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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]

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]

Embedded applications have begun to reap the benefits of embedded vision in recent years. The proliferation of high-performance computers, the maturing of deep learning algorithms, and vision is being added to electronic devices like integrated medical applications due to image processing advances. However, due to the high amount of CPU resources necessary to process even a single image to recognize an object, it is especially challenging to integrate image processing in embedded systems. Deep learning approaches, when deployed and validated on data sets matched to the specific task at hand, may produce better results. We propose and investigate the use of deep learning to improve the image-processing architecture of embedded vision systems in this study. Deep learning algorithm testing on embedded vision yielded encouraging results. [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]

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]

Driver drowsiness and fatigue are leading causes of road accidents, particularly due to drivers having to operate vehicles for long hours under challenging physical and adverse weather conditions. Despite extensive research on subjective, physiological, and vehicle-based detection methods, recent reports still highlight drowsiness and fatigue as significant contributors to accidents and injuries on highways. However, existing drowsiness detection models are often intrusive or expensive. To address these limitations, we propose a low-cost, non-intrusive drowsiness detection model based on facial features, leveraging principles of image processing and machine learning. Our research involves a multi-step approach. Initially, image frames are extracted and subjected to image processing techniques. The images are subsequently passed through a Dlib classifier to detect and localize 68 facial landmarks, including the mouth and eyes. From the localized eye landmarks, salient features such as eye aspect ratio (EAR), pupil circularity (PUC), and eyelid closure over the eye pupil are extracted. To classify the driver's state, these collective features are inputted into various machine learning classifiers. We conducted experiments using the National Tsing Hua University Computer Vision Lab dataset, implementing the proposed approach in the Python OpenCV environment. Notably, the KNN algorithm demonstrated the highest accuracy when the test data was evaluated, yielding a paramount accuracy of 78% on the NTHU DDD dataset. In conclusion, the non-intrusive facial features-based driver drowsiness detection model offers a cost-effective solution to address the pervasive problem of drowsiness-related accidents. The promising results obtained through the utilization of machine learning techniques underscore the potential for practical implementation. [ABSTRACT FROM AUTHOR]

This research aims to explore the potential of various facial image types in diagnosing Autism Spectrum Disorder (ASD) through the application of deep learning neural networks. It delves into how deep learning algorithms perform with different facial image modalities, especially 2D and 3D, while addressing specific challenges associated with each. The methodology includes training deep learning models on distinct datasets and conducting an in-depth analysis of their accuracy and performance metrics. Significantly, the ResNet50V2 model recorded a 96.9% accuracy rate on the 2D dataset, and the Xception model achieved an 84.4% accuracy rate on the 3D dataset. These findings emphasize the strong capability of deep learning neural networks in making accurate ASD diagnoses from facial images. Nonetheless, the research reveals a stronger proficiency in handling 2D over 3D images, suggesting a need for more comprehensive 3D dataset training to improve three-dimensional image processing. Through evaluating the efficacy of different image modalities, this investigation enriches the field's knowledge base, highlights the necessity for robust dataset development, and charts a course for future studies to advance the precision and practicality of ASD diagnosis via deep learning approaches. [ABSTRACT FROM AUTHOR]

The main objective of image compression in the digital era is to reduce the size, of irrelevant and redundant data in digital images. this is able to keep and transfer the image in an efficient manner. In the present digital world, multimedia files have large sizes and consume a lot of space, and are also very difficult to transmit. Image compression plays a prominent role to reduce the size of the images and transmit such compressed data in a permitted time duration. This paper addresses image compression using machine learning methods. Machine learning is one of the subsets in artificial intelligence, in recent days machine learning plays prominent role in image processing. Using machine learning algorithms in image compression provides better-quality images and improves the different performance metrics. This paper attempts a study various machine learning algorithms as well as image compression techniques. [ABSTRACT FROM AUTHOR]

Various object detection models are presented in this paper. Definition of object is well defined in order to understand object detection models. Now-a-days the object detection which is computer vision and image processing related technology is widely used. Applications of object detections are infinite as it can be used all over like objects tracking, surveillance of videos, face recognition, detection of anomaly, counting of people, and the records still continue to many more. This paper compares various object detection algorithms the basic splitting is based on machine learning based and deep learning based. Fundamental objective of the paper focuseson Single stage object detectors (SSD) like You Only Look Once (YOLO) and Two stage object detectors as RCNN, Fast RCNN, etc. This paper's main goal is to highlight such object detection models and their use in various fields. [ABSTRACT FROM AUTHOR]

