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A three-operand binary adder is used to perform arithmetic operations in various cryptography and image processing. In this project, four types (CS3A, HC3A, HHC3A, and the PPAPG3A techniques) of three-operand adder techniques and their comparison have been done. In contrast to the previous three ways, the originality of this three-operand adder is the decrease the delay in all stages. The MDCLCG architecture is implemented with this three-operand adder, and the result shows that this architecture is faster than the other three architectures. An application of the 16-bit PPAPG3A three-operand adder is done in cryptography for data encryption and decryption and also in gaussian filtering for image convolution using MAC operations. [ABSTRACT FROM AUTHOR]

Digital image processing is useful in analysing the images obtained through Transmission electron microscopy, TEM in verifying the morphology of polymer composite structures. In the proposed paper, digital image processing method is utilized for analysing the influence of High Abrasive Furnace (HAF) carbon black on Natural Rubber (NR) – Low density polyethylene (LDPE) composites. The filler distribution and dispersion in the rubber and plastic phase will give insight to the material behaviour in the real time applications. The image processing techniques can be utilised to distinguish the various components in the heterogeneous polymer composites. In this work image processing techniques are used to identify the constituent components and its properties. Susan filter groups the homogeneous regions thereby highlighting the LDPE-HAF region. Susan filtering on a contrast enhanced image using Selective gray level grouping(S-GLG) differentiates the highly concentrated and lightly concentrated component regions. The HAF filler is localised in the LDPE matrix, whereas it indicates better dispersion and distribution in NR matrix. The high and low concentration contours obtained through contrast enhancement represents significant information on material characteristics. It is clearly visible from contour images that the size of the LDPE-HAF zone shrinks on addition of more HAF. [ABSTRACT FROM AUTHOR]

In the diagnosis and treatment of a patient, blood cell detection and classification is a critical activity. Skilled lab technicians manually inspect the blood cells in a conventional method, which is time-consuming and error-prone. With the recent advancements in image processing, deep learning and object detection techniques has acquired larger attention for automating the procedure of blood cell detection and classification. This article focuses on how image processing and machine learning can be used to morphologically characterise and recognise cell pictures collected from peripheral blood smears. Image processing techniques for blood cell detection are typically based on thresholding, segmentation and morphological operations. Machine learning algorithms can learn from data and adapt to new conditions allowing for more accurate and robust blood cell detection. Deep learning algorithms can learn to extract relevant features from raw image data, eliminating the need for manual feature engineering. Deep learning methodology is superior to traditional image processing methods in literature. This paper also focuses on typical generic architectures for object detection covering one stage as well as two stage detectors to improve the detection performance further. Multiple approaches to microscopic blood cells examinations are analysed and compared using various performance metrics. [ABSTRACT FROM AUTHOR]

The study investigates the classification and identification of various kinds of rheumatoid arthritis (RA) through applying X-ray pictures and deep learning methods like convolutional neural networks (CNN) and recurrent neural networks (RNN). The key component of this work is CNNs, which are made for effective modification of data properties, attribute reduction, and processed training. The review describes obstacles of evaluating RA X-ray images, including issues with computing efficiency, clinical data integration, and dataset quality. Solutions include larger datasets, complicated models, and improved noise reduction. Given these drawbacks, resolving these problems might enhance the precision of RA diagnosis and care. Significance of the review can be seen by its importance of medical applications and advancements in data collecting. Refined image processing, less manual interactions, enhanced visualization, and automated diagnostic tools should be the main priorities of future research. A non-intrusive diagnostic tool for the identification and categorization of RA is the motive. For better diagnosis, more research may look at the integration of modern imaging (MRI, CT scans). Investigating real-time patient data and multi-modal data fusion may provide a thorough grasp of the course of RA. Explanatory deep learning models for transparent decision-making may be the subject of future research. Ongoing technological and data-driven developments promise improved predictive and diagnostic accuracy for Rheumatoid Arthritis. [ABSTRACT FROM AUTHOR]

Biometric is the most fundamental and efficient technique used in terms of recognizing individuals uniquely. As we have two types of biometric; one on physical and the other in terms behavioral characteristics. Iris segmentation is a crucial stage in iris recognition systems since it determines the accuracy of all future phases and can be segmented using a variety of techniques. There are different iris segmentation techniques, but the most widely used ones are the circular Hough transform and Daugman's algorithm which are commonly employed in commercial and research problems involving iris recognition systems. In iris recognition system, the time it took to detect the iris and the accuracy with which it was located are both important aspects. Due to low in terms of accuracy we have suggested a different approach of iris segmentation based on the black hole search method and canny edge detector in this research. The proposed approach is tested using CASIA and MMU Database iris images. This technique is effective in terms of the time it takes to detect an eye's iris, as it directly utilizes intensity and shape. Also, we can detect the iris of an eye much faster than the standard technique with the help of image processing. [ABSTRACT FROM AUTHOR]

Diabetic foot is a severe medical problem that occurs as a result of high blood sugar levels. It is a common complication in diabetics. Diabetes can lead to complications, especially in the form of diabetic foot problems. If these problems are not detected and treated promptly, they can worsen, leading to severe consequences. Screening methods for the disease can be conventional and do not predict diabetic foot in the early stages. These prompted researchers to find an alternative solution to detect diabetic foot early and non-surgically. Researchers have sought other non-invasive methods to diagnose and predict diabetic feet using image processing techniques and machine learning algorithms. This study presents a comparative performance between six machine learning algorithms (Neural Network, Random Forest, Adaboost, Naïve Bayes) based on a dataset of images of normal and diabetic feet. The results show that Neural Network has an accuracy of 95.6%, the highest performance among other algorithms used in this study. [ABSTRACT FROM AUTHOR]

Efficient digital archiving systems are indispensable for managing vast amounts of data, facilitating streamlined information retrieval, enabling remote data exchange, and ensuring robust data security. While existing techniques often introduce complexity and security concerns, necessitating larger storage spaces, this paper proposes a new and straightforward approach using mathematical coding to construct a secure archiving system. Our methodology prioritizes simplicity while maintaining robust security measures to archive higher education system information, particularly document images. The proposed system integrates three key domains: mathematical coding for security, image processing for high-quality image archiving, and archive system development. Specifically, information is encoded into a unique CODE using XOR coding for enhanced security and combined with student names to generate PDF file titles containing scanned documents. Additional security layers are implemented through password-protected PDF files. Benchmarking against other database types reveals that our approach yields a simple, secure system for HES records to be archived without requiring high storing abilities or security complexities. Our findings underscore the effectiveness of our methodology in achieving efficient digital archiving while maintaining data security. [ABSTRACT FROM AUTHOR]

Blood cell count is crucial to medical diagnosis. Numerous disorders in the human body are caused by changes in the blood cell count. There are several methods for counting blood cells, including automatic and conventional methods. Under a microscope, the traditional hand-counting method takes a lot of time and produces unreliable findings. Even with hardware solutions like the Automated Hematology Counter, developing nations are unable to set up such prohibitively expensive equipment in each hospital laboratory nationwide. In order to address this issue and offer a software-based, affordable, and efficient replacement for blood cell identification and assessment, this research offers a preliminary analysis of digital image processing-based automated blood cell counting. The patient's diagnosis and the identification of abnormalities, such as leukemia, can subsequently be made using the RBC (red blood cell) and WBC (white blood cell) counts of blood cells. A few methods for pre- and post-processing have been applied to the blood cell picture for this reason arranged to provide a considerably fresher and clearer image. Lastly, picture processing includes cell counting algorithms as well as image acquisition, pre-processing, segmentation, and post-processing. [ABSTRACT FROM AUTHOR]

Predictive maintenance is emerging as the highest growing research field as it supports various applications like: image processing, speech processing, machine maintenance. As lot of e-vehicles are coming into the market, the focus is to have efficient life cycle of the machine. To obtain these the predictive maintenance is the only solution. Machine health can be assessed in advance in terms of remaining useful life. These methods are data-based analysis. On the basis of data available prediction is done through various machine learning algorithms. Execution parameters depict the performance of the algorithms. Remaining useful life of the machine represents the duration of the time a machine can work without any fault. It helps to schedule maintenance and increases operational safety This paper represents results of Stratified k fold cross validation technique among various cross validation techniques is depicted in terms of rmse, mean, and standard deviation as performance parameters for different machine learning algorithm to get an explicit framework for remaining useful life prediction. [ABSTRACT FROM AUTHOR]

The proposed system automates fish separation in coastal regions using electro-mechanical components: conveyor belt, camera, Deep Convolutional Neural Networks (CNNs), and separating bins. Mechanical features prevent fish overlap under the camera, while bins collect desired fish types. The effectiveness and accuracy of the proposed model are verified by considering different environmental conditions, such as illumination and background effects. In real-time condition, the proposed fish classifier model classifies five distinct types of fish available on the Mangaluru coast. Currently, no dedicated electro-mechanical system for fish classification in harbor is available. Hence, the developed model is customized for the harbor environment. The fish which comes from mechanical system is captured with a camera, features are extracted using image processing algorithms and classification is performed using Deep Learning algorithm. A Deep Learning Algorithm, MobileNet V2 CNN model is used for the classification of fishes. Both image processing and Deep Learning algorithm are implemented using Raspberry Pi and Computer. The major attributes of the developed system is economical, energy efficient, and reduction of labour costs. The developed system also works in real-time and invariant to background conditions such as illumination, overlapping of fishes etc. with an accuracy of 97%. The developed model works in real-time and able to classify more than 10000 fishes in an hour. [ABSTRACT FROM AUTHOR]

Effective administration of cold storage facilities plays a significant responsibility in maintaining the quality of perishable items and reducing wastage within the supply chain. This study unveils a creative method that employs the combined pros of image processing, the Internet of Things (IoT), and a notification system to enhance the efficiency of cold storage procedures. The IoT framework enhances this system by enabling seamless communication between the cold storage framework and a central monitoring hub. The proposed paper mainly focuses on real-time monitoring of the cold storage environment and if there is any power outage notification will be sent to the person through SMS using GSM. The notification system is integrated to provide timely alerts to relevant stakeholders. In the current implementation of the project, the DHT11 Temperature and Humidity Sensor, the MQ-3 Ethylene gas sensor is integrated into a low-cost and Wi-Fi-enabled Node-MCU Microcontroller for Cold Storage monitoring purposes. The Node-MCU posts data to a cloud-based platform, where they are dealt with and scrutinized. Integration of image processing techniques assists real-time monitoring of cold storage environment. Cameras placed strategically within the storage units capture images of stored goods, enabling the system to analyze factors such as product quantity, placement, and quality. Advanced image analysis algorithms identify anomalies, spoilage, or potential issues, thus securing the upkeep of product integrity throughout the storage duration. This real-time responsiveness minimizes the risk of product spoilage and verifies adherence to regulatory standards. [ABSTRACT FROM AUTHOR]

Image processing techniques are widely used in all domains of application, including digital imaging, precision agriculture, computer vision, remote sensing, medical imaging, and many more. The aforementioned applications utilize various types of images, such as RGB, Infrared, Multispectral, and so forth. The image generated by a single source, sensor, or modality is insufficient for precisely realizing the item in applications such as medical imaging and remote sensing. Image fusion provides a more effective and efficient way to produce highly useful data for human perception when used with individual input source data. Numerous image fusion techniques exist, including Laplacian pyramids, Gradient Pyramids, SF, IHS, PCA, DCT, and DWT. This study examines several spatial domain Image Fusion techniques to assess the efficacy of distinct techniques based on noise content, spectral degradation, and color distortion. Comparing the outcomes of various spatial domain methods, it is found that PCA is a good choice as its PSNR value is the largest of all spatial domain methods. [ABSTRACT FROM AUTHOR]

Within the broad field of remote sensing, object detection utilising SAR images is an important application such as deforestation, marine monitoring, security for the defence and civilian sectors, disaster management, etc. Object Detection in SAR images could be a difficult task, as these images are intrinsically affected with the speckle noise & strong clutter interference due to backscattering. Two environmental challenges for SAR-based object detection are camouflage and image quality. Sensor-based device problems include limited resolution, image processing indicator, small or glistening item indications, low signal-to-noise ratios, and so on. Here proposed the elaborated literature review on the object detection in SAR representational process on associated problems in SAR images. Major three issues in SAR images, Range Cell migration, Speckle noise, complex clutters are elaborated within the discussion. [ABSTRACT FROM AUTHOR]

Experimental studies on the mini channel were carried out on the flow pattern, flow pattern map and slug frequency in two-phase flow in the downstream area of the horizontal mini channel T-junction with a bend radius of r/dh = 0.7. The mini channel used is a rectangular cross section with a width and height of 2.25 mm × 1.25 mm, respectively, with a hydraulic diameter of 1.607 mm. The working fluids are water and air. Superficial air velocity and water superficial velocity used have a range of respectively JG = 0.593 m/s - 2.963 m/s and JL = 0.626 m/s - 3.186 m/s. In this study, the flow pattern data and slug frequency were taken using a highspeed camera and processed using the image processing method with the MATLAB program. Based on the research results, the major flow regimes obtained are bubbly, slug, churn and the sub-regime flow patterns are bubbly to slug, elongated slug and churn to elongated slug. The superficial velocity of JG air and JL water has a significant effect on changes in flow patterns. The flow pattern map is created based on the JG and JL relationships and the flow pattern sub-regimes are included in it. The frequency of slug formation increases with the increase in the value of the superficial velocity of water at a constant superficial velocity of air. [ABSTRACT FROM AUTHOR]

Curvelet Transform typically offers significantly superior performance in image analysis, multi-resolution and multidi-rectional representation as compared to Wavelet Transform. This paper exploites strong relationship between Wavelet Transform and Curvelet Transform. Also we use mother Wavelet to construct Curvelet Transform as an extension of Wavelet Transform which has broad implications, particularly for the field of signal and image processing. [ABSTRACT FROM AUTHOR]

In order to maintain good health and avoid illness, people need access to potable water. People often become sick from water-related causes because they drink polluted water. This is why monitoring the water quality is essential. The Novel Tensor model is built using time-, location-, and water-related variables. A support vector regression approach is used to forecast the novel water quality by the suggested system, which takes water samples from the DWS system. This research employed a dataset consisting of about 1993 samples, with 127 samples analyzed statistically using SPSS. The results suggest that this new tensor has the potential to produce the best results with the most accuracy (p=.676). The goal of this research is to improve water quality monitoring by using a novel approach to existing image processing classifiers. When compared to older, less sophisticated algorithms, modern artificial neural network methods perform far better. [ABSTRACT FROM AUTHOR]

Medical image processing requires image segmentation and image registration as important diagnostic tools. Image segmentation aims to split structures of interest into distinct components. Meanwhile, image registration finds an ideal transformation between given images. Despite their interrelationship, both tasks are often performed separately. As a result, it faces several problems when undertaken independently. Therefore, the joint models between segmentation and registration have advantages over the separate tasks, particularly when dealing with noisy images. This paper reviewed and compared three existing variational joint segmentation and registration (JSR) models based on active contour without edges and linear curvature. The models are tested on 2D mono-modal and multi-modal images with the presence of white Gaussian noise. The Dice coefficient metric and the registration performance measure are used to evaluate the performance of the models. Numerical results showed that the JSR model using the modified sum of the squared difference outperformed the other two JSR models for noisy mono-modal images. However, the JSR model using the modified normalized gradient fields performed better than the JSR model based on the modified mutual information for noisy multi-modal images. [ABSTRACT FROM AUTHOR]

So far, the identification of 16 types of tree damage is still following the guidelines stated in the Forest Health Monitoring method. The types of tree damage can be recognized by human vision, as well as by computers. Computer vision allows computers to identify things that humans can remember. In this case, the study can be realized with computer vision to make work easier. This study aimed to identify 16 types of damage in Forest Health Monitoring using image data or photos with computer vision. The stages of this research are image acquisition (image acquisition), image processing (image preprocessing), and feature extraction (feature extraction). The results showed that computer vision could identify images in JPG/JPEG (Joint Photographic Experts) format, assisted by the Convolutional Neural Network (CNN) algorithm. The percentage value of success using the CNN algorithm reaches 99.06%, with an error detection of 0.94%. However, several classes were incorrectly identified, indicating that the dataset needs improvement. Thus, it can be concluded that identifying 16 types of damage using image data or photos with computer vision has been successfully carried out. [ABSTRACT FROM AUTHOR]

The oil palm plantation industry heavily relies on foreign labour for harvesting, particularly for collecting loose fruits (LF) alongside fresh fruit bunches (FFB). Manual LF collection, involving bending and repetitive movements, not only diminishes productivity but also poses health risks to workers. This study proposes an automated LF collector to mitigate these challenges. The developed system integrates an LF picker with a robot arm, an LF detector using image processing and a camera, a GPS-based human-follower vehicle, a back-to-home navigation system based on weight detection, and an obstacle avoidance system. The automated LF collector aims to operate autonomously, reducing workforce reliance and enhancing productivity in oil palm plantations. The study discusses the motivation, challenges, and objectives of developing such a system, emphasizing its potential economic and societal benefits. Additionally, the implementation of a novel image processing technique, Faster Objects More Objects (FOMO), using a neural network, is detailed for efficient LF detection. The proposed automated LF collector addresses labour shortages, enhances economic productivity, and reduces the risk of worker injuries in the oil palm industry. [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]

The photos taken by all, bring backs remarkable memories for everyone life. By keeping in mind, this research was focused to enhance the quality of an image, aiming to bring it as close to its original high-quality state. Advanced image processing techniques are employed to achieve this goal. This approach is particularly useful when dealing with degraded images that remain usable but require improvement to enhance their utility or presentation. To elevate the Naturalness Image Quality Evaluator (NIQE) score, we employ the Novel Generative Adversarial Network v3 (NGAN3) with Enhanced Super Resolution Generative Adversarial Networks (ESRGAN). Our study involved two distinct groups: Group 1, which utilized the NGAN3 algorithm, and Group 2, which employed the ESRGAN algorithm. The group sample size carefully determined using the Clincalc tool. This tool also facilitated the calculation of error rates, including an beta level of 0.2, alpha level of 0.05 with power of 0.05. In total, we analyzed 40 samples (20 per group). The NGAN3 algorithm yielded an average NIQE score of 4.46, while the ESRGAN algorithm produced an average NIQE score of 6.36. Notably, the ESRGAN algorithm demonstrated statistical significance with a significance value (P) of 0.001, as determined by a sample t-test. This result underscores the superiority of NGAN3 in terms of image quality. Furthermore, Novel GAN3 generates visually compelling and realistic images, surpassing the existing algorithm. Consequently, it holds the potential to enhance employment prospects by increasing the likelihood of securing well-paid jobs. [ABSTRACT FROM AUTHOR]

Food production and distribution in the food industry require efficient organization. However, manual grading of agricultural produce faces challenges of subjectivity, inefficiency, and inconsistency. To address these issues, this paper proposes a food identification algorithm that utilizes image processing for agricultural technology to identify the type and grades of food products. The methodology involved capturing images of food produced using an ESP32-CAM camera module connected to an Arduino MEGA 2560 board. The images were processed through algorithms to identify the food produced and grade them based on size. The color of the image was extracted by calculating the Euclidean distance of RGB values in the image. The type of food product was identified using the perimeter and the Euclidean distance of RGB values. Then, the algorithm extracted information about the area from the image and provided the value of the area, which determined the grade of the product. The results and discussions present the test outcomes of the food identification and grading algorithms. The food identification algorithm successfully recognized tomatoes and mangoes from the captured images, achieving high accuracy. The food grading algorithm effectively categorized the produce based on the food product's area into Grade A (Big Food Product) if the food product's area is more than 10,000 pixels, and Grade B (Small Food Product) if it is less than 10,000 pixels. In conclusion, the proposed image processing-based food identification and grading system demonstrated potential in automating the food grading process. It provided reliable results in identifying and categorizing an agricultural produce based on visual attributes. [ABSTRACT FROM AUTHOR]

The oil palm industry heavily relies on foreign labour for harvesting, including the collection of fresh fruit bunches (FFB) and loose fruits (LF). Manual LF collection, prone to inefficiencies and worker injuries, prompted the development of an automated LF collector. This project introduces a comprehensive system, featuring an LF picker with a robot arm, an LF detector using image processing, a GPS-based human-follower vehicle, a back-to-home navigation system with weight detection, and an obstacle avoidance system. The automated LF collector aims to reduce workforce dependency, enhance LF collection productivity, and prevent worker injuries. The study discusses the motivation, challenges, and objectives of developing the system, emphasizing potential economic and societal benefits. Furthermore, the implementation of advanced image processing techniques, such as the Faster Objects More Objects (FOMO) neural network, is detailed for efficient LF detection. The section on Back-to-Home Navigation with Weight Detection introduces the concept of an autonomous collector robot vehicle, highlighting the importance of sensors and weight detection for navigation and load management. The integration of these technologies promises to revolutionize loose fruit collection in oil palm plantations, reducing labour dependence, increasing productivity, and mitigating economic losses. [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 oil palm plantation industry plays a pivotal role in Malaysia's economy, contributing significantly to global palm oil production and exports. However, the industry faces challenges related to labour-intensive harvesting, particularly the manual collection of loose oil palm fruits (LF). This manual process not only leads to reduced productivity but also poses health risks to workers. To address these issues, the development of an autonomous LF collector is proposed, aiming to reduce labour requirements and enhance collection efficiency. The automated LF collector incorporates a robotic arm for fruit picking, an LF detection system using image processing, a GPS-based human-follower vehicle, a back-to-home navigation system, and obstacle avoidance mechanisms. This innovation allows the LF collector to operate autonomously in oil palm plantations, thus relieving the workforce. The project details materials and components selection for the LF picker, providing insights into the practical implementation of this technology. By reducing the reliance on manual labour and improving LF collection, the proposed autonomous LF collector contributes to increasing overall palm oil production, ensuring the industry's sustainability and economic growth. [ABSTRACT FROM AUTHOR]

The application of image processing and Neural Network for controlling an actuator or robot was developed and evaluated. The main objective of the system is to establish or create a system for the disability to use facial expressions to control an actuator to assist their daily activities which the group of people easily struggling with. By making use and capturing of the open and closed eyes and mouth motion, the data able to "train" Convolution Neural Network (CNN) models and Mouth Aspect Ratio (MAR) value. All the mentioned parameters had been evaluated together with a new proposed technique, which is named as "quantity" method. The result showed that the developed system is able to capture the actual and true expression of an individual accurately. [ABSTRACT FROM AUTHOR]

Drowsiness and Fatigue of motorists and drivers cause more than 30% of road accidents. Every time, they increase the quantities of deaths, losses, and injuries encyclopedically. Discovery of drowsiness is needed so that it cautions the motorist and the drivers which might save their lives. It's being done using the CNN algorithm in image processing. In this discovery fashion, a motorist is continuously monitored through a webcam. In this technique, the driver's face is taken and it is fed to Deep Learning so that the blinking of the eye is predicted. We use an algorithm, if a motorist's eye seems to be closed for a particular quantum of time, the system cautions the motorist with a sound. To perform the segmentation of the images, ways similar as Image recognition and Image Processing. [ABSTRACT FROM AUTHOR]

This study looks into a number of recently developed pre-trained models and assesses how well they perform using the ImageNet Dataset. In the disciplines of image processing, computer vision, and machine learning, image Classification is a well-known issue. In this study, we studied six different models—three transformer-based models and three convolutional neural network-based models. The findings of this research offer insightful information on the perks and drawbacks of using pre-trained models for image classification tasks. The results indicate that while transformer-based models exhibit promising outcomes, convolutional neural network-based models continue to perform better overall and with regard to accuracy. Additionally, the pre-trained models' performance may be greatly enhanced by fine-tuning them on a smaller dataset. This paper provides an assessment of recently developed pre-trained models and their performance on the ImageNet dataset, which adds to the current research in the fields of image processing, computer vision, and machine learning. The knowledge gathered from this work can guide the creation of models for image classification tasks that are more effective and accurate. [ABSTRACT FROM AUTHOR]

QR codes is the type of matrix bar code. It was invented by Denso Wave in japan.QR code,Due to its quick readability and bigger storage capacity than traditional UPC barcodes. This essay explores the fundamentals of QR codes as well as how they used in daily life. Shopping centres are expanding in size and variety,carrying a bigger variety of goods. To reduce the complexity of shopping frameworks,and various shopping guide mailings, and To make Shopping for customers more convenient,the system has developed.he creation of an easy, quick, and convenient shopping system is a concern for both retailers and customers. Additionally, new approach corrects a number of flaws in the old system. This system generates QR codes for each product using a QR code generator. The test's findings are implemented in a Python software that uses a camera to read QR codes and create a bill for purchases. [ABSTRACT FROM AUTHOR]

Schizophrenia (SZ) is a complex mental disorder that affects millions of people worldwide, and early diagnosis is critical for effective treatment. Structural changes in certain areas of the brain have been consistently observed across many studies of the disorder. However, existing imaging techniques have limitations in identifying structural brain changes in the disorder because the changes begin prior to the onset of clinical symptoms. The changes observed in Grey Matter (GM) and White Matter (WM) are particularly concerned with language processing and communication, and later, they can be detected by progressive ventricular enlargement. The proposed work involves utilizing various algorithms to perform image processing on both patients with onset schizophrenia (SZ) and healthy controls (HC), with the aim of segmenting the Region of Interest (ROI) of the brain using Magnetic Resonance Images (MRI). The resulting segmented MRI images are then employed to generate a 3D visualization of the brain. The segmentation of WM using the Level based Fuzzy C-Means algorithm (LFCM) and K-means algorithm is established. The segmented ROI is further used for analysis. The goal for the segmentation of brain MRI images in schizophrenia is to identify structural pattern changes of regions such as WM of HC and SZ patients. The approach mentioned has the potential to provide a more objective and accurate diagnosis of the WM region in SZ and to identify new targets for treatment by providing detailed information about the location, size, and shape of abnormalities or lesions. The application of these algorithms for image processing tasks in the proposed research showcases the results which can accurately distinguish subjects with SZ from HC. The segmentation of the WM showed the highest similarity with the ground truth (GT) labels, with Structural Similarity Metrics (SSIM) and Feature Similarity Metrics (FSIM) values of 0.82507 and 0.792124, respectively, for HC, and SSIM 0.827187 and FSIM of 0.782535 respectively for SZ disorder. [ABSTRACT FROM AUTHOR]

This work involves the development of a computer application known as the Emotion-Based Music Player, which is aimed specifically at music enthusiasts. Because choosing music can be difficult, most people prefer to play the playlist's tracks at random. Some of the songs selected as a result did not accurately reflect the users' current mood. Furthermore, no widely used music player can play songs based on the user's emotions. The proposed model can identify the user's emotions by extracting the user's facial expression. The songs in the suggested model will then be played by the music player based on the type of emotion identified. It aims to give music fans more ways to enjoy music. The proposed model encompasses a wide range of emotions, including Neutral, Sad, Happy, and Angry. The system heavily employs facial recognition and image processing technology using Fisher Face Method. The model's train and test inputs are still photographs in.jpeg format that is available online. To assess the proposed model's accuracy in identifying emotions, eighty users of various ages and genders—twenty for each type of emotion— are tested. According to the testing results, the proposed model has an 85% recognition rate. [ABSTRACT FROM AUTHOR]

Many devices, including digital cameras, washing machines, smart cars, computers, etc., will have a PCB as an integral component. Today's PCB demands the highest density of components and increased tolerance on these components. Surface mount technology (SMT) is making it possible to add many miniaturized components to PCBs. Component size is decreasing, and the component number is increasing; inspecting these components is becoming even more challenging. As many products are becoming smart, there is a huge demand for PCBs. Huge demands for PCBs also demand quicker inspection techniques. This shows the increased demand for online and inspection techniques. Many types of defects are encountered in the manufacturing of PCBs, such as missing components, cracks, etc. Many of the existing inspection techniques are not suitable for online PCB evaluation. In this scenario, the method presented in this article becomes very important. The present method uses image processing and deep learning-based technique for image identification and classification. Also, the method helps in the real-time assessment of PCBs. [ABSTRACT FROM AUTHOR]

Visual Surveillance is one of the necessary research fields in Computer vision and image processing. Various techniques are developed to perform automated video surveillance. One of the critical challenges in surveillance is detecting abandoned objects in crowded places like parks, shopping complexes and airports, etc., to prevent ecological loss frombomb blasting, gun fires, lost baggage, etc. Closed Circuit Television (CCTV) continuously monitor public places and discriminate unattended and abandoned luggage from different objects. An abandoned thing in surveillance is nothing but an unattended object without a person for a long duration. Identifying these objects in real-time can help prevent bomb blasts and terrorist attacks etc. Many types of research have been performed and published in surveillance for abandoned object detection in recent years. Deep learning is one of the significant fields where various techniques and algorithms are developed for detecting unattended objects. Though these methods detect the objects significantly, they lack computational complexity. A classical edge detection method using Canny edge detection to overcome these challenges is developed to identify the abandoned objects in surveillance scenes. [ABSTRACT FROM AUTHOR]

There is a correlation between our posture and our physical and emotional wellness. The detection of various human postures has been approached in a variety of ways. Determine a patient's resting position, for example, using posture analysis in the medical industry. Image processing using OpenCV and mediapipe python library for human posture estimation. Analyzing standing and sitting postures is made easier with an image-processing-based technique. The benefits of fitness activities to a person's health are remarkable, but if they are done improperly, they may be ineffective and even detrimental. Exercise blunders happen when someone doesn't adopt the right posture. This suggested application makes use of pose estimation to identify the user's workout posture and then offers specific, tailored advice on what the user can do to correct their posture. A pose estimation module in python called mediapipe helps to detect all major joints of the human body. Then it calculates the angle between the joints and increases the counter for each repetition. Pose estimate uses a picture or video of the subject to determine the precise locations of the body's major joints. This computer vision technology recognizes human posture in films and displays important regions, such as the elbow or shoulder, in the finished product. Another way to work out at home is through interactive games. The proposed application has an interactive boxing game built using pygame, which helps all age groups to work out and take care of their health. Overall the user can track all their details on a workout on this application, like how many calories are burned and the daily goals, and different workout planners for each individual. [ABSTRACT FROM AUTHOR]

Deep learning's application to machine learning has seen significant growth in recent years. One of the greatest difficulties now confronting AI systems is the integration of visual data with machine learning in the field of medical diagnostics. Brain tumours, the result of unchecked cell proliferation, are a particularly lethal kind of illness. If painful brain tumours aren't treated properly, they may spread and cause other problems. Cancer treatment relies heavily on an accurate diagnosis. In order to determine whether a tumour is benign or malignant, accurate identification is required. More individuals are diagnosed with cancer now than ever before in part because physicians lack the knowledge to effectively treat tumours at an early stage. In this research, we classify brain MRI and PET scans into tumour and nontumor categories using a convolutional neural network (CNN) technique, augmented by Data Augmentation (DA) and Image Processing (IP) with Image Acquisition (IA). Although we only used a tiny dataset in our experiment, the high level of accuracy (96%) it achieved vouched for the efficiency and minimal complexity of our model. Our approach requires much less computational resources to achieve comparable levels of accuracy as compared to previous pre-trained models. [ABSTRACT FROM AUTHOR]

Tumours are currently the most common cancer cause. Many patients are at risk as a result of cancer. To detect tumours like brain tumours, the medical sector needs a quick, automated, effective, and trustworthy technique. A crucial part of treatment is detection. Doctors keep a patient out of danger if accurate tumour identification is possible. In this application, many image processing methods are applied. Doctors treat many tumour patients properly and save their lives using this application. Currently, medical professionals manually inspect the patient's Magnetic imaging scans of the brain to identify the position and magnitude of the brain neoplasm. This process is time-consuming and results in imprecise detection of the neoplasm. To identify brain tumours, we can apply a Deep Learning framework integrating Convolutional Neural Network (CNN), also called Neural Network, with Visual Geometry Group (VGG) 16 Transfer learning. The advantage of the model is to anticipate the existence of a tumour in an image by returning a positive result if present and a negative effect if not. [ABSTRACT FROM AUTHOR]

The development of aberrant cell proliferation in the lungs is a problematic condition that has the potential to result in death. On the list of diseases that most frequently result in mortality, lung cancer takes first place. The early stages of lung cancer are notoriously difficult to diagnose due to the fact that cancer cells with dimensions less than very small are notoriously difficult to spot by imaging. If the cell abnormalities are discovered in the early stages, it will be possible to begin therapy sooner, which will result in an improved chance of the patient surviving the illness. Several different image processing strategies can be utilized in the diagnostic phase of patient care to help spot signs of disease. In this paper, classification of Lung Cancer from chest X-ray images has been done using optimized Convolutional Neural Network (OCNN) and Ant Lion Optimization (ALO) algorithm. In pre-processing step, the contrast of all images are enhanced using Histogram Equalization (HE) method and the noises are removed from all images using Median Filtering. After the pre-processing step, feature extraction is performed using Gray Level Spatial Dependence (GLSD) to extract the statistical features. The feature vector is then trained and classified using OCNN-ALO algorithm. The ALO algorithm is used to optimize the hyper parameters of CNN layers. It classifies the lung images into normal and lung tumor affected. Performance results have indicated that OCNN-ALO attains the superior performance with 95.15% accuracy, 85.43% sensitivity, 93.4% specificity and 76.43% F1-score. [ABSTRACT FROM AUTHOR]

Recognizing objects is an important task in many real-world applications, including security systems, surveillance, and robots, making it a hot topic in computer vision research. Because of the progress made in deep learning, real-time object identification systems have been created. These systems can instantly identify things in photos and movies. A major focus of computer vision research, object detection has found practical applications in areas as diverse as autonomous vehicles, robots, security cameras, and people counting. Although deep neural networks have powerful feature representation capabilities in image processing and are often used as feature extraction modules in object detection, their introduction has changed conventional techniques of object identification and detection. Models trained using deep learning may be used as classifiers or regression tools, and they don't need any further human input. This suggests that object identification is a promising area for deep learning research. In order to recognize items in a picture, object detection first needs to pinpoint their exact position (object localization). [ABSTRACT FROM AUTHOR]

Semantic segmentation, once considered a daunting challenge in computer vision, has seen significant advancements with the rise of deep learning. This progress has transformed automated driving from a distant aspiration into a practical reality. However, many existing semantic segmentation algorithms were not specifically designed for autonomous vehicle applications; they were initially developed for more general image processing tasks. In this article, we introduce a reliable technique tailored for semantic segmentation in autonomous driving scenarios. It is imperative to swiftly develop a precise and up-to-date semantic segmentation system to ensure the safe and efficient operation of autonomous vehicles. At the core of this endeavor lies the concept of autonomous driving or self-driving cars. Without the capability to accurately identify and respond to hazards such as pedestrians, other vehicles, and traffic lanes, autonomous vehicles cannot function effectively. [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]

Among all foods, rice is the one that people from different cultures eat the most of. When rice is of high grade, its price rises sharply. As it is, a physical evaluation method using the unaided eye is used to evaluate the rice variety & grade. Nevertheless, this approach is laborious, time-consuming, depends on human expertise, and poses risks to the investigator's wellbeing. This paper proposes a technique that employs computerized methods of image processing to automatically detect and categorise rice grains, therefore addressing the aforementioned problems. This image processing method is ideal because it does not involve any physical touch while photographing the rice grains. To evaluate rice quality, a CNN is utilised to pre-process, partition, & retrieve characteristics from the capturand SVM algorithmic classifiers. Our comparative analysis shows that the suggested system categorization outperforms the alternative. [ABSTRACT FROM AUTHOR]

To take out precise ROIs and improve images for precise night-time vehicle number plate detection, we combine a brand-new area of interest (ROI) extraction technique based on improved multi-scale retinex with a method for improving night time images that combines object recommendations with vehicle light detection. (MSR). A suggested score-level feature fusion unifies five complementing traits. The approach we provide can find moving objects that are blurry or partially obscured, as well as moving objects of different shapes, sizes, numbers, positions, and backdrops. The use of a licence plate detection (LPD) system is essential in a variety of traffic-related applications. This project aims to create a sophisticated detecting system that excels in challenging situations. It suggests a reliable preprocessing improvement technique for correctly identifying licence plates in challenging vehicle photos. The suggested approach combines a Gaussian filter with a contrast-limited adaptive histogram equalisation methodology and an enhanced cumulative histogram equalisation method, the local binary pattern and the histogram of an orientated gradient. We identify names using the licence plate. Eventually, we see outcomes. Using the voice command, DON'T PARK IN THIS AREA to send an SMS to the number that is saved in the database. This object has a penalty of this exact amount if it is not obeyed. [ABSTRACT FROM AUTHOR]

This project is to build an Animal Intrusion Detection System that will alert the user or the respective authorities of sightings of animals in real time if it is detected entering the village or human establishment with the distance from the border. We will achieve this by training the model with the Mask RCNN algorithm. Mask R-CNN, sometimes known as Mask RCNN, is the most advanced Convolutional Neural Network (CNN) for instance and picture segmentation. Faster R-CNN, a region-based convolutional neural network, served as the foundation for Mask R-CNN. While training the model we will feed the animal photos with labelling. This will enable the detection and recognize the animal. This project was mainly developed while keeping elephants in mind. Since elephants are poached regularly for trading, meat, tusk, and entertainment purposes. Detecting animal intrusion using image processing helps the system send out alert messages to the user and respective authorities. This real-time implementation will help avoid and reduce animal-human accidents and save human properties from damage. This will also help the residents take quick action to find solutions. [ABSTRACT FROM AUTHOR]

Effective communication is crucial in our daily lives, and it occurs through various channels such as vocal, written, and body language. However, individuals with hearing impairments often rely on sign language as the primary means of communication. The inability to understand sign language can lead to isolation and barriers in communication, hindering the social lives of deaf individuals. To address this need, we propose a marker-free, visual Indian Sign Language identification system that employs image processing, computer vision, and neural network techniques. Our proposed system analyzes video footage captured by a webcam to recognize hand gestures and translate them into text, which is subsequently converted into audio. The system uses a range of image processing techniques to identify the shape of the hand from continuous video frames, including background subtraction, thresholding, and contour detection. The Haar Cascade Classifier algorithm is used to interpret the signs and assign meaning to them based on the recognized patterns. Finally, a speech synthesizer is employed to convert the displayed text into speech. The proposed system is intended to improve the social lives of deaf individuals by facilitating communication with hearing individuals. It is designed to be user-friendly, efficient, and affordable, as it does not require any additional hardware or markers to recognize signs. The proposed system could be integrated into various devices such as smartphones, tablets, or laptops, making it accessible to a wide range of users. The implementation of such a system could potentially break down communication barriers between the deaf and hearing communities, providing deaf individuals with more opportunities to interact with others and participate in society. [ABSTRACT FROM AUTHOR]

Diabetic patients persist at risk of acquiring Proliferative Diabetic Retinopathy, a retinal condition (PDR). development of neovascularization, a disease in which aberrant blood vessels form on retina, one of key hallmarks of PDR. If not diagnosed & treated early enough, aforementioned illness preserve lead via blindness. Various image processing algorithms considering detecting neovascularization in fundus images have been proposed in a number of studies. Neovascularization, on other hand, difficult via identify due via its erratic growth pattern & tiny size. As a result of their ability via execute automatic feature extraction on objects among complex properties, deep learning approaches persist becoming more common in neovascularization recognition. A fore mentioned research proposes a transfer learning-based approach considering detecting neovascularization. We show certain suggested technique outperforms each individual in detecting neovascularization. [ABSTRACT FROM AUTHOR]

The use of Al enabled tools with smart data processing technologies has increased broadly in cites for the betterment and safety of the citizens. Data which we are talking here is of the data from video surveillance cameras on the street. It can record various human activities, using this feature we can record various anomalies which takes place like a Vehicle crash or human fall (due to sudden cardiac arrest, going faint, etc). Automatic accident detection system can secure the life of people who are in serious need of aid by providing timely treatment to the needy. This all thing can be done real time using a very famous object detection module known as YOLO. It uses fully convolutional neural network (CNN) along with OpenCV for the event prediction image processing. Under various circumstances and experimental data our system attained an accuracy of 80-86% in detecting vehicle crash and human fall, the average precision and fps was also increased using YOLOv4 as our underlying technology, the computational speed of the system also increased quite a bit when compared to CNN or R-CNN. Also, the immediate alert generation system which can send real-time alerts to the nearby authority becomes the most important aspect of this work. [ABSTRACT FROM AUTHOR]

Rice is a vital staple crop, and the presence of diseases can significantly impact its production. Consequently, there is an increasing demand to create efficient strategies for recognizing and controlling diseases in rice plants. This paper provides a comprehensive review of the recent advancements in disease identification techniques for rice plants, focusing on the integration of image processing, machine learning, and deep learning methodologies. Despite the progress made, there remains a need for further research to identify and classify additional rice plant diseases beyond the major ones discussed in this paper. By continually exploring new techniques and expanding the scope of disease identification, researchers have the potential to enhance the overall effectiveness of rice disease detection systems. This review serves as a valuable resource for researchers and practitioners in the field, offering insights into the current state of the art and suggesting future directions for advancements in the identification and management of rice plant diseases introduced. [ABSTRACT FROM AUTHOR]

To gauge head growth and identify foetal anomalies, it is crucial to examine head shape during the foetal period. The most popular imaging technique for carrying out this assessment is prenatal ultrasonography. On the other hand, manual interpretation of these images is difficult, hence methods for image processing have been put out in the literature to help with this task. The goal of this essay is to review these cutting-edge techniques. This study aims to evaluate and categorize the various image processing methods applied to ultrasound imaging to evaluate the foetal head. For foetal head examination, numerous image processing techniques have been put forth. In conclusion, methods from several categories demonstrated their potential to enhance clinical practice. However, more study is required to improve the existing techniques, particularly for 3D image acquisition and analysis for detection of abnormality. [ABSTRACT FROM AUTHOR]

As the number of vehicles increases day by day, finding an empty parking space to park a vehicle becomes difficult. Car parking can cause wasted time and interfere with surrounding mobility, as a result the parking area cannot be utilized optimally. The existing parking management system using sensors to detect the available parking spaces is less effective and efficient, for example a system with the use of ultrasonic sensors that must be placed in each parking box will require many sensors in large-scale implementation. The proposed intelligent parking system provides a structured solution by using a parking lot camera available in the campus or office area to observe the used parking area and using image processing to detect the available parking space from the camera in real time. From the results of image processing available parking spaces will be recommended to users through a front-end system based on the closest distance that helps drivers park their vehicles. The proposed system improves the overall effectiveness and efficiency of the current parking system and solves the problem that drivers spend a lot of time in finding suitable parking spaces in crowded campus or office parking areas. The architecture of the intelligent parking system includes three stages: the first stage of the system uses sensors to capture images of the parking area and sends them to the database server in real time; the second stage of the proposed method uses object detection algorithms (ie, Region-based Convolutional Neural Networks) to identify whether parking spaces in the building area are available or not and calculate their utility; the third stage a front-end system was developed for drivers to get real-time parking information by using a monitor placed at the entrance gate of a campus or office parking area. [ABSTRACT FROM AUTHOR]

The initial process of developing a Javanese script transliteration system to other scripts using a character recognition approach requires training data in the form of script images of all possible forms. The source of the dataset are script images from a book written in Javanese and then processed using image processing approach. The captured images were then grouped into their respective classes. The study starts with pre-processing the document images that includes the sub-processes of binarization, inverse, filtering, and followed by script segmentation using the projection profile method. Each script image is then processed in the feature extraction steps using the Intensity of Character or IoC algorithm. The feature data of each script image is then grouped using the K-Means clustering algorithm. The data was taken from the scan results of Hamong Tani's book on pages 2 and 59. After pre-processed and segmented images, 597 images of Javanese script were obtained. Using the IoC 3x3 feature, and the number of groups determined by 65 classes, the silhouette index value of the grouping results was found to be 0.5060. After calculating the ground truth value, it was found that the accuracy of the results was 86%. It can be concluded that the steps taken in this research can be used as a model in the process of providing a Javanese script images dataset. [ABSTRACT FROM AUTHOR]