inherent nature of high contagious across the globe. This very reason also hindered the physicians and healthcare support systems to provide in-time medical aid to the suffering patients.The diagnosis of COVID-19 using clinical information is the prime source for detection and treatment. However, computerised tomography (CT) and Chest X-ray (CXR) images have found useful for screening purposes due to the successful usage of image based analysis by physicians which was aided by image processing based algorithms.Automated means of screening, diagnosis and prognostic studies using CT and CXR with the help of comprehensive image processing pipelines could invariably augment the efficiency in the respective tasks. The main objective of this study is to implement multi-class classification of COVID-19, viral-pneumonia (VP) and lung opacity (LO) using threshold-based segmented CXR images further transformed using wavelet transform (WT)and Laws based texture representation. Machine learning (ML) based implementation and comparison was carried using Random Forest (RF) and Logistic regression (LR) techniques with of the WT and Laws features individually as well as with combined feature vectors. The ML based implementation was aimed to classify the CXR image based on the individual or combined features into COVID-19, VP or LO pathology. The validation of the ML techniques was further carried using confusion matrix and other validation measures. The performance of the RF classifier was superior with WT features than Laws texture features on individual category of features than LR technique. Amarginal improvement was observed in the classifier performance when WT and Laws based features were combined. [ABSTRACT FROM AUTHOR]

A topic that evokes a large base for assorted studies, across numerous countries is soil classification. Recently, the world recorded an exorbitant rise in human population, the denouement is a higher demand for food and shelter. Both these factors directly demand exceptional study in geo technology, if not, a more reliable and faster method to do so. Conventional strategies taken by farmers are insufficient to meet the skyrocketing demands, and as a result, they must impede soil cultivation. This leads to the requirement to remain cognizant of the optimum soil type for a certain crop to maximize agricultural production. Several laboratory and field methods for classifying soil exist, however, these have constraints namely time and labour sine qua non. A need for a software-based soil categorization modus operandi that will assist humankind from a range of engineers to farmers, in reducing the time deviation is sought. This paper discusses apropos software, that allows soil classification to be achieved more efficiently. The functioning includes image processing and Computer vision-based soil classification methodologies, embodying the customary image assessing algorithms based on parameters, namely texture, colour, and particle size. [ABSTRACT FROM AUTHOR]

Early diagnosis of brain tumors depends on their detection and treatment of brain-related disorders. When it comes to diagnosing brain tumors, for example, deep learning models have shown remarkable success in the field of medical picture processing in recent years. The implementation of two cutting-edge convolutional neural network designs, DenseNet169 and DenseNet201, for brain tumor identification is the main emphasis of this study. A sizable dataset of magnetic resonance imaging (MRI) scans, including different types and sizes of brain tumors, was used to train the DenseNet169 and DenseNet201 models. These models' extensive connections enable data exchange and feature reuse, improving performance and enhancing tumor localization accuracy. The trained models are assessed using a different testing dataset, and their performance indicators, such as accuracy and loss, are examined. Both models' effectiveness is shown by the findings. DenseNet201 outperformed Dense 169 ResNet 50 & VGG19, with test accuracy of 88.58, train accuracy of 95.98, train loss of 10.36, and test loss of 33.59. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most common types of cancer among women. Early diagnosis of breast cancer has vital importance to prevent unexpected losses. A worldwide effort has been made to tackle early detection challenge. Dynamic contrast-enhanced magnetic resonance imaging is a superior imaging system that improves breast cancer diagnosis quality of physicians. Computer Aided Diagnosis systems are used as a complementary tool to improve breast cancer diagnosis. In last decades, various computer aided diagnosis systems have been proposed. However, the state-of-the-art deep learning-based approaches have started to overcome conventional medical image processing methods. In this study, we aimed to detect malignant breast lesions from open access dynamic contrast-enhanced magnetic resonance imagery dataset using most recent YOLOv7 deep learning architecture. 2400 images have been used for training (80%) and testing (20%) of the network. The metrics calculated with the test dataset are 98.54%, 96.42% and 84.40% for mAP@0.50 IoU, mAP@0.75 IoU and mAP, respectively. The results show that YOLOv7 architecture is capable to detect malignant breast lesions from dynamic contrast-enhanced magnetic resonance images efficiently. [ABSTRACT FROM AUTHOR]

Positioning within indoor environments is one of the major challenges facing many different applications. It has become necessary to find algorithms and systems capable of locating a specific object within an environment that contains many objects. In this paper, the researchers have made a detailed study on many methods of positioning within different environments. Some methods rely on digital images and others rely on identification systems by radio waves and other methods. Each system has a number of advantages that qualify it to be the best choice in certain applications. On the other hand, there are drawbacks to each system that prevent it from working within certain environments. Clear comparisons are made in this paper showing the different systems with the pros and cons of each one. [ABSTRACT FROM AUTHOR]

The detection of image edges is a major challenge in image processing and computer vision, since it needs to dispense of unnecessary pixels while preserving significant ones which indicate the image's structure. An optimized algorithm for image edge detection is introduced in this study, which uses a Genetic Optimization (GO) Algorithm to seek out sources of the maximum pixel value to optimally allocate the image edges. The algorithm simulates the evaluation of various values and consists of three operations: selection, crossover operations, and thresholding the optimized value. The community is made up of these evaluations which are conducted at each stage to select the fittest individuals and eliminate the less fit ones. By repeating this process, GO eventually identifies the image edges and eliminates non-edge pixels. The results of the study show that the proposed algorithm successfully addresses the challenges associated with image edge detection. The limitations of the research are also discussed, and potential areas for future research are suggested. This algorithm is expected to be useful for solving complex problems. [ABSTRACT FROM AUTHOR]

Cervical cancer is caused by the growth of abnormal cells in the cervix's lining. Human papillomavirus (HPV), a sexually transmitted infection, plays a role in most cervical cancers. Therefore, cervical cancer can be avoided by having regular screenings and being vaccinated against HPV infection. The term pap-smear refers to human cell samples stained using the Papanicolaou method. The Papanicolaou method is used to detect precancerous cell changes before they become invasive cancer. The smear cell image is composed of a nucleus and cytoplasm. Cancer prevalence is determined by the shape and structure of the nucleus. Therefore, the segmentation of the nucleus is an essential step in detecting cancer. However, overlapping, poor contrast, uneven staining, and other factors make cervical nucleus segmentation difficult. This paper proposes a new segmentation method for the cervical nucleus using digital image processing. Our proposed method used a median filter to remove noise and a non-linear contrast stretching to enhance the Pap smear images. Then, we used Bradley thresholding for the segmented cervical nucleus. The main impact of this paper will assist doctors in diagnosing cervical cancer based on Pap smear images and increase the accuracy percentages compared to the conventional method. [ABSTRACT FROM AUTHOR]