The data that was acquired by the globe Prosperity Affiliation reveals that the worldwide pandemic of the Covid virus has had a substantial impact on the globe and has now caused damage to more than eight million people all over the various regions of the world. Two of the most current health precautions are advised to be done immediately in order to avoid the spread of the contamination. The first is to wear facial coverings, and the second is to adopt safe social isolation practices. Both of these measures are recommended to be implemented immediately. We provide a productive strategy that is focussed on the continuous computerised observation of individuals in order to discriminate face coverings. This approach is based on the combination of TensorFlow and PC vision. The purpose of this action is to create a secure setting that makes a positive contribution to the overall health and happiness of the inhabitants of the community. Python is provided with an interpretation for the purpose of face mask detection as soon as the image processing system comes to the realisation that a breach has occurred. After then, an infrared sensor is kept in place in order to ascertain the total number of individuals present, supposing that the face covering is identified and that the door will be open. Under the premise that the number of people who are permitted to be inside the room is greater than the threshold, the door will not open. [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]

Blind Image Quality Assessment (BIQA) is crucial for various image processing applications, including image denoising, transmission evaluation, optimization, watermarking, and situations where a reference image is unavailable. However, existing state-of-the-art Image Quality Assessment (IQA) metrics are often specific to certain types of distortion and fail to align with human perception. To address this issue, our research study proposes a novel approach that incorporates perceptual-based features and utilizes a pooling algorithm based on Extreme Learning Machine (ELM). By considering human visual characteristics and the impact on image content, we aim to mimic human perception, which can detect noticeable differences at specific frequency ranges, akin to neurons. The work is divided into three phases. In the first phase, we derive perceptual features using lifting wavelet, focusing on texture, edge, and contrast components. Subsequently, in the second phase, these features are trained to generate output for pooling using Extreme Learning Machine (ELM). The pooling strategy of ELM is chosen due to its ability to overcome limitations found in previous pooling techniques like Neural Networks (NNs) and Support Vector Regression (SVR). This approach enables us to evaluate the quality score of images accurately, benefiting from ELM's superior learning accuracy and faster learning speed. The third phase involves performance evaluation, including statistical analysis for algorithm validation and a comparison with existing BIQA methods using MATLAB software. We verify our proposed approach on diverse image databases containing various distortion types, aiming to create a general-purpose BIQA solution. The outcomes of this work will have significant implications in several image processing applications, such as optimizing image enhancement for medical purposes like tumor or cancer detection, image watermarking for security applications, image coding and compression, and image forensic analysis. In biodiversity monitoring, image enhancement plays a crucial role in tracking and data collection. [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]

India is one of the hugest rice cultivating country in the world. For thousands of years rice is a most major food, consequently every mankind depends on it directly and indirectly. But the Main hindrance in rice production is rice leaf diseases. It greatly reduces the production rate. Rice leaf diseases have specific symptoms that cannot be easily identified by usual human's eye. Using one of the image processing techniques, the rice leaf diseases are detected efficiently. In order to detect the diseased image Median Filter, K-means Clustering, GLCM and SVM classifier algorithms were proposed. Accuracy, Precision and ROC of SVM classifier were calculated as performance metrices. Around 97±2 % accuracy was achieved in this method. This paper discusses the efficient methodology for rice leaf disease detection. By using proposed method diseased leaves are identified at the early stage, which can aid farmers in preventing further damage to their crops. [ABSTRACT FROM AUTHOR]

The aim of this paper is to carry out work those results in a good friend to farmers in every way. The work primarily recommends a high-yielding crop to the farmer based on regional soil data and the farmer's requirements. The job entails analysing a database of different types of soil from a specific area. To improve the analysis' efficiency, a machine learning method called linear regression is used. The study also implements disease detection in crop leaves using image processing techniques and provides remedy/solutions to farmers with diseased leaves, which promises to be a preventative measure in the future. The study carried out has an effect on various cultivation methods for various crops, assisting farmers as a "SAHA PATI" for this work. This service assists farmers in determining the best method of cultivation based on climatic and geographical factors. The final section of the Paper introduces a programme that provides current market prices for various crops by connecting to a government website that is updated on a regular basis. This service assists farmers in avoiding being duped by business intermediaries. The overall work done with the agriculture-based component turns out to be the best application for the current situation. [ABSTRACT FROM AUTHOR]

On this paper there is a study of different type of parallel prefix adders. As we know that adders are the basic building block of any Digital Circuits. Now a Days high speed circuits are used for various applications like communication circuits, hardware for Machine learning algorithms, Image processing algorithms etc. Various types of parallel prefix adders are used for different application. For Doing the Study of parallel Prefix adders we use Verilog HDL for the function description and then mapped the functionality on FPGA technology library using ISE14.5. The experimental results based on the design and constraints explains that the performance of Ladner fischner adder is better than Kogge-Stone. [ABSTRACT FROM AUTHOR]

The identification and diagnosis of plant diseases is one of the key factors in determining crop losses in crop production and agriculture. An analytical study of plant diseases is the study of any visible points in any part of a plant that help us to distinguish between two plants, technically or any dots or shades of color. Crop sustainability is one of the key factors in agricultural development. Diagnosis of plant disease requires a lot of work and expertise, a lot of knowledge in the field of plants, and research to diagnose those diseases. Therefore, image processing is used to differentiate plant diseases. Disease diagnosis follows methods for image acquisition, image removal, image classification, and pre-image processing. Leaf color, amount of damage to leaves, leaf area of the unhealthy plant is used for sorting. In this case, we reviewed the learning algorithms of the various machines to identify the leaf diseases of different plants and to determine the best accuracy. [ABSTRACT FROM AUTHOR]

Pneumonia is a common respiratory ailment impacting millions worldwide. It stems from bacterial, viral, or fungal infections and can escalate into a serious, life-threatening condition if not promptly addressed. As per the World Health Organization (WHO), pneumonia stands as the leading cause of death among children under five globally, contributing to nearly 15% of all fatalities in this age group. Moreover, pneumonia accounts for 15% of all hospitalizations in theUnited States and is a prominent cause of death globally, especially in low-income nations. Pneumonia diagnosis must be preciseand prompt for optimal treatment and patient outcomes. Deep learning algorithms have developed as a useful tool for analyzing and classifying medical pictures, especially chest X-rays used to detect pneumonia, in recent years. The correct categorization of pneumonia into several categories can assist healthcare providers in making educated decisions about patient care and treatment. We can increase the accuracy and efficiency of pneumonia diagnosis using deep learning approaches, potentially leading to betterpatient outcomes. The study emphasizes the promise of deep learning techniques in medical image processing, as well as their ability to enhance healthcare outcomes. [ABSTRACT FROM AUTHOR]

The Corona virus wreaked havoc on the worldwide health system. The transmission of Corona virus can be reduced and prevented by wearing a face mask. To eradicate the spreading of Corona virus among the individuals we proposed a system that can detect the individual who are not wearing mask and alert the individual and we employed a dataset with and without masks photos from the public domain Kaggle facial mask detection dataset, and a predefined model MobileNetV2 and CNN Algorithm are accomplished for real-time face detection. We use Python, OpenCV, Tensor Flow, and Keras to develop a facemask detector in depth using computer vision techniques. Our objective is to apply image processing techniques and deep learning algorithms to analyse an image or video stream to ascertain whether an individual is wearing a face mask or not. If the person is not, an alarm warning will be generated. In proposed model Swish activation function applied in place of ReLu to generate an optimal accuracy of 99.81 [ABSTRACT FROM AUTHOR]

Tracking animal behaviour, habitat use, population demographics, conflicts between humans and animals, vehicle-animal accidents, events involving snares and poaching, and illnesses all depend on wildlife monitoring. Over time, there have been an increasing number of incidents between vehicles and animals on both mountainous and rural roads and highways. Occasionally, animals that cross the road at the wrong moment result in tragic collisions. The identification and categorization of animals from pictures and videos is a popular area of study. A crucial part of the methods used today for wildlife monitoring is image processing. Since most wild creatures are most active at night, it might be difficult to view them without specialist equipment. The availability of heat sensing technology and technical advancements have made it possible for researchers to observe animals. This study compares a number of methods, including deep neural network-based (Faster RCNN and YOLO) and conventional (HOG/SVM) approaches, for identifying animals in thermal camera images. Through comparison, the best mean Average Precision (mAP) was found for different Intersection over Union (IoU) coverage thresholds and recall (sensitivity) levels. [ABSTRACT FROM AUTHOR]

Due to the overuse of antibiotics, antibiotic resistance in bacteria has emerged as a significant public health concern. Various methods can be employed to determine the susceptibility of bacteria to antibacterial compounds, and artificial intelligence (AI) has proven to be highly impactful in this field, especially with the advancements in image processing and classification. This research investigates two approaches for classifying antibiotic-resistant bacteria: traditional machine learning (ML) using the Orange Data Mining tool and deep learning with Convolution Neural Network (CNN). Using an antibiotic disk image dataset, various classifier models are compared using the Orange Data Mining tool. The traditional classifier model Logistic Regression is found to be the most accurate with a 91% accuracy rate, followed by Neural Networks (NN) with 90% accuracy. Other techniques such as Support Vector Machine (SVM), k-nearest neighbors (KNN), and decision trees (DT) are also analyzed, with varying levels of accuracy. Deep learning with CNN is found to be more efficient in classification, achieving an impressive 99.99% accuracy. This study's high accuracy results suggest that CNN could be a promising tool for the rapid and reliable diagnosis of antibiotic-resistant bacteria in the future. [ABSTRACT FROM AUTHOR]

Quality has become a major concern in the present world. Quality check is a part of every industry and that is also the case with the agriculture. The Quality of the grains used to harvest influences the plants growth and most importantly on the Yield making socio, biological and monetary effects on the agriculture. Yield loss in agriculture is regularly connected with the selection of the poor-quality seeds. It is the quality of seeds that improves the value of production and widens the marketing scope. To start with the agriculture, farmers select the top quality seeds and when the crop is harvested consumers checks quality of grains before consuming and the yield will be profitable if the harvested seed quality is good enough to produce more grains. Grain quality analysis processes the image of grains to get its properties and further classify the grains on their quality. The research is carried on using the Image processing methods, machine learning algorithms which is trained and implemented using python language with the dataset of grain images considering various features. [ABSTRACT FROM AUTHOR]