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Satellite images mainly utilized in the events of a natural disaster management, identifying geographical information, viz land cover classes namely, buildings, roads, vegetation, water, agriculture land, crop types, plants, bare ground, cities, atmosphere conditions. Machine Learning (ML) approaches have been utilized effectively to develop a model for classification, detection, and segmentation tasks. Therefore, Satellite image processing and analysis purpose, ML techniques plays vital role and remotely sensed data become essential while training the model. The aim of this study is to investigate the various of ML techniques in satellite image analysis. However, to predict the various events in advance across the globe, it is necessary to focus more on remote sensed data and data processing techniques for accurate classification. Even though remote sensing quality has been increased and artificial intelligence solutions are equally increased. This paper addressed various types of advanced ML techniques utilized in the classification and assessment of satellite images and used to track the earthquakes, faulting, landslides, floodings, wildfire, and hazards associated with the stated activities. Still there is a gap and interference in the approaches and it is important to fill the gap by thorough review of recent classification approaches. In this connection it is necessary to look in depth to the state-of-the-art ML techniques of satellite image processing. [ABSTRACT FROM AUTHOR]

The presence of atypical lymphocyte cells in the blood smear of a patient suspected of dengue is a specific sign that the person concerned is positive for an attack of dengue fever. In general, the counting of atypic lymphocyte cells is done manually under a microscope, but this method has consequences for counting errors and a longer time. In digital image processing, we can use the improved counting morphology algoritm to count atypic lymphocytes faster and more accurately than manual calculations. The improved counting morphology algorithm uses the concept of adjacency 8 as the basis for calculating atypical lymphocytes by memorizing the neighbor next to it, if the color next to it is the same color as the reference point, it is said that the pixel points are neighbors or cognate with the reference pixel point and are counted as the same object. But on the contrary, if a pixel point is not related to its neighbors, it means that the pixel point is a separator or a different object. The results of this tool have an accuracy of 100%. [ABSTRACT FROM AUTHOR]

Anxiety is an element of existence it is a disagreeable condition of expressive stimulation that public understanding in circumstances similar to operational for extended hours in facade of electronic gadgets. Electronic gadgets like laptops, mobiles and computers have grow to be a approach of existence, to a great extent living is worn-out on the Electronic gadgets moreover consequently we are as a result additional exaggerated through the ups and downs that they reason us. One cannot immediately totally circumvent their toil on Electronic gadgets although one knows how to be as a minimum authority his/her handling while being worried regarding him being stressed out at one position of instance. Scrutinize the expressive condition of a human being who is functioning in frontage of a Electronic gadgets for extended period is fundamental for the wellbeing of a human being. In this toil a instantaneous non-intrusive videocassette records are taken by capturing the community, which notice the expressive condition of a person by investigating the facial appearance. We identify a human being feeling in every recorded video capture structure and the conclusion on the strain intensity is completed in chronological times of the videocassette imprisoned. We utilize a method that permits us to educate a representation and investigate differentiations in forecasting these characteristics. The package Theano is a python programming construction which intends at improving both the completing moment in addition to improvement point of the Linear Regression representation that is utilized in the perspective as a DL algorithm. The investigational outcomes demonstrate that the enlarged organization is well on information with the standard representation of every era. [ABSTRACT FROM AUTHOR]

Advancements in deep learning and computer vision have resulted in a surplus of counterfeit facial content that is incredibly realistic in appearance, which is controlled by artificial intelligence., for example, Deep Fake or Face2Face that control facial personalities or on the other hand articulations. Counterfeit appearances made utilizing Generative Adversarial Networks (GANs) are becoming increasingly sophisticated and difficult to distinguish. The synthetic faces were designed primarily for amusement purposes, but their abuse has created emotional harm. For example, a few celebrities have fallen victim to Deep Fake's false erotic entertainment. Concerns are also growing regarding false political debate recordings generated by Face2Face. Building models that can identify fake faces in the media is essential for maintaining personal, societal, political, and international security. The purpose of this paper is to suggest a novel way to identify these counterfeits on the internet using Convolutional Neural Networks (CNN) and image processing techniques. We first used Contrast Limited Adaptive Histogram Equalization (CLAHE) and Complex Shearlet-based edge detection on the dataset and then used the resultant images obtained to train a proposed CNN to categorize fake and real personalities and separate them. [ABSTRACT FROM AUTHOR]

Medical imaging has seen substantial improvements not only in image procurement but also in terms of interpretational methodologies. The practice of using technologies such as image processing and artificial intelligence has shown remarkable results in the detection, classification, and localization of human bone fractures. Breakage or cracks in the bone occur when an external force is applied to a bone that is significantly larger than the bone's tolerance, which may lead to dislocation requiring extensive treatment. However, manual examination by experts is a complex and arduous process with a high chance of false detection. Many approaches under Artificial Intelligence and its subsets are proposed to facilitate fracture diagnosis and prevent misinterpretation, including Deep Learning and Convolutional Neural Networks (CNN). This paper is a comprehensive study of the various techniques and models designed using image processing approaches and the advances in machine learning and Deep Learning that have demonstrated improved performance in the precise detection of fractures in human bones. [ABSTRACT FROM AUTHOR]

Image processing is the process of manipulating images by visualizing, recognizing, sharpening of image features. Digital image processing deals with enhancing quality of image by using image processing techniques. Real time object tracking methods are studied for effective object identification. The algorithms for single and multiple object detection, color detection and face detection of an image are discussed. This article provides a comparative study of various object detection algorithms for video surveillance. The advantages and disadvantages of the system are compared and contrasted. From the results, it is evident that the object tracking algorithms provide high accuracy and precision by capturing the video frame and processing it frame-by-frame. It is used in various applications such as health care, forensics and security systems. [ABSTRACT FROM AUTHOR]

Image compression techniques have become a hot topic due to the increasing growth of multimedia applications. This paper presents a ground-breaking full quadtree range block partition-based search-less fractal encoding algorithm that can efficiently encode range blocks or even individual pixels. This method, which may also be applied to high-quality image compression, solves both issues. Experimental results show that the technique outperforms traditional search-based and search-less fractal algorithms at low and high compression rates, thus improving both compression ratio and reconstructed image quality. Another advantage of the approach is its speedy encoding speed because it does not require a search process. When compared to traditional fractal algorithms, this method encrypts data much faster. [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]

Cervical cancer is a form of cancer that develops in the cells of the cervix, which links the uterus to the vagina. Cervical cancer can often be prevented by attending the cervical screening, which aims to find and treat changes to cells before they turn into cancer. Pap smear images are a more popular technique for screening cervical cancer. The nucleus and cytoplasm are present in the smear cell images. The spread of cancer is determined by the shape and structure of the nucleus. Thus, nucleus segmentation is an important step in cancer detection. 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. In our proposed method, image processing techniques are employed to segment the nucleus. This method mainly compared the performance of using Red Green Blue (RGB) channels and Hue, Saturation, and Value (HSV). The best two channels from each two-colored image representation are compared, where the V channel is the best among H and S channels. On the other hand, G chancel is the best among the G and B channels. Therefore, the best two channels are compared, and the segmentation using the G channel is the best among all, with a sensitivity of 94.3%, specificity of 94.6% and precision of 88.3%. The main impact of this paper will be to assist doctors in diagnosing cervical cancer-based segmentation of the nucleus in Pap smear images. [ABSTRACT FROM AUTHOR]

This work presents an automated and novel system for cricket commentary generation by the introduction of event driven approach and image captioning features. The presented system uses artificial intelligence and machine learning (ML) based methods to investigate real-time match data and generate real-time commentary by Image Captioning technique. For this purpose, deep neural network-based models and digital image processing techniques are used to detect the significant moments in the real-time match and generate commentary based on these real-time match events. The proposed method has been assessed using a dataset of 2 hours live cricket matches of India and England. After processing the match video, it has been observed that the developed model is successfully able to generate high-quality real-time commentary with a significant amount of accuracy. By commissioning leading-edge deep neural network-based model, the developed model determines the fitness to generate subtitles that are not only precise but also contextually appropriate, and efficiently apprehending the essence of the input frames. [ABSTRACT FROM AUTHOR]

Pothole detection is a challenging task as it involves inhomogeneity in intensity, complex topologies, contrast less, and noise rich background. Still, it's a very important task concerning the maintenance of roads. Potholes are being detected using computer vision which generally involves analysis of a two-dimensional image of the road or modeling of the road surface. Provided both these methods are independently used. The accuracy of this method of pothole detection is far from the top point. These bring the usage of digital image processing which develops the detection of potholes with at most accuracy. Digital Image processing provides strong receptivity, usage of huge actions, and broad benefits. In this paper, we propose a system that supports digital image processing techniques of high efficiency to result in consistent and accurate detection of potholes. This project studies an intelligent digital industrial pothole detection mechanism to increase human safety, detection capability and decrease the factor of risk. Firstly, the sources of images from the road are pre-processed to remove noise and other irrelevant features. Then a training model was developed to train the system to classify between path holes and normal cracks using convolution neural networks and MATLAB. Also, an IoT device is used in this system which updates the detected potholes on the road to the respective web server along with the location coordinates, as a part to notify a complaint to check. This system works on providing a better pothole detection technique and also updating the same to the concerned. [ABSTRACT FROM AUTHOR]

The diverse motifs of Lampung Batik require an automated identification process using digital image processing technology. This research aims to identify Lampung Batik motifs by utilizing computer technology, particularly in digital image processing, to assist in recognizing these motifs. In this era of digitization, there are various object recognition methods, one of which is image recognition that utilizes GLCM feature extraction to obtain the characteristics of Lampung Batik motifs. After the feature extraction process on Lampung Batik images is completed, this data is used for classification. This study employs the K-Nearest Neighbor and Naïve Bayes classification methods, with a total dataset consisting of 300 images, comprising 150 images of "siger" motifs and 150 images of "sembagi" motifs. In testing with the K-Nearest Neighbor method, the highest accuracy is achieved at K = 3, approximately 77% for the training data and 81% for the testing data. Meanwhile, in testing with the Naïve Bayes method, an accuracy of around 96% is attained for both the training and testing data. [ABSTRACT FROM AUTHOR]

This study aims to detect myopia using digital image processing technology, which can be detected by pupil size. The methods of artificial neural networks, namely the Backpropagation and Convolution Neural Network (CNN) methods are used to analyze the digital images of eyes. The different preparation of data input to be fit for these two methods has been conducted. The test results using Backpropagation ANN give the highest accuracy 71.66%. The test results of the CNN model produce an accuracy value of 77.14%. [ABSTRACT FROM AUTHOR]

The paper introduces a novel watermarking scheme to enhance robustness and integrity in medical images using the Knight's Tour algorithm. Medical image digitization and sharing necessitate robust techniques for data security. Existing watermarking methods often lack resilience against attacks and maintain imperceptibility. To address these issues, the proposed scheme employs the Knight's Tour algorithm to determine the optimal pixel locations for watermark embedding. Extensive evaluations demonstrate its effectiveness against geometric attacks, specifically image rotation and scaling. The study's significance lies in providing a reliable solution for safeguarding medical image data, ensuring authenticity, and bolstering trust in healthcare applications. Moreover, the scheme's adaptability extends its significance to multimedia data protection and content authentication. [ABSTRACT FROM AUTHOR]

Segregation is a phenomenon of separating small and large fractions in a mixture, resulting in the presence of coarse aggregate and fine aggregate in an uneven mixture. As a result of the non-uniform distribution, the possibility of potholes, raveling, and cracks in the asphalt of the highway is very likely to occur. Therefore, we need to be able to take preventive measures as a form of minimizing the possibility of this phenomenon occurring. Segregation in asphalt is generally detected through manual visual inspection. However, using the assessment method obtained will tend to choose and take a long time. Thus, this research was conducted to provide a new solution to detect segregation areas in a more credible, faster, and economical way. This solution utilizes digital image processing methods that are still rarely used. In the process, this method will be implemented together with the Support Vector Machine method. Then, the variable that will be used as the main focus is the standard deviation. In this study, we will test the classification of segregated and non-segregated areas on the asphalt road environment at the University of Indonesia. [ABSTRACT FROM AUTHOR